Presentation describes the idea of heuristic scanning - method used for malware detection and recognition by almost every modern antivirus product. I explain how heuristic scanning works, why it is better than conventional solutions like signature scan, how it bypasses antiheuristic techniques used by malware. Finally I present modern and even future solutions such as Nereus - genetic heuristic engine, developed by Panda Security.
Artificial Intelligence in Virus Detection & Recognitionahmadali999
Heuristic scanning is a method for virus detection and recognition that uses metaheuristics like pattern matching, automatic learning, and environment emulation. It examines program behaviors and characteristics to recognize potential threats without specific knowledge of their code or structure. While heuristic scanning can detect new threats, it risks false positives by flagging innocent programs. Continued development focuses on improving accuracy through techniques like automatic learning from non-infected machines.
What’s spyware and malware detection? How to carry out malware detection? How to tell if you are infected by malware? How to survive from malware attacks?
Autonomic Anomaly Detection System in Computer Networksijsrd.com
This paper describes how you can protect your system from Intrusion, which is the method of Intrusion Prevention and Intrusion Detection .The underlying premise of our Intrusion detection system is to describe attack as instance of ontology and its first need is to detect attack. In this paper, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-governing: self-labeling, self-updating and self-adapting. Our structure employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies.
Clustering Categorical Data for Internet Security ApplicationsIJSTA
This document summarizes research on clustering categorical data for internet security applications. It discusses using clustering techniques for malware categorization, phishing website detection, and detecting secure emails. Feature extraction and categorization are generally used to automatically group file samples or websites. The document also reviews several related works applying clustering and other techniques for malware analysis, phishing detection, and analyzing privacy-breaching malware behavior.
Adware is a software that may be installed on the client machine for displaying advertisements for the
user of that machine with or without consideration of user. Adware can cause unrecoverable threat to the security
and privacy of computer users as there is an increase in number of malicious adware’s. The paper presents an
adware detection approach based on the application of data mining on disassembled code. This is an approach for
an accurate adware detection algorithm with adware data set and machine learning techniques. In this paper, we
disassemble binary files, generate instruction sequences and past his data through different data mining as well as
machine learning algorithms for feature extraction and feature reduction for detection of malicious adware.Then
system accurately detect both novel and known adware instances even though the binary difference between
adware and legitimate software is usually small.
Keywords — Data Mining; Adware Detection; Binary Classification; Static Analysis; Disassembly;
Instruction Sequences
This document provides an overview of malware and techniques for bypassing antivirus software. It begins with introductions and a disclaimer, then outlines topics to be covered including types of malware, statistics, how antivirus works through signature-based and heuristic detection, and standards for testing software. Methods for bypassing antivirus are discussed like encoding, crypters, and DLL injection. A demonstration is promised of bypassing antivirus with Metasploit and crypters. The document concludes with mitigation techniques like keeping software updated, verifying files with hashes, and practicing safe downloading habits.
This document analyzes virus algorithms and proposes guidelines for controlling viruses based on the human immune system. It discusses three stages of virus writers from novice to professional. It describes features of various virus algorithms, including their ability to cover traces, use encryption, be polymorphic, use metamorphic code, be terminate and stay resident (TSR), and use non-standard techniques. Finally, it proposes four guidelines for computer security based on analogies to the human immune system: data protection, detection of anomalous behavior, isolation of infected systems, and development of adaptive security systems.
Artificial Intelligence in Virus Detection & Recognitionahmadali999
Heuristic scanning is a method for virus detection and recognition that uses metaheuristics like pattern matching, automatic learning, and environment emulation. It examines program behaviors and characteristics to recognize potential threats without specific knowledge of their code or structure. While heuristic scanning can detect new threats, it risks false positives by flagging innocent programs. Continued development focuses on improving accuracy through techniques like automatic learning from non-infected machines.
What’s spyware and malware detection? How to carry out malware detection? How to tell if you are infected by malware? How to survive from malware attacks?
Autonomic Anomaly Detection System in Computer Networksijsrd.com
This paper describes how you can protect your system from Intrusion, which is the method of Intrusion Prevention and Intrusion Detection .The underlying premise of our Intrusion detection system is to describe attack as instance of ontology and its first need is to detect attack. In this paper, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-governing: self-labeling, self-updating and self-adapting. Our structure employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies.
Clustering Categorical Data for Internet Security ApplicationsIJSTA
This document summarizes research on clustering categorical data for internet security applications. It discusses using clustering techniques for malware categorization, phishing website detection, and detecting secure emails. Feature extraction and categorization are generally used to automatically group file samples or websites. The document also reviews several related works applying clustering and other techniques for malware analysis, phishing detection, and analyzing privacy-breaching malware behavior.
Adware is a software that may be installed on the client machine for displaying advertisements for the
user of that machine with or without consideration of user. Adware can cause unrecoverable threat to the security
and privacy of computer users as there is an increase in number of malicious adware’s. The paper presents an
adware detection approach based on the application of data mining on disassembled code. This is an approach for
an accurate adware detection algorithm with adware data set and machine learning techniques. In this paper, we
disassemble binary files, generate instruction sequences and past his data through different data mining as well as
machine learning algorithms for feature extraction and feature reduction for detection of malicious adware.Then
system accurately detect both novel and known adware instances even though the binary difference between
adware and legitimate software is usually small.
Keywords — Data Mining; Adware Detection; Binary Classification; Static Analysis; Disassembly;
Instruction Sequences
This document provides an overview of malware and techniques for bypassing antivirus software. It begins with introductions and a disclaimer, then outlines topics to be covered including types of malware, statistics, how antivirus works through signature-based and heuristic detection, and standards for testing software. Methods for bypassing antivirus are discussed like encoding, crypters, and DLL injection. A demonstration is promised of bypassing antivirus with Metasploit and crypters. The document concludes with mitigation techniques like keeping software updated, verifying files with hashes, and practicing safe downloading habits.
This document analyzes virus algorithms and proposes guidelines for controlling viruses based on the human immune system. It discusses three stages of virus writers from novice to professional. It describes features of various virus algorithms, including their ability to cover traces, use encryption, be polymorphic, use metamorphic code, be terminate and stay resident (TSR), and use non-standard techniques. Finally, it proposes four guidelines for computer security based on analogies to the human immune system: data protection, detection of anomalous behavior, isolation of infected systems, and development of adaptive security systems.
This document discusses and compares signature-based and behavior-based anti-malware approaches. Signature-based detection identifies malware by matching patterns in software to known malware signatures but is susceptible to evasion and cannot detect new malware. Behavior-based detection monitors program behaviors and flags anomalous behaviors as potentially malicious, but it can produce false positives and be evaded through mimicry attacks. The document also describes specification-based monitoring, a behavior-based technique that mediates program events according to security policies.
The document discusses Advanced Evasion Techniques (AETs) and introduces Evader, a software tool from Stonesoft. It summarizes that AETs are hacking techniques that evade detection, most security devices cannot detect AETs, and Evader allows organizations to test if their defenses would stop an AET-borne attack. Evader is presented as a free and easy-to-use way for security teams and vendors to determine the real-world effectiveness of networks against AETs.
Enhancing Intrusion Detection System with Proximity InformationZhenyun Zhuang
This document proposes PAIDS, a Proximity-Assisted Intrusion Detection System that identifies unknown worm outbreaks by leveraging proximity information of compromised hosts. PAIDS operates independently from existing signature-based and anomaly-based IDS approaches. It observes that compromised hosts tend to cluster geographically and remain active for long periods, allowing proximity to infected machines to indicate higher infection risk. The document motivates PAIDS based on limitations of other IDSes and clustered/long-term nature of worm spread. It then outlines PAIDS design, deployment model, software architecture, and key components for detecting outbreaks using proximity information.
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLSIJNSA Journal
This document summarizes a survey paper on malware detection and analysis tools. It provides an overview of different types of malware like viruses, worms, Trojans, rootkits, spyware and keyloggers. It describes techniques for malware analysis, including static analysis which examines code without execution, and dynamic analysis which analyzes behavior during execution. It also lists some limitations of static analysis and the need for dynamic analysis. Finally, it discusses various tools available for malware detection, analysis, reverse engineering and debugging.
Malware is a worldwide pandemic. It is designed to damage computer systems without
the knowledge of the owner using the system. Software‟s from reputable vendors also contain
malicious code that affects the system or leaks information‟s to remote servers. Malware‟s includes
computer viruses, spyware, dishonest ad-ware, rootkits, Trojans, dialers etc. Malware detectors are
the primary tools in defense against malware. The quality of such a detector is determined by the
techniques it uses. It is therefore imperative that we study malware detection techniques and
understand their strengths and limitations. This survey examines different types of Malware and
malware detection methods.
Traditionally, "Cryptography" is a benediction to information processing and communications, it helps people to store information securely and the private communications over long distances. Cryptovirology is the study of applications of cryptography to build the malicious software. It is an investigation, how modern cryptographic tools and paradigms can be used to strengthen, develop and improve new malicious software attacks. Cryptovirology attacks have been categorized as : give malware enhanced privacy and be more robust against reverse-engineering, secondly give the attacker enhanced anonymity while communicating with deployed malware. This paper presents the idea of ``Cryptovirology'' which introduce a twist on how cryptography can also be used offensively. Being offensive means, it can be used to mount extortion based attacks that cause loss of access to information, loss of confidentiality, and information leakage, tasks which cryptography usually prevents. Also analyze threats and attacks that misuse of cryptography can cause when combined with fraudulent software (viruses, Trojans). Public-key cryptography is very essential for the attacks that based on cryptovirology. This paper also suggest some of the countermeasures, mechanisms to cope with and prevent such attacks. Even if the attackers actions on the host machine are being monitored, it still cannot be proven beyond reasonable doubt that he or she is the attacker; and it is an “originator-concealing attack”. Evidence should be collected from the “author’s own system which was used for the attack”. These attacks have implications on how the use of
cryptographic tools and techniques should be audited and managed in general purpose computing environments, and imply that access to the cryptographic tools should be in well control of the system(such as API routines). The experimental virus would demonstrate how cryptographic packages can be packed into a small space, which may have independent existence. These are many powerful attacks, where the attacker can encrypt the victim’s data for ransom and release it after hostage.
Malware classification using Machine LearningJapneet Singh
Uses examples from book titled "Malware Data Science" to explain how AV companies use Machine learning to identify malware. Also, refers to open-source project "Ember" which provides a data set and python code to train and classify malware.
Malware Detection Using Machine Learning TechniquesArshadRaja786
Malware viruses can be easily detected using machine learning Techniques such as K-Mean Algorithms, KNN algorithm, Boosted J48 Decision Tree and other Data Mining Techniques. Among them J48 proved to be more effective in detecting computer virus and upcoming networks worms...
Antivirus software uses various methods to detect and remove malicious computer programs like viruses, worms, and trojans. Signature-based detection searches files for known malicious patterns, but cannot find new threats. Heuristics use generic signatures and behavior analysis to identify variants of known malware. As viruses evolve over time through mutations and refinements, antivirus software must be frequently updated to recognize new strains.
Malware Dectection Using Machine learningShubham Dubey
Malware detection is an important factor in the security of the computer systems. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. That is why the need for machine learning-based detection arises.
The document discusses automatic malware clustering and detection. It covers the current state of antivirus classification, which relies primarily on signature-based methods. Automatic malware clustering aims to recognize known malware to filter it out and focus on new threats. The clustering process typically involves malware analysis, feature extraction, and clustering algorithms. Inconsistent labeling of malware families by different antivirus vendors poses challenges. The document advocates improving classification by describing the full malware lifecycle.
This document discusses computer viruses and antivirus software. It provides an overview of infection strategies used by viruses, methods to identify viruses, and statistics on the impacts of viruses. It then covers the history of early computer viruses from 1971 to 1986. The document also includes diagrams depicting virus encounter vectors and the impact of vulnerabilities. It describes virus signature definitions and scanning techniques used by antivirus software to detect and remove viruses from systems and networks.
website vulnerability scanner and reporter research paperBhagyashri Chalakh
This document discusses developing a website analysis tool to improve vulnerability scanning and reporting. It proposes using deep neural networks to enhance the accuracy of vulnerability scanners. It first reviews existing vulnerability scanners like Nessus, Acunetix, and OWASP ZAP and their capabilities. It then discusses how vulnerability scanners use techniques like crawling, fuzzing, and machine learning algorithms to detect vulnerabilities. The proposed tool aims to reduce false positives, allow simultaneous scans, and provide clear reports with countermeasure recommendations to better secure websites.
Malware Classification Using Structured Control FlowSilvio Cesare
This document summarizes a system for classifying malware using control flow graph signatures. It discusses:
1) Using entropy analysis to identify and unpack packed malware through application-level emulation.
2) Generating control flow graph signatures using a "structuring" technique and calculating similarities to signatures in a malware database.
3) Evaluating the system on real malware, showing high similarities between variants and low similarities between unrelated programs.
MITRE ATT&CK framework is about the framework that is followed by Threat Hunters, Threat Analysts for Threat Modelling purpose, which can be use for Adversary Emulation and Attack Defense. Cybersecurity Analyst widely use it for framing the attack through its various used Tactics and Techniques.
Novel Malware Clustering System Based on Kernel Data Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A trust system based on multi level virus detectionUltraUploader
This document summarizes a research paper that proposes a new multi-level virus detection system (MDS). The MDS uses three levels of protection: 1) A smart memory monitor that detects virus behavior in real-time, 2) A file checker that analyzes batch files for virus-like code, and 3) An integrity checker that stores file signatures to detect modifications where viruses typically infect. The system was tested and able to detect virus activity through monitoring, file analysis, and integrity checking at different levels simultaneously. The paper concludes the MDS approach provides improved virus detection over single-method systems.
A malware detection method for health sensor data based on machine learningjaigera
The document proposes a malware detection method for health sensor data based on machine learning. It aims to identify malware patterns in health sensor data rather than just detecting small changes. It will use XGBoost, LightGBM, and Random Forest models to analyze health sensor data from terabytes of benign and malware programs. The challenges are selecting features from the health sensor data, modifying the models for training and testing, and evaluating the features and models. When a malware program is detected by one model, its pattern will be broadcast to the other models to prevent malware intrusion more effectively.
This document discusses network security and various types of attacks. It describes malware like viruses, worms, Trojan horses, and rootkits. It also discusses vulnerabilities in different components of the network like SQL injection vulnerabilities, cross-site scripting, and remote file inclusion issues. Finally, it analyzes attacks like denial of service attacks, man-in-the-middle attacks, and spoofing attacks that can compromise availability, integrity, and privacy of the network.
Classification of Malware based on Data Mining Approachijsrd.com
This document discusses a system called the Intelligent Malware Detection System (IMDS) that uses data mining techniques to classify malware. The IMDS uses a PE parser to extract API execution sequences from Windows portable executable files. It then applies an OOA mining algorithm called OOA_Fast_FP-Growth to generate association rules from the API sequences to classify files as malware or benign. Experimental results showed the IMDS outperformed other classification techniques and anti-virus software in detecting malware.
This document discusses and compares signature-based and behavior-based anti-malware approaches. Signature-based detection identifies malware by matching patterns in software to known malware signatures but is susceptible to evasion and cannot detect new malware. Behavior-based detection monitors program behaviors and flags anomalous behaviors as potentially malicious, but it can produce false positives and be evaded through mimicry attacks. The document also describes specification-based monitoring, a behavior-based technique that mediates program events according to security policies.
The document discusses Advanced Evasion Techniques (AETs) and introduces Evader, a software tool from Stonesoft. It summarizes that AETs are hacking techniques that evade detection, most security devices cannot detect AETs, and Evader allows organizations to test if their defenses would stop an AET-borne attack. Evader is presented as a free and easy-to-use way for security teams and vendors to determine the real-world effectiveness of networks against AETs.
Enhancing Intrusion Detection System with Proximity InformationZhenyun Zhuang
This document proposes PAIDS, a Proximity-Assisted Intrusion Detection System that identifies unknown worm outbreaks by leveraging proximity information of compromised hosts. PAIDS operates independently from existing signature-based and anomaly-based IDS approaches. It observes that compromised hosts tend to cluster geographically and remain active for long periods, allowing proximity to infected machines to indicate higher infection risk. The document motivates PAIDS based on limitations of other IDSes and clustered/long-term nature of worm spread. It then outlines PAIDS design, deployment model, software architecture, and key components for detecting outbreaks using proximity information.
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLSIJNSA Journal
This document summarizes a survey paper on malware detection and analysis tools. It provides an overview of different types of malware like viruses, worms, Trojans, rootkits, spyware and keyloggers. It describes techniques for malware analysis, including static analysis which examines code without execution, and dynamic analysis which analyzes behavior during execution. It also lists some limitations of static analysis and the need for dynamic analysis. Finally, it discusses various tools available for malware detection, analysis, reverse engineering and debugging.
Malware is a worldwide pandemic. It is designed to damage computer systems without
the knowledge of the owner using the system. Software‟s from reputable vendors also contain
malicious code that affects the system or leaks information‟s to remote servers. Malware‟s includes
computer viruses, spyware, dishonest ad-ware, rootkits, Trojans, dialers etc. Malware detectors are
the primary tools in defense against malware. The quality of such a detector is determined by the
techniques it uses. It is therefore imperative that we study malware detection techniques and
understand their strengths and limitations. This survey examines different types of Malware and
malware detection methods.
Traditionally, "Cryptography" is a benediction to information processing and communications, it helps people to store information securely and the private communications over long distances. Cryptovirology is the study of applications of cryptography to build the malicious software. It is an investigation, how modern cryptographic tools and paradigms can be used to strengthen, develop and improve new malicious software attacks. Cryptovirology attacks have been categorized as : give malware enhanced privacy and be more robust against reverse-engineering, secondly give the attacker enhanced anonymity while communicating with deployed malware. This paper presents the idea of ``Cryptovirology'' which introduce a twist on how cryptography can also be used offensively. Being offensive means, it can be used to mount extortion based attacks that cause loss of access to information, loss of confidentiality, and information leakage, tasks which cryptography usually prevents. Also analyze threats and attacks that misuse of cryptography can cause when combined with fraudulent software (viruses, Trojans). Public-key cryptography is very essential for the attacks that based on cryptovirology. This paper also suggest some of the countermeasures, mechanisms to cope with and prevent such attacks. Even if the attackers actions on the host machine are being monitored, it still cannot be proven beyond reasonable doubt that he or she is the attacker; and it is an “originator-concealing attack”. Evidence should be collected from the “author’s own system which was used for the attack”. These attacks have implications on how the use of
cryptographic tools and techniques should be audited and managed in general purpose computing environments, and imply that access to the cryptographic tools should be in well control of the system(such as API routines). The experimental virus would demonstrate how cryptographic packages can be packed into a small space, which may have independent existence. These are many powerful attacks, where the attacker can encrypt the victim’s data for ransom and release it after hostage.
Malware classification using Machine LearningJapneet Singh
Uses examples from book titled "Malware Data Science" to explain how AV companies use Machine learning to identify malware. Also, refers to open-source project "Ember" which provides a data set and python code to train and classify malware.
Malware Detection Using Machine Learning TechniquesArshadRaja786
Malware viruses can be easily detected using machine learning Techniques such as K-Mean Algorithms, KNN algorithm, Boosted J48 Decision Tree and other Data Mining Techniques. Among them J48 proved to be more effective in detecting computer virus and upcoming networks worms...
Antivirus software uses various methods to detect and remove malicious computer programs like viruses, worms, and trojans. Signature-based detection searches files for known malicious patterns, but cannot find new threats. Heuristics use generic signatures and behavior analysis to identify variants of known malware. As viruses evolve over time through mutations and refinements, antivirus software must be frequently updated to recognize new strains.
Malware Dectection Using Machine learningShubham Dubey
Malware detection is an important factor in the security of the computer systems. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. That is why the need for machine learning-based detection arises.
The document discusses automatic malware clustering and detection. It covers the current state of antivirus classification, which relies primarily on signature-based methods. Automatic malware clustering aims to recognize known malware to filter it out and focus on new threats. The clustering process typically involves malware analysis, feature extraction, and clustering algorithms. Inconsistent labeling of malware families by different antivirus vendors poses challenges. The document advocates improving classification by describing the full malware lifecycle.
This document discusses computer viruses and antivirus software. It provides an overview of infection strategies used by viruses, methods to identify viruses, and statistics on the impacts of viruses. It then covers the history of early computer viruses from 1971 to 1986. The document also includes diagrams depicting virus encounter vectors and the impact of vulnerabilities. It describes virus signature definitions and scanning techniques used by antivirus software to detect and remove viruses from systems and networks.
website vulnerability scanner and reporter research paperBhagyashri Chalakh
This document discusses developing a website analysis tool to improve vulnerability scanning and reporting. It proposes using deep neural networks to enhance the accuracy of vulnerability scanners. It first reviews existing vulnerability scanners like Nessus, Acunetix, and OWASP ZAP and their capabilities. It then discusses how vulnerability scanners use techniques like crawling, fuzzing, and machine learning algorithms to detect vulnerabilities. The proposed tool aims to reduce false positives, allow simultaneous scans, and provide clear reports with countermeasure recommendations to better secure websites.
Malware Classification Using Structured Control FlowSilvio Cesare
This document summarizes a system for classifying malware using control flow graph signatures. It discusses:
1) Using entropy analysis to identify and unpack packed malware through application-level emulation.
2) Generating control flow graph signatures using a "structuring" technique and calculating similarities to signatures in a malware database.
3) Evaluating the system on real malware, showing high similarities between variants and low similarities between unrelated programs.
MITRE ATT&CK framework is about the framework that is followed by Threat Hunters, Threat Analysts for Threat Modelling purpose, which can be use for Adversary Emulation and Attack Defense. Cybersecurity Analyst widely use it for framing the attack through its various used Tactics and Techniques.
Novel Malware Clustering System Based on Kernel Data Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A trust system based on multi level virus detectionUltraUploader
This document summarizes a research paper that proposes a new multi-level virus detection system (MDS). The MDS uses three levels of protection: 1) A smart memory monitor that detects virus behavior in real-time, 2) A file checker that analyzes batch files for virus-like code, and 3) An integrity checker that stores file signatures to detect modifications where viruses typically infect. The system was tested and able to detect virus activity through monitoring, file analysis, and integrity checking at different levels simultaneously. The paper concludes the MDS approach provides improved virus detection over single-method systems.
A malware detection method for health sensor data based on machine learningjaigera
The document proposes a malware detection method for health sensor data based on machine learning. It aims to identify malware patterns in health sensor data rather than just detecting small changes. It will use XGBoost, LightGBM, and Random Forest models to analyze health sensor data from terabytes of benign and malware programs. The challenges are selecting features from the health sensor data, modifying the models for training and testing, and evaluating the features and models. When a malware program is detected by one model, its pattern will be broadcast to the other models to prevent malware intrusion more effectively.
This document discusses network security and various types of attacks. It describes malware like viruses, worms, Trojan horses, and rootkits. It also discusses vulnerabilities in different components of the network like SQL injection vulnerabilities, cross-site scripting, and remote file inclusion issues. Finally, it analyzes attacks like denial of service attacks, man-in-the-middle attacks, and spoofing attacks that can compromise availability, integrity, and privacy of the network.
Classification of Malware based on Data Mining Approachijsrd.com
This document discusses a system called the Intelligent Malware Detection System (IMDS) that uses data mining techniques to classify malware. The IMDS uses a PE parser to extract API execution sequences from Windows portable executable files. It then applies an OOA mining algorithm called OOA_Fast_FP-Growth to generate association rules from the API sequences to classify files as malware or benign. Experimental results showed the IMDS outperformed other classification techniques and anti-virus software in detecting malware.
AI approach to malware similarity analysis: Maping the malware genome with a...Priyanka Aash
In recent years, cyber defenders protecting enterprise networks have started incorporating malware code sharing identification tools into their workflows. These tools compare new malware samples to a large databases of known malware samples, in order to identify samples with shared code relationships. When unknown malware binaries are found to share code "fingerprints" with malware from known adversaries, they provides a key clue into which adversary is generating these new binaries, thus helping develop a general mitigation strategy against that family of threats. The efficacy of code sharing identification systems is demonstrated every day, as new family of threats are discovered, and countermeasures are rapidly developed for them. Unfortunately, these systems are hard to maintain, deploy, and adapt to evolving threats. First and foremost, these systems do not learn to adapt to new malware obfuscation strategies, meaning they will continuously fall out of date with adversary tradecraft, requiring, periodically, a manually intensive tuning in order to adjust the formulae used for similarity between malware. In addition, these systems require an up to date, well maintained database of recent threats in order to provide relevant results. Such a database is difficult to deploy, and hard and expensive to maintain for smaller organizations. In order to address these issues we developed a new malware similarity detection approach. This approach, not only significantly reduces the need for manual tuning of the similarity formulate, but also allows for significantly smaller deployment footprint and provides significant increase in accuracy. Our family/similarity detection system is the first to use deep neural networks for code sharing identification, automatically learning to see through adversary tradecraft, thereby staying up to date with adversary evolution. Using traditional string similarity features our approach increased accuracy by 10%, from 65% to 75%. Using an advanced set of features that we specifically designed for malware classification, our approach has 98% accuracy. In this presentation we describe how our method works, why it is able to significantly improve upon current approaches, and how this approach can be easily adapted and tuned to individual/organization needs of the attendees.
(Source: Black Hat USA 2016, Las Vegas)
Detection of Malware Downloads via Graph Mining (AsiaCCS '16)Marco Balduzzi
Mastino, a novel defense system to detect malware download events. A download event is a 3-tuple that identifies the action of downloading a file from a URL that was triggered by a client (machine). Mastino utilizes global situation awareness and continuously monitors various network- and system-level events of the clients' machines across the Internet and provides real time classification of both files and URLs to the clients upon submission of a new, unknown file or URL to the system. To enable detection of the download events, Mastino builds a large download graph that captures the subtle relationships among the entities of download events, i.e. files, URLs, and machines. We implemented a prototype version of Mastino and evaluated it in a large-scale real-world deployment. Our experimental evaluation shows that Mastino can accurately classify malware download events with an average of 95.5% true positive (TP), while incurring less than 0.5% false positives (FP). In addition, we show the Mastino can classify a new download event as either benign or malware in just a fraction of a second, and is therefore suitable as a real time defense system.
Understand How Machine Learning Defends Against Zero-Day ThreatsRahul Mohandas
Detection Challenges
Machine Learning Approaches
Modeling Machine Learning classifiers
Attacks on Machine Learning Defenses
Real Protect
Deep Learning in Sandbox
The document discusses techniques used by malware to detect virtual machines and strategies to prevent such detection. It outlines several techniques malware uses to detect virtual machines, including hardware fingerprinting, registry checks, process/file checks, memory checks, timing analysis, and communication channel checks. It then discusses approaches used by popular virtual machines like VMware, VirtualBox, and VirtualPC. The document proposes developing a tool called VMDetectGuard that would monitor for calls and instructions used in detection and mask the virtual machine's identity by providing false information to tricks malware.
Malware detection software using a support vector machine as a classifierNicole Bili?
This document provides an overview of different types of malware, including definitions and characteristics. It discusses adware, spyware, viruses, worms, Trojans, rootkits, backdoors, keyloggers, rogue security software, and ransomware. Machine learning and classification techniques are then introduced, focusing on supervised learning, model selection, evaluating classifiers, support vector machines, principal component analysis, and linear discriminant analysis. The document describes how a dataset of executables was processed and analyzed to create a malware detection software using these machine learning methods.
The document proposes an ensemble-based categorization and adaptive learning model for malware detection. The model consists of 4 phases: (1) feature selection and extraction from malware binaries, (2) categorization of malware into generic classes, (3) ensemble classification using weak learners, and (4) adaptive learning where new signatures are added for previously unknown malware. The goal is to improve malware detection accuracy and tackle new malware through adaptive learning.
2012 B-Sides and ToorCon Talk Offensive Defense
Blog Post - http://blog.ioactive.com/2013/01/offensive-defense.html
Cyber-criminals have had back-end infrastructures equivalent to Virus Total to test if malware and exploits are effective against AV scanners for many years, thus showing that attackers are proactively avoiding detection when building malware. In this day of age malicious binaries are generated on demand by server-side kits when a victim visits a malicious web page, making reliance solely on hash based solutions inadequate. In the last 15 years detection techniques have evolved in an attempt to keep up with attack trends. In the last few years security companies have looked for supplemental solutions such as the use of machine learning to detect and mitigate attacks against cyber criminals. Let's not pretend attackers can't bypass each and every detection technique currently deployed. Join me as I present and review current detection methods found in most host and network security solutions found today. We will re-review the defense in depth strategy while keeping in mind that a solid security strategy consists of forcing an attacker to spend as much time and effort while needing to know a variety of skills and technologies in order to successfully pull off the attack. In the end I hope to convince you that thinking defensively requires thinking offensively.
Malware Detection - A Machine Learning PerspectiveChong-Kuan Chen
This document discusses machine learning approaches for malware detection. It notes that millions of new malware are created each year, making it difficult for signature-based antivirus software to keep up. Machine learning is presented as a potential solution by automatically constructing models to detect malware based on training data. However, the quality of the training data and features is critical, as machine learning risks producing garbage outputs from garbage inputs. Different machine learning algorithms and evaluation benchmarks are also discussed.
This document discusses using machine learning for malware detection. It begins with an introduction to machine learning and feature selection for classification problems. Popular supervised learning algorithms that can be used for malware detection are discussed, including K-Nearest Neighbors (KNN), K-Means clustering, Support Vector Machines (SVM), Decision Trees, and Random Forests. Features that could be used for malware detection, such as file size, entropy, and behavioral patterns, are also outlined. The document concludes with brief mentions of neural networks, deep learning, and Python libraries that can be used like Scikit-Learn.
The document provides an overview of malware types and techniques. It discusses viruses, worms, trojans, rootkits, and other malware. It describes how malware infects systems, propagates, and hides. Historic malware examples like Morris worm, Code Red, and SQL Slammer are summarized. Methods for malware detection like signatures, heuristics, sandboxing, and network monitoring are also covered at a high level.
Telecom Fraud Detection - Naive Bayes ClassificationMaruthi Nataraj K
This document discusses using Naive Bayes classification to detect subscription fraud at a telecom company. It analyzes call detail records from known fraudsters to build a model to predict fraudulent subscribers. The model is trained on 70% of records from 3 known fraudsters and tested on 30% of records. It then uses the model to analyze an audit log of 15 subscribers and predicts which subscribers are most likely to be the known fraudsters Sally, Virginia or Vince, along with the probabilities. The document outlines the datasets, tools used including R and various R packages, data preprocessing steps, model building process using 10-fold cross validation, performance evaluation on test data and using the model to analyze the audit log dataset.
Basic survey on malware analysis, tools and techniquesijcsa
The term malware stands for malicious software. It is a program installed on a system without the
knowledge of owner of the system. It is basically installed by the third party with the intention to steal some
private data from the system or simply just to play pranks. This in turn threatens the computer’s security,
wherein computer are used by one’s in day-to-day life as to deal with various necessities like education,
communication, hospitals, banking, entertainment etc. Different traditional techniques are used to detect
and defend these malwares like Antivirus Scanner (AVS), firewalls, etc. But today malware writers are one
step forward towards then Malware detectors. Day-by-day they write new malwares, which become a great
challenge for malware detectors. This paper focuses on basis study of malwares and various detection
techniques which can be used to detect malwares.
This document provides an overview of malware analysis. It defines malware and lists common types. It describes two main techniques for analyzing malware: static and dynamic analysis. Static analysis involves examining malware code and structure without executing it, while dynamic analysis observes malware behavior by executing it in a controlled environment. The goal of malware analysis is to understand malware functionality, behavior, and impact in order to better defend against threats. Host- and network-based intrusion detection systems use malware signatures to detect unauthorized or malicious activities on individual systems and network traffic.
Malware Detection Approaches using Data Mining Techniques.pptxAlamgir Hossain
The document discusses malware detection approaches using data mining techniques. It describes signature-based and behavior-based approaches. Signature-based detection identifies malware by matching signatures in a predefined database, but struggles with polymorphic malware. Behavior-based detection analyzes malware behaviors through dynamic analysis, allowing detection of novel malware but having higher computational costs. Both approaches have advantages and limitations for malware detection.
The document proposes new methods for automatically generating malware invariants from binary code to detect and identify malware. Current signature-based malware detectors can be evaded through obfuscation, but malware invariants capture semantic properties that are more difficult to obfuscate. The method involves using formal methods and static analysis to extract invariants from binary code and represent them as semantic signatures, called malware invariants, that can be matched against suspicious code to detect malware families. Combining multiple static and dynamic analysis tools can help generate strong malware invariants that circumvent common obfuscation techniques used by malware writers.
This document discusses detecting and mitigating cyber threats and attacks. It defines threats as malicious acts seeking to damage, steal, or disrupt data. Common threats include ransomware, phishing, data leakage, and insider threats. Attacks are defined as actions that can disable computers, steal data, or use breached devices to launch other attacks, such as password attacks and malware attacks. The document outlines threat detection technologies like intrusion detection systems, network firewalls, and honeypots. It concludes with recommendations for mitigating threats, such as keeping software updated, using anti-virus protection, backing up data, and implementing multi-factor authentication.
This document discusses different machine learning techniques for malware detection. It introduces malware and its types, then describes using neural networks and naive bayes for detection. The methodology section explains signature-based, behavioral-based, and heuristic-based detection approaches. Advantages of machine learning for malware detection are robustness and ability to detect unknown malware. Applications include data mining and malware recognition. The conclusion states that neural networks provide more accurate malware detection compared to other approaches.
911 Computers Support and Repair is a Fort Worth-based emergency response team that offers an array of computer repair services to the distressed computer user. With over 10 years of commitment and swift service, we are continuing to provide exemplary technology support to all our clients.
911 Computers Support and Repair is a Fort Worth-based emergency response team that offers an array of computer repair services to the distressed computer user. With over 10 years of commitment and swift service, we are continuing to provide exemplary technology support to all our clients.
This document discusses ethical hacking. It begins with an introduction and outlines the types of hackers and attacks. It then describes the methodology of hacking, including reconnaissance, scanning, gaining access, and covering tracks. The document outlines advantages such as providing security for organizations and evolving techniques, and disadvantages like cost and reliance on trust. It concludes by emphasizing the importance of security and preventing vulnerabilities.
This document discusses ethical hacking and provides an overview of its key aspects in 6 paragraphs. It begins by distinguishing between hacking and ethical hacking, noting that ethical hacking involves evaluating a system's security with the owner's permission. It then describes different types of hackers and various types of attacks, such as worms, denial of service attacks, and viruses. The document outlines the methodology of hacking through stages like reconnaissance and scanning. It discusses advantages like providing security for organizations, and disadvantages such as costs and trust issues. It concludes by emphasizing the importance of security in software and businesses.
This document discusses using data mining techniques to detect spyware. It begins by defining spyware and artificial intelligence. It then discusses three AI approaches that have been applied to spyware detection: heuristic technology, neural network technology, and data mining techniques. It focuses on using breadth-first search (BFS) within a data mining approach. The document finds that data mining techniques achieve an overall accuracy of 90.5% in detecting spyware, performing better than traditional signature-based or heuristic-based methods.
Utilization Data Mining to Detect Spyware IOSR Journals
This document discusses using data mining techniques to detect spyware. It begins by defining spyware and artificial intelligence. It then discusses three AI approaches that have been applied to spyware detection: heuristic technology, neural network technology, and data mining techniques. It focuses on using breadth-first search (BFS) within a data mining approach. The document finds that data mining techniques perform better than traditional signature-based or heuristic-based detection methods, achieving an overall accuracy of 90.5% at detecting spyware using BFS algorithms.
Unveiling the Shadows: A Comprehensive Guide to Malware Analysis for Ensuring...cyberprosocial
Malicious software, or malware, is a constant concern in the networked world of digital landscapes. Cybercriminals are always improving their strategies, which makes malware more complex and difficult to identify. To combat this, protecting computer systems requires an understanding of and application of malware analysis.
This document discusses and compares signature-based and behavior-based anti-malware approaches. Signature-based detection identifies malware by matching patterns in software to known malware signatures but is susceptible to evasion and cannot detect new malware. Behavior-based detection monitors program behaviors and flags anomalous behaviors as potentially malicious, but it can produce false positives and be evaded through mimicry attacks. The document also describes specification-based monitoring, a behavior-based technique that mediates program events according to security policies.
This chapter provides an overview of malware analysis. It outlines the goals of malware analysis as determining what happened during a network intrusion and ensuring all infected machines and files are located. It describes static and dynamic analysis techniques, from basic to advanced. It also defines common types of malware like backdoors, botnets, downloaders, and more. Finally, it provides general rules for malware analysis, like focusing on key features and using different tools/approaches when stuck.
Training on July 16, 2017.
This training is the compressed version of Malware Engineering & Crafting.
In this training, we will talk about malware as well as crafting the simple working malware. The goal of this session is to understanding malware internal so one can have tactics to combat it.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis
approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially
overcome these deceits by observing the actual behaviour of the code execution. In this regard, various
methods, techniques and tools have been proposed. However, because of the diverse concepts and
strategies used in the implementation of these methods and tools, security researchers and malware
analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to
contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call
monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic
malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s
implementation strategy, analysis approach, system-wide analysis support and its overall handling of
binaries, helping them to select a suitable and effective one for their study and analysis.
Malware is harmful software that acts against the requirements of computer users. There are many types including viruses, worms, Trojans, spyware, and ransomware. Malware analysis is the process of understanding how malware functions and its potential impact. It involves scanning malware with automated tools, analyzing static properties like file hashes, observing interactive behavior in an isolated lab, and manually reversing code to understand capabilities and logic.
Hancitor malware recognition using swarm intelligent techniqueCSITiaesprime
Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. swarm intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the gray wolf optimization (GWO) algorithm and artificial bee colony (ABC) algorithm, which can effectively recognize Hancitor in networks.
Antivirus software detects viruses using several techniques:
1. Signature scanning compares files to known virus signatures in a database.
2. Heuristic scanning examines code for virus-like behavior even without a signature.
3. Integrity checking compares a file's hash to its original uninfected hash.
4. Behavior monitoring flags suspicious activities like reformatting disks.
5. Resident scanning actively scans files on access to prevent infection spread.
Similar to Artificial Intelligence Methods in Virus Detection & Recognition - Introduction to heuristic scanning (20)
Prezentacja przedstawia wprowadzenie do strumieniowych baz danych, wyjaśnia pojęcia związane z tą technologią oraz opisuje jeden z najbardziej popularnych i efektywnych narzędzi strumieniowego przetwarzania danych - SZSBD StreamBase.
[PL] Krótkozasięgowe systemy telemetryczne i identyfikacyjneWojciech Podgórski
Prezentacja przedstawia przegląd popularnych systemów telemetrycznych i identyfikacyjnych stosowanych w gospodarce i przemyśle. Główny nacisk położono jest na opis technologi RFID wraz z jej zaletami, wadami oraz potencjalnymi zagrożeniami z nią związanymi. Ponadto w prezentacji opisane są alternatywne technologie takie jak NFC oraz ich wykorzystanie w życiu codziennym.
[PL] Mechanizmy bezpieczeństwa w sieciach z rodziny 802.11xWojciech Podgórski
Prezentacja przedstawia najważniejsze mechanizmy zabezpiecznia sieci bezprzewodowych stosowanych zarówno w domowych jak i korporacyjnych rozwiązaniach. Wyjaśniane są algorytmy stosowane w zabezpieczeniach i standardy z nimi związane. Prezentacja jest podsumowana praktyczną listą czynności, które należy wykonać w celu efektywnego zabezpieczenia sieci WiFi.
Rola projektowania architektonicznego w inżynierii oprogramowania zorientowan...Wojciech Podgórski
Niniejsza praca ma na celu zaprezentowanie idei inżynierii
oprogramowania systemów zorientowanych na usługi (SOA) oraz
architektonicznego podejścia do ich projektowania i implementacji. Jako
przykład systemu SOA przedstawiono aspekt serwisu webowego służący
planowaniu imprez. Następnie opisano zastosowanie meta-architektury
PCBMER-A jako fundamentu budowy systemów zorientowanych na usługi
oraz jej cechy, istotne z punktu widzenia inżynierii oprogramowania SOA. W
podsumowaniu przedstawia się propozycje modyfikacji i rozszerzeń
powyższego rozwiązania oraz potencjalne regiony zastosowań
Niniejszt artykuł przedstawia kompleksowe podejście do
interpretacji i zastosowania metryk obiektowych, do mierzenia jakości oraz
parametrów oprogramowania. Autor opisuje pojęcie metryki obiektowej,
atrybutów które mierzy oraz korzyści jakie można uzyskać stosując ją w
praktyce. Następnie wymienia ograniczenia związane z wykorzystaniem metryk
oraz próby ich pokonania dzięki wizualizacji za pomocą perspektyw
polimetrycznych i planów klas. Na koniec prezentuje przegląd popularnych
narzędzi pokrycia kodu metrykami do języka Java.
XPrince: Równoważenie zwinności i dyscypliny. Prezentacja przedstawia metodykę XPrince w kontekście innych metodyk zarówno ciężki jak i lekkich takich jak PRINCE2, RUP oraz XP. Autor opisuje budowe zespołu, cykl życia projektu oraz inżynierię wymgań zastosowaną w XPrince.
eXtensible Markup Language APIs in Java 1.6 - Simple and efficient XML parsin...Wojciech Podgórski
Presentation describes modern ways of parsing XML documents using Java language. It shows different approaches to the same problem, their capabilities, advantages, disadvantages and their comparison. Moreover, we can learn what to expect from Java 7 in context of XML.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Northern Engraving | Nameplate Manufacturing Process - 2024
Artificial Intelligence Methods in Virus Detection & Recognition - Introduction to heuristic scanning
1. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Artificial Intelligence Methods in
Virus Detection & Recognition
Introduction to heuristic scanning
Wojciech Podg´rski
o
http://podgorski.wordpress.com
October 16, 2008
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
2. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Presentation outline
1 Introduction
Fundamentals of malware
Metaheuristics in Virus Detection & Recognition
2 Heuristic scanning theory
Lacks in specific detection
Heuristic scanning conception
Recognizing potential threat
Coping with anti-heuristic mechanisms
Towards accuracy improvement
3 Case Study: Modern heuristic scanner features
Panda’s Technology Evolution
Genetic Heuristic Engine - Nereus
4 Summary
5 Further reading...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
3. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Malware
Malware (malicious software) is software designed to infiltrate or
damage a computer system without the owner’s informed consent.
Source: http://en.wikipedia.org/wiki/Malware
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
4. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Malware types
We can distinguish quite few malicious software types. It is important to
be aware that nevertheless all of them have similar purpose, each one
behave differently.
Viruses
Worms
Wabbits
Trojan horses
Exploits/Backdoors
Spyware/Scumware/Stealware/Parasiteware/Adware
Rootkits
Keyloggers/Dialers
Hoaxes
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
5. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Malware = Viruses
Due to different behaviour, each malware group uses alternative
ways of being undetected. This forces anti-virus software
producers to develop numerous solutions and countermeasures for
computer protection.
This presentation focuses on methods used especially for virus
detection, not necessarily effective against other types of
malicious software.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
6. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Infection strategies
To better understand how viruses are detected and recognized, it is
essential to divide them by their infection ways.
Nonresident viruses The simplest form of viruses which
don’t stay in memory, but infect founded executable file and
search for another to replicate.
Resident viruses More complex and efficient type of viruses
which stay in memory and hide their presence from other
processes. Kind of TSR apps.
Fast infectors Type which is designed to infect as many files as
possible.
Slow infectors Using stealth and encryption techniques to stay
undetected outlast.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
7. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Infection strategies
To better understand how viruses are detected and recognized, it is
essential to divide them by their infection ways.
Nonresident viruses The simplest form of viruses which
don’t stay in memory, but infect founded executable file and
search for another to replicate.
Resident viruses More complex and efficient type of viruses
which stay in memory and hide their presence from other
processes. Kind of TSR apps.
Fast infectors Type which is designed to infect as many files as
possible.
Slow infectors Using stealth and encryption techniques to stay
undetected outlast.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
8. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Infection strategies
To better understand how viruses are detected and recognized, it is
essential to divide them by their infection ways.
Nonresident viruses The simplest form of viruses which
don’t stay in memory, but infect founded executable file and
search for another to replicate.
Resident viruses More complex and efficient type of viruses
which stay in memory and hide their presence from other
processes. Kind of TSR apps.
Fast infectors Type which is designed to infect as many files as
possible.
Slow infectors Using stealth and encryption techniques to stay
undetected outlast.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
9. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Infection strategies
To better understand how viruses are detected and recognized, it is
essential to divide them by their infection ways.
Nonresident viruses The simplest form of viruses which
don’t stay in memory, but infect founded executable file and
search for another to replicate.
Resident viruses More complex and efficient type of viruses
which stay in memory and hide their presence from other
processes. Kind of TSR apps.
Fast infectors Type which is designed to infect as many files as
possible.
Slow infectors Using stealth and encryption techniques to stay
undetected outlast.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
10. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Metaheuristic
Metaheuristic is a heuristic method for solving a very general class of
computational problems by combining user-given black-box procedures
in a hopefully efficient way. Metaheuristics are generally applied to
problems for which there is no satisfactory problem-specific algorithm
or heuristic.
Source: http://en.wikipedia.org/wiki/Metaheuristic
Heuristic
Heuristic is a method to help solve a problem, commonly an informal
method. It is particularly used to rapidly come to a solution that is
reasonably close to the best possible answer, or ’optimal solution’...
Source: http://en.wikipedia.org/wiki/Heuristic
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
11. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
12. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
13. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
14. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
15. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
16. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
17. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
18. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
General metaheuristics
It is important to remember that metaheuristics are only ’ideas’ to
solve a problem not a specific way to do that. List below shows
main metaheuristics used for virus detection and recognition:
Pattern matching
Automatic learning
Environment emulation
Neural networks
Data mining
Bayes networks
Hidden Markov models
and other...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
19. Introduction
Heuristic scanning theory
Fundamentals of malware
Case Study: Modern heuristic scanner features
Metaheuristics in Virus Detection & Recognition
Summary
Further reading...
Concrete heuristics
Specific heuristics practically used in virus detection and
recognition, are naturally inherited from metaheuristics.
And so, for example concrete method for virus detection using
neural networks can be implementation of SOM (Self Organizing
Map).
Neural Networks (metaheuristic) → SOM (heuristic)
The most popular, and one of most efficient heuristic used by
anti-virus software is technique called Heuristic Scanning.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
20. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Lacks in specific detection I
Great deal of modern viruses are only slightly changed versions of
few conceptions developed years ago. Specific detection methods
like signature scanning became very efficient ways of detecting
known threats. Finding specific signature in code allows scanner to
recognize every virus which signature has been stored in built-in
database.
BB ?2 B9 10 01 81 37 ?2 81 77 02 ?2 83 C3 04 E2 F2
FireFly virus signature(hexadecimal)
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
21. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Lacks in specific detection II
Problem occurs when virus source is changed by a programmer or
mutation engine. Signature is being malformed due to even minor
changes. Virus may behave in an exactly same way but is
undetectable due to new, unique signature.
BB ?2 B9 10 01 81 37 ?2 81 A1 D3 ?2 01 C3 04 E2 F2
Malformed signature(hexadecimal)
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
22. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception I
Q: How to recognize a virus without any knowledge about its
internal structure?
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
23. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception I
Q: How to recognize a virus without any knowledge about its
internal structure?
A: By examining its behaviour and characteristics.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
24. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception II
Heuristic scanning in its basic form is implementation of three
metaheuristics:
1 Pattern matching
2 Automatic learning
3 Environment emulation
Of course modern solutions provide more functionalities but
principle stays the same.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
25. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception II
Heuristic scanning in its basic form is implementation of three
metaheuristics:
1 Pattern matching
2 Automatic learning
3 Environment emulation
Of course modern solutions provide more functionalities but
principle stays the same.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
26. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception II
Heuristic scanning in its basic form is implementation of three
metaheuristics:
1 Pattern matching
2 Automatic learning
3 Environment emulation
Of course modern solutions provide more functionalities but
principle stays the same.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
27. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception II
Heuristic scanning in its basic form is implementation of three
metaheuristics:
1 Pattern matching
2 Automatic learning
3 Environment emulation
Of course modern solutions provide more functionalities but
principle stays the same.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
28. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic scanning conception III
The basic idea of heuristic scanning is to examine assembly
language instruction sequences(step-by-step) and qualify them by
their potential harmfulness. If there are sequences behaving
suspiciously, program can be qualified as a virus. The phenomenon
of this method is that it actually detects threats that aren’t yet
known!
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
29. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Recognizing potential threat I
In real anti-virus software, heuristic scanning is implemented to recognize
threats by following built-in rules, e.g. if program tries to format hard
drive its behaviour is highly suspicious but it can be only simple disk
utility. Singular suspicion is never a reason to trigger the alarm. But
if the same program also tries to stay resident and contains routine to
search for executables, it is highly probable that it’s a real virus. AV
software very often classifies sequences by their behaviour granting them
a flag. Every flag has its weight, if total values for one program
exceeds a predefined threshold, scanner regards it as virus.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
30. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Heuristic Scanning as artificial neuron
Figure: Single-layer classifier with threshold From [1]
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
31. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Recognizing potential threat II
Figure: TbScan 6.02 heuristic flags From [3]
For instance Jerusalem/PLO virus would raise FRLMUZ flags.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
32. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Malware evolves
After presenting specific scanning to AV software, malware authors were
obligated to introduce new techniques of being undetected. Beside of
polymorphism and mutation engines viruses started to use various
stealth techniques which basically hooked interrupts and took control
over them. This allowed them to be invisible for traditional scanner.
Moreover, most of them started using real-time encryption which made
them look like totally harmless program.
Figure: Virus evolution chain [Source http://searchsecurity.techtarget.com]
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
33. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Pattern matching is not enough
Mixing stealth techniques with encryption and anti-heuristic sequences
(code obfuscated by meaningless instructions) allowed viruses to be
unseen even by signature and heuristic scanning combined together.
It was obvious that new solution was needed. The idea came from VM
conceptions. Why not to create artificial runtime environment to let
the virus do its job?
Such approach found implementation in environment emulation
engines, which became standard AV software weapon.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
34. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Virtual reality
The idea of environment emulation is simple. Anti-virus program
provides a virtual machine with independent operating system
and allows virus to perform its routines. Behaviour and
characteristics are being continuously examined, while virus is
not aware that is working on a fake system.
This leads to decryption routines and revealment of its true
nature. Also stealth techniques are useless because whole VM is
monitored by AV software.
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
35. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
False positives & automatic learning
HS as a heuristic method is only reasonably close to the best
possible answer. In this case we can imagine that heuristic
scanning will blame innocent programs for being potential threats.
Such behaviour is called false positive
We must be aware that program is right when rising alarm,
because scanned app posses suspicious sequences, we can’t blame
scanner for failure. So what can be done to avoid false positives?
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
36. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
False positives & automatic learning
HS as a heuristic method is only reasonably close to the best
possible answer. In this case we can imagine that heuristic
scanning will blame innocent programs for being potential threats.
Such behaviour is called false positive
We must be aware that program is right when rising alarm,
because scanned app posses suspicious sequences, we can’t blame
scanner for failure. So what can be done to avoid false positives?
A: Automatic learning!
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
37. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy I
Some applications, especially non-commercial ones, can raise false
positive, because of their suspicious routines. e.g. UnHash v1.0
through its encryption functionalities (used for finding hash
collisions) almost every time is qualified as virus. What we can do
to prevent it, is to:
1 Let Monitor learn
Teach AV monitor to recognize programs causing false
positives. (requires advanced user)
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
38. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy I
Some applications, especially non-commercial ones, can raise false
positive, because of their suspicious routines. e.g. UnHash v1.0
through its encryption functionalities (used for finding hash
collisions) almost every time is qualified as virus. What we can do
to prevent it, is to:
1 Let Monitor learn
Teach AV monitor to recognize programs causing false
positives. (requires advanced user)
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
39. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy II
2 Set proper scanning depth
Configure Monitor with suitable heuristic scanning depth by
manipulating threshold computed from flag weights.
3 Assume that machine is not infected
Some AV software can scan through computer knowing it’s
clean and learn which programs are false positives.
4 Combine scanning techniques
Combine multiple scanning techniques to exclude potential
false positives.
5 Perform scan as often as possible
Knowing what’s going on is essential...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
40. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy II
2 Set proper scanning depth
Configure Monitor with suitable heuristic scanning depth by
manipulating threshold computed from flag weights.
3 Assume that machine is not infected
Some AV software can scan through computer knowing it’s
clean and learn which programs are false positives.
4 Combine scanning techniques
Combine multiple scanning techniques to exclude potential
false positives.
5 Perform scan as often as possible
Knowing what’s going on is essential...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
41. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy II
2 Set proper scanning depth
Configure Monitor with suitable heuristic scanning depth by
manipulating threshold computed from flag weights.
3 Assume that machine is not infected
Some AV software can scan through computer knowing it’s
clean and learn which programs are false positives.
4 Combine scanning techniques
Combine multiple scanning techniques to exclude potential
false positives.
5 Perform scan as often as possible
Knowing what’s going on is essential...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
42. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy II
2 Set proper scanning depth
Configure Monitor with suitable heuristic scanning depth by
manipulating threshold computed from flag weights.
3 Assume that machine is not infected
Some AV software can scan through computer knowing it’s
clean and learn which programs are false positives.
4 Combine scanning techniques
Combine multiple scanning techniques to exclude potential
false positives.
5 Perform scan as often as possible
Knowing what’s going on is essential...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
43. Introduction Lacks in specific detection
Heuristic scanning theory Heuristic scanning conception
Case Study: Modern heuristic scanner features Recognizing potential threat
Summary Coping with anti-heuristic mechanisms
Further reading... Towards accuracy improvement
Avoiding false positives & improving accuracy II
2 Set proper scanning depth
Configure Monitor with suitable heuristic scanning depth by
manipulating threshold computed from flag weights.
3 Assume that machine is not infected
Some AV software can scan through computer knowing it’s
clean and learn which programs are false positives.
4 Combine scanning techniques
Combine multiple scanning techniques to exclude potential
false positives.
5 Perform scan as often as possible
Knowing what’s going on is essential...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
44. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Presenting Panda Security solutions
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
45. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Modern Panda Security solutions belong to the third generation
AV software.
First Generation: Antivirus
From 1990’s, signature scanning including polymorphic virus
recognition. Primitive heuristics.
Second Generation: Antimalware
From 2000, integrated firewall, anti-malware engine.
Third Generation: Proactive Technologies
From 2004, TruPrevent R , genetic and rootkit heuristics,
behavioral analysis and blocking, uncloaking techniques,
generic unpacking
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
46. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Modern Panda Security solutions belong to the third generation
AV software.
First Generation: Antivirus
From 1990’s, signature scanning including polymorphic virus
recognition. Primitive heuristics.
Second Generation: Antimalware
From 2000, integrated firewall, anti-malware engine.
Third Generation: Proactive Technologies
From 2004, TruPrevent R , genetic and rootkit heuristics,
behavioral analysis and blocking, uncloaking techniques,
generic unpacking
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
47. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Modern Panda Security solutions belong to the third generation
AV software.
First Generation: Antivirus
From 1990’s, signature scanning including polymorphic virus
recognition. Primitive heuristics.
Second Generation: Antimalware
From 2000, integrated firewall, anti-malware engine.
Third Generation: Proactive Technologies
From 2004, TruPrevent R , genetic and rootkit heuristics,
behavioral analysis and blocking, uncloaking techniques,
generic unpacking
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
48. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Modern Panda Security solutions belong to the third generation
AV software.
First Generation: Antivirus
From 1990’s, signature scanning including polymorphic virus
recognition. Primitive heuristics.
Second Generation: Antimalware
From 2000, integrated firewall, anti-malware engine.
Third Generation: Proactive Technologies
From 2004, TruPrevent R , genetic and rootkit heuristics,
behavioral analysis and blocking, uncloaking techniques,
generic unpacking
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
49. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Modern Panda Security solutions belong to the third generation
AV software.
First Generation: Antivirus
From 1990’s, signature scanning including polymorphic virus
recognition. Primitive heuristics.
Second Generation: Antimalware
From 2000, integrated firewall, anti-malware engine.
Third Generation: Proactive Technologies
From 2004, TruPrevent R , genetic and rootkit heuristics,
behavioral analysis and blocking, uncloaking techniques,
generic unpacking
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
50. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Genetic Heuristic Engine, codename: Nereus was initially released in
2005. The innovation connected with Nereus rely on idea inspired by the
field of genetics and its usefulness to understand how organisms are
individually identified and associated to other organisms. Features:
More than few hundred characteristics of each file that is scanned
Complex malware recognition (type determination)
Rootkit heuristics (time based analysis)
Generic packer detectors and generic unpacking algorithms
New threat automatic notification
Automatic creation of detection and disinfection signatures for
samples previously analyzed by processing and classification module
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
51. Introduction
Heuristic scanning theory
Panda’s Technology Evolution
Case Study: Modern heuristic scanner features
Genetic Heuristic Engine - Nereus
Summary
Further reading...
Figure: Panda’s integrated endpoint security From [6]
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
52. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Heuristic scanning is the most popular heuristic method for virus
detection and recognition. Basically it is inherited from combination of
pattern matching, automatic learning and environment emulation
metaheuristics. As a heuristic method it’s not 100% effective. So why do
we apply HS?
Pros
Can detect ’future’ threats
User is less dependent on product update
Improves conventional scanning results
Cons
False positives
Making decision after alarm requires knowledge
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
53. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Peter Szor
The Art of Computer Virus Research and Defense
Addison-Wesley Professional, 1st edition, February 2005
Tomasz Andel, Krzysztof Zawadzki
Techniki pisania wirus´w oraz antywirus´w
o o
Inynieria bezpiecze´stwa system´w sieciowych i internetowych, PWr
n o
Wroclaw 2008
Frans Veldman
Heuristic Anti-Virus Technology
http://mirror.sweon.net/madchat/vxdevl/vdat/epheurs1.htm
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
54. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Richard Zwienenberg
Heuristic Scanners: Artificial Intelligence?
http://mirror.sweon.net/madchat/vxdevl/vdat/epheurs2.htm
´
edited by Eric Filiol
Journal in Computer Virology
Springer Paris, Volume 1-4
http://www.springerlink.com/content/119769
Panda Research
From Traditional Antivirus to Collective Intelligence
August 2007
http://research.pandasecurity.com/
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
55. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Various online knowledge repositories
For starters it’s good to search wikipedia...
VX Heavens http://vx.netlux.org/lib
Breaking Business and Technology News https://silicon.com/
IEEE http://ieeexplore.ieee.org/
Zines: Phalcon/Skism: 40HEX, VLAD, VBB: Viruses Bits & Bytes,
Immortal Riot: Insane Reality, NuKe: NuKe IntoJournal, Dark Angel
VirGuide
AND OTHER...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
56. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
Why?...
Questions ?
What if?...
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition
57. Introduction
Heuristic scanning theory
Case Study: Modern heuristic scanner features
Summary
Further reading...
THANK YOU
Wojciech Podg´rski http://podgorski.wordpress.com
o Artificial Intelligence Methods inVirus Detection & Recognition