The document describes research on automatically generating multiple neural network classifiers to detect unknown Win32 viruses through heuristics. Individual classifiers have too high a false positive rate, but combining outputs through voting reduces false positives to arbitrarily low levels with a slight increase in false negatives. The researchers constructed neural network classifiers based on n-gram features from virus samples, combining outputs through voting to achieve low false positive rates suitable for real-world use.
This document summarizes a study on automatically generating heuristic classifiers to detect unknown Win32 viruses. The researchers constructed multiple neural network classifiers using distinct n-gram features from virus samples. They found that combining the individual classifier outputs using voting reduced false positives to a very low level while only slightly increasing false negatives. This voting approach made it practical to retrain the classifiers regularly on updated sample sets.
Application of data mining based malicious code detection techniques for dete...UltraUploader
This document discusses using data mining techniques to detect spyware. It applies Naive Bayes algorithms used in previous work to detect viruses to a dataset of 312 benign and 614 spyware executables. Feature extraction examines byte sequences in files. Initial tests showed low accuracy, but removing Trojan programs from the dataset improved results, with a window size of 4 and without Trojans achieving 80% detection with a 4% false positive rate. Future work proposes testing against larger window sizes and obfuscated code.
A feature selection and evaluation scheme for computer virus detectionUltraUploader
This document proposes a feature selection and evaluation scheme for computer virus detection using machine learning. It presents an exhaustive search method to identify generic n-gram features from virus code, selecting features that meet minimum support thresholds within and across virus families. A hierarchical feature selection process is described to obtain concise yet representative features. The evaluation method aims to simulate detecting new virus outbreaks by testing the classifier on previously unseen viruses from the same families not in the training set.
This document proposes a data mining framework to automatically detect new malicious executables. It extracts features from binaries and uses three data mining classifiers trained on these features: a rule learner, probabilistic classifier, and multi-classifier system. When evaluated on a test set, the framework detects 97.76% of new malicious binaries, more than doubling the detection rate of a signature-based method.
This summary provides the key details about a generic virus scanner in C++ described in the document:
The document describes a generic virus scanner implemented in C++ that can scan files for viruses across different file systems, file types, and operating systems. It defines an abstract class called VirInfo that encapsulates common virus features, and subclasses can be used to define viruses that infect different systems. The scanner's general design allows it to potentially scan for other types of threats beyond just viruses. Signature scanning is identified as the most common and effective method for detecting known viruses described in the document.
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.
Automatic extraction of computer virus signaturesUltraUploader
The document discusses the development of an automatic method for extracting computer virus signatures from machine code. Signatures are short byte sequences used by antivirus programs to identify viruses. Currently, human experts manually select signatures, but this process is time-consuming and cannot keep up with the accelerating rate of new viruses. The authors developed a statistical method to automatically extract signatures with low probabilities of false positives or negatives from virus machine code. This technique has been used to generate over 2,500 signatures for IBM's antivirus database and helps reduce the workload for human experts.
A study of anti virus' response to unknown threatsUltraUploader
A study evaluated 12 popular anti-virus programs' ability to detect unknown threats through behavior analysis. Tests were conducted for keylogging, code injection, and network access. Most programs did not detect the threats. Sophos and Trend Micro blocked some tests but still allowed malicious behavior. The study recommends anti-virus vendors improve detection of unknown malware through behavior analysis.
This document summarizes a study on automatically generating heuristic classifiers to detect unknown Win32 viruses. The researchers constructed multiple neural network classifiers using distinct n-gram features from virus samples. They found that combining the individual classifier outputs using voting reduced false positives to a very low level while only slightly increasing false negatives. This voting approach made it practical to retrain the classifiers regularly on updated sample sets.
Application of data mining based malicious code detection techniques for dete...UltraUploader
This document discusses using data mining techniques to detect spyware. It applies Naive Bayes algorithms used in previous work to detect viruses to a dataset of 312 benign and 614 spyware executables. Feature extraction examines byte sequences in files. Initial tests showed low accuracy, but removing Trojan programs from the dataset improved results, with a window size of 4 and without Trojans achieving 80% detection with a 4% false positive rate. Future work proposes testing against larger window sizes and obfuscated code.
A feature selection and evaluation scheme for computer virus detectionUltraUploader
This document proposes a feature selection and evaluation scheme for computer virus detection using machine learning. It presents an exhaustive search method to identify generic n-gram features from virus code, selecting features that meet minimum support thresholds within and across virus families. A hierarchical feature selection process is described to obtain concise yet representative features. The evaluation method aims to simulate detecting new virus outbreaks by testing the classifier on previously unseen viruses from the same families not in the training set.
This document proposes a data mining framework to automatically detect new malicious executables. It extracts features from binaries and uses three data mining classifiers trained on these features: a rule learner, probabilistic classifier, and multi-classifier system. When evaluated on a test set, the framework detects 97.76% of new malicious binaries, more than doubling the detection rate of a signature-based method.
This summary provides the key details about a generic virus scanner in C++ described in the document:
The document describes a generic virus scanner implemented in C++ that can scan files for viruses across different file systems, file types, and operating systems. It defines an abstract class called VirInfo that encapsulates common virus features, and subclasses can be used to define viruses that infect different systems. The scanner's general design allows it to potentially scan for other types of threats beyond just viruses. Signature scanning is identified as the most common and effective method for detecting known viruses described in the document.
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.
Automatic extraction of computer virus signaturesUltraUploader
The document discusses the development of an automatic method for extracting computer virus signatures from machine code. Signatures are short byte sequences used by antivirus programs to identify viruses. Currently, human experts manually select signatures, but this process is time-consuming and cannot keep up with the accelerating rate of new viruses. The authors developed a statistical method to automatically extract signatures with low probabilities of false positives or negatives from virus machine code. This technique has been used to generate over 2,500 signatures for IBM's antivirus database and helps reduce the workload for human experts.
A study of anti virus' response to unknown threatsUltraUploader
A study evaluated 12 popular anti-virus programs' ability to detect unknown threats through behavior analysis. Tests were conducted for keylogging, code injection, and network access. Most programs did not detect the threats. Sophos and Trend Micro blocked some tests but still allowed malicious behavior. The study recommends anti-virus vendors improve detection of unknown malware through behavior analysis.
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.
Enormous growth and generation of data is happening in every day from various sources. The generated data is presented in various formats, i.e., in structured, unstructured, semi-structured, pdfs, docs, csvs, and raw file formats. All these files are not genuine or pure in all scenarios cause which is generated from identified and unidentified sources. The modern malware is designed with mutation characteristics, that means, it can change its behavior based on the properties of physical file. It is a contraction from malicious software. The tremendous growth of the data is very helpful to the malware designers to execute the malware files such as Virus, Trojans, and Ransomware in any file. The formation of modern malware poses a variety of challenges to the antivirus industries. In this paper, we are going to induce a system with a lightweight model to accurately detect the malware for industrial use with high accuracy. In this, we are identifying nine different types of malwares like Ramnit, Lollipop, Kelihos_ver3, Vundo, etc., on huge amount of data (0.5 TB) that is provided by Microsoft.
Keith J. Jones, Ph.D. - MALGAZER: AN AUTOMATED MALWARE CLASSIFIER WITH RUNNIN...Keith Jones, PhD
The document describes research on developing an automated malware classifier called Malgazer. Key aspects include:
- Optimizing the running window entropy (RWE) algorithm to reduce computation time. RWE is used as a feature for training machine learning models.
- Collecting and exploring a dataset of over 25,000 malware samples classified across 6 functional categories from VirusTotal.
- Training over 200 machine learning experiments using RWE and GIST features, and algorithms including decision trees, random forests, and neural networks. The best models achieved over 94% accuracy.
- Developing Malgazer as an RWE-based malware classifier and web application to classify new samples, achieving performance superior to prior literature
The document summarizes the results of a test comparing the malware protection of Windows 8 and Kaspersky Internet Security. Kaspersky blocked all 42 real-world malware attacks in tests of URLs and emails, while Windows 8 failed to block 5 attacks. In static detection tests, Kaspersky detected 99% of over 111,000 malware files while Windows 8 only detected 90%. Both products detected all 2,500 prevalent malware files and had no false positives on 345,900 clean files. The results indicate Kaspersky provides better protection against modern malware threats than Windows 8 alone.
Auto sign an automatic signature generator for high-speed malware filtering d...UltraUploader
This document describes Auto-Sign, a novel automatic method for generating signatures of malware executables to be used by high-speed malware filtering devices. Auto-Sign disregards signature candidates that appear in benign executables in order to generate highly specific signatures with low false positives. It was tested on large malware applications and aims to rapidly generate signatures once malware is detected, in order to allow signature-based solutions to be quickly updated. The document reviews related work on automatic signature generation and discusses challenges such as generating signatures that are general enough to capture malware instances while avoiding benign traffic.
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
Today’s threats have become very complex and serious in their packing and encryption techniques. Every day new malware variants are becoming increasingly in quantity together with quality by using packing and encrypting techniques. The challenges in this research field are the traditional malware detection systems sometimes might fail to detect new malware variants and produces false alarms. Malicious software in the form of virus, worm, trojan, ransom, and spy harms our computer systems, network environment, and organizations in various ways. Therefore, malware analysis for detection and family classification plays a significant role in Cyber Crime Incident Handling Systems. This system contributes malware family classification with 10 prominent features by conduction feature selection process. The process of labeling the malicious samples using Regular Expressions has been contributed in this approach. The proposed malware classification system provides 7 different families including malware and benign using machine learning classifiers. The finding from our experiment proves that the selected 10 API features provide the best evaluation metrics in terms of accuracy, precision-recall, and ROC scores.
IRJET - Survey on Malware Detection using Deep Learning MethodsIRJET Journal
This document discusses various machine learning methods for malware detection, including support vector machines (SVM), random forests, and decision trees. It provides an overview of each method and related works that have applied these techniques. Specifically, it examines analyses that used linear SVM, random forests on Android apps, and an improved decision tree algorithm to classify malware families. The document concludes that machine learning methods have become important for malware detection as signatures alone cannot keep up with new malware variants.
The proposed solution uses dynamic analysis to identify behavioral patterns and sequences of malware samples. It extracts these patterns using a Cuckoo analysis environment and stores them in a repository using the MAEC language. The detection system then executes suspicious binaries and compares the observed behaviors in real-time to the known malware patterns using ANN classification. This allows detection of novel malware and helps defeat polymorphism, improving over static analysis approaches. The framework is evaluated using the public VXHeaven malware dataset to compare results with the anchor paper, overcoming some of its limitations like inability to decrypt all samples.
TriggerScope: Towards Detecting Logic Bombs in Android ApplicationsPietro De Nicolao
Presentation of paper "TriggerScope: Towards Detecting Logic Bombs in Android Applications" for the course of Advanced Topics in Computer Security of prof. Stefano Zanero.
Source and further information: https://github.com/pietrodn/triggerscope
This document discusses improving techniques for identifying Phytophthora species through metabarcoding. It describes a current method using nested PCR and Illumina sequencing of the ITS1 region, followed by bioinformatic analysis with software like Pycits or METAPY. However, results can vary significantly between clustering tools due to the large number of ITS copies within Phytophthora genomes, ranging from 20 to over 600. Future work involves reassembling genomic ITS regions from long-read sequencing to better understand copy number variation and developing tools that consider the diversity of ITS sequences within species.
Detection of Low Level Sequence Variants by Sanger Sequencing | ESHG 2015 Pos...Thermo Fisher Scientific
Sanger sequencing using fluorescent BigDye® terminator chemistry and semi‐automated capillary electrophoresis (CE) has long been considered the gold standard for identifying sequence variations such as disease‐causing mutations. The robustness, low error rate, ease of use, human interpretable visual displays of the signals generated by the instruments, and low cost per sample and target have all contributed to this reputation. Homozygous and heterozygous germ line mutations are reliably detected and reported using established DNA sequencing analysis software such as the Applied Biosystems Variant Reporter™ software. However, somatic variants with an allelic proportion of 25% or less are often undetected (i.e. not "called") by the software and thus escape awareness if not detected by careful visual inspection of the electropherograms. With the rapid adoption of next generation sequencing technology (NGS) and its use for characterization of specific and discrete mutations in tumor samples, an urgent need has emerged to establish an orthogonal technology for reliable and sensitive detection of somatic mutations which may occur at proportions of 10% or lower compared to the normal allele.
To this end, we have developed an innovative algorithm, software, and a protocol that specialize in the detection and reporting of minor mutations by Sanger sequencing. Moreover the algorithm preserves the ability to generate the familiar displays of the data to facilitate human review. Using panels of prepared mixtures of minor alleles in the range of 2.5%, 5%, 10% and 20%, we have achieved 94.6% sensitivity and 99.8% specificity for automated detection of mutations present at the 5% level with high quality data.
In conclusion, we have demonstrated that standard protocols for fluorescent dye terminator Sanger sequencing in conjunction with the new algorithm delivered in Variant Finder software may enable the identification of de novo somatic mutations to a level of 5%. This technology will also be useful for the confirmation of minor variants identified by NGS platforms.
IRJET- Zombie - Venomous File: Analysis using Legitimate Signature for Securi...IRJET Journal
The document discusses a proposed method for detecting viruses and malware that evade existing antivirus software. It uses a combination of analyzing files with VirusTotal's database of known threats and applying natural language processing techniques like suffix trees and TF-IDF to identify malicious patterns in files. An evaluation shows the proposed method can detect viruses that existing antivirus and VirusTotal miss, achieving a 97% accuracy rate in testing.
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.
Integrated Feature Extraction Approach Towards Detection of Polymorphic Malwa...CSCJournals
Some malware are sophisticated with polymorphic techniques such as self-mutation and emulation based analysis evasion. Most anti-malware techniques are overwhelmed by the polymorphic malware threats that self-mutate with different variants at every attack. This research aims to contribute to the detection of malicious codes, especially polymorphic malware by utilizing advanced static and advanced dynamic analyses for extraction of more informative key features of a malware through code analysis, memory analysis and behavioral analysis. Correlation based feature selection algorithm will be used to transform features; i.e. filtering and selecting optimal and relevant features. A machine learning technique called K-Nearest Neighbor (K-NN) will be used for classification and detection of polymorphic malware. Evaluation of results will be based on the following measurement metrics-True Positive Rate (TPR), False Positive Rate (FPR) and the overall detection accuracy of experiments.
A cost analysis of typical computer viruses and defensesUltraUploader
This document analyzes the costs of typical computer virus defenses including virus scanners, monitors, cryptographic checksums, and integrity shells. It finds that the largest cost for scanners and cryptographic checksums comes from regularly scanning systems, while monitors and integrity shells have lower costs since they detect viruses before infection can spread. Integrity shells are optimal for defense since they detect all viruses without requiring updates like other methods. Overall costs are calculated based on licensing fees, employee time for scans, potential cleanup costs from infections, and other factors.
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODSijaia
This document presents a static malware detection system using data mining techniques. The system extracts raw features from Windows Portable Executable (PE) files including PE header information, DLLs, and API functions. It then selects important features using Information Gain and reduces dimensions using Principal Component Analysis. Three classifiers (SVM, J48, Naive Bayes) are trained on the transformed feature vectors to classify files as malicious or benign. When evaluated on a dataset of over 247,000 files, the system achieved a detection rate of 99.6%.
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.
Anomalous payload based network intrusion detectionUltraUploader
This document summarizes a payload-based anomaly detection system called PAYL that models normal application payloads for network traffic. PAYL computes a byte frequency distribution profile during training and uses Mahalanobis distance during detection to measure similarity to the profile. It was shown to achieve nearly 100% accuracy with a 0.1% false positive rate on some datasets. The system aims to detect new worms and exploits at a network gateway before propagation.
This document summarizes a research paper about binary obfuscation techniques that aim to make reverse engineering of software more difficult. The paper proposes replacing control transfer instructions like jumps and calls with signals (traps) that are handled by signal handling code to perform the control transfer. It also inserts dummy control transfers and junk instructions after traps to confuse disassemblers. Experimental results show this obfuscation causes disassemblers to miss 30-80% of instructions and make mistakes on over half of control flow edges, while increasing execution time.
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.
Enormous growth and generation of data is happening in every day from various sources. The generated data is presented in various formats, i.e., in structured, unstructured, semi-structured, pdfs, docs, csvs, and raw file formats. All these files are not genuine or pure in all scenarios cause which is generated from identified and unidentified sources. The modern malware is designed with mutation characteristics, that means, it can change its behavior based on the properties of physical file. It is a contraction from malicious software. The tremendous growth of the data is very helpful to the malware designers to execute the malware files such as Virus, Trojans, and Ransomware in any file. The formation of modern malware poses a variety of challenges to the antivirus industries. In this paper, we are going to induce a system with a lightweight model to accurately detect the malware for industrial use with high accuracy. In this, we are identifying nine different types of malwares like Ramnit, Lollipop, Kelihos_ver3, Vundo, etc., on huge amount of data (0.5 TB) that is provided by Microsoft.
Keith J. Jones, Ph.D. - MALGAZER: AN AUTOMATED MALWARE CLASSIFIER WITH RUNNIN...Keith Jones, PhD
The document describes research on developing an automated malware classifier called Malgazer. Key aspects include:
- Optimizing the running window entropy (RWE) algorithm to reduce computation time. RWE is used as a feature for training machine learning models.
- Collecting and exploring a dataset of over 25,000 malware samples classified across 6 functional categories from VirusTotal.
- Training over 200 machine learning experiments using RWE and GIST features, and algorithms including decision trees, random forests, and neural networks. The best models achieved over 94% accuracy.
- Developing Malgazer as an RWE-based malware classifier and web application to classify new samples, achieving performance superior to prior literature
The document summarizes the results of a test comparing the malware protection of Windows 8 and Kaspersky Internet Security. Kaspersky blocked all 42 real-world malware attacks in tests of URLs and emails, while Windows 8 failed to block 5 attacks. In static detection tests, Kaspersky detected 99% of over 111,000 malware files while Windows 8 only detected 90%. Both products detected all 2,500 prevalent malware files and had no false positives on 345,900 clean files. The results indicate Kaspersky provides better protection against modern malware threats than Windows 8 alone.
Auto sign an automatic signature generator for high-speed malware filtering d...UltraUploader
This document describes Auto-Sign, a novel automatic method for generating signatures of malware executables to be used by high-speed malware filtering devices. Auto-Sign disregards signature candidates that appear in benign executables in order to generate highly specific signatures with low false positives. It was tested on large malware applications and aims to rapidly generate signatures once malware is detected, in order to allow signature-based solutions to be quickly updated. The document reviews related work on automatic signature generation and discusses challenges such as generating signatures that are general enough to capture malware instances while avoiding benign traffic.
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
Today’s threats have become very complex and serious in their packing and encryption techniques. Every day new malware variants are becoming increasingly in quantity together with quality by using packing and encrypting techniques. The challenges in this research field are the traditional malware detection systems sometimes might fail to detect new malware variants and produces false alarms. Malicious software in the form of virus, worm, trojan, ransom, and spy harms our computer systems, network environment, and organizations in various ways. Therefore, malware analysis for detection and family classification plays a significant role in Cyber Crime Incident Handling Systems. This system contributes malware family classification with 10 prominent features by conduction feature selection process. The process of labeling the malicious samples using Regular Expressions has been contributed in this approach. The proposed malware classification system provides 7 different families including malware and benign using machine learning classifiers. The finding from our experiment proves that the selected 10 API features provide the best evaluation metrics in terms of accuracy, precision-recall, and ROC scores.
IRJET - Survey on Malware Detection using Deep Learning MethodsIRJET Journal
This document discusses various machine learning methods for malware detection, including support vector machines (SVM), random forests, and decision trees. It provides an overview of each method and related works that have applied these techniques. Specifically, it examines analyses that used linear SVM, random forests on Android apps, and an improved decision tree algorithm to classify malware families. The document concludes that machine learning methods have become important for malware detection as signatures alone cannot keep up with new malware variants.
The proposed solution uses dynamic analysis to identify behavioral patterns and sequences of malware samples. It extracts these patterns using a Cuckoo analysis environment and stores them in a repository using the MAEC language. The detection system then executes suspicious binaries and compares the observed behaviors in real-time to the known malware patterns using ANN classification. This allows detection of novel malware and helps defeat polymorphism, improving over static analysis approaches. The framework is evaluated using the public VXHeaven malware dataset to compare results with the anchor paper, overcoming some of its limitations like inability to decrypt all samples.
TriggerScope: Towards Detecting Logic Bombs in Android ApplicationsPietro De Nicolao
Presentation of paper "TriggerScope: Towards Detecting Logic Bombs in Android Applications" for the course of Advanced Topics in Computer Security of prof. Stefano Zanero.
Source and further information: https://github.com/pietrodn/triggerscope
This document discusses improving techniques for identifying Phytophthora species through metabarcoding. It describes a current method using nested PCR and Illumina sequencing of the ITS1 region, followed by bioinformatic analysis with software like Pycits or METAPY. However, results can vary significantly between clustering tools due to the large number of ITS copies within Phytophthora genomes, ranging from 20 to over 600. Future work involves reassembling genomic ITS regions from long-read sequencing to better understand copy number variation and developing tools that consider the diversity of ITS sequences within species.
Detection of Low Level Sequence Variants by Sanger Sequencing | ESHG 2015 Pos...Thermo Fisher Scientific
Sanger sequencing using fluorescent BigDye® terminator chemistry and semi‐automated capillary electrophoresis (CE) has long been considered the gold standard for identifying sequence variations such as disease‐causing mutations. The robustness, low error rate, ease of use, human interpretable visual displays of the signals generated by the instruments, and low cost per sample and target have all contributed to this reputation. Homozygous and heterozygous germ line mutations are reliably detected and reported using established DNA sequencing analysis software such as the Applied Biosystems Variant Reporter™ software. However, somatic variants with an allelic proportion of 25% or less are often undetected (i.e. not "called") by the software and thus escape awareness if not detected by careful visual inspection of the electropherograms. With the rapid adoption of next generation sequencing technology (NGS) and its use for characterization of specific and discrete mutations in tumor samples, an urgent need has emerged to establish an orthogonal technology for reliable and sensitive detection of somatic mutations which may occur at proportions of 10% or lower compared to the normal allele.
To this end, we have developed an innovative algorithm, software, and a protocol that specialize in the detection and reporting of minor mutations by Sanger sequencing. Moreover the algorithm preserves the ability to generate the familiar displays of the data to facilitate human review. Using panels of prepared mixtures of minor alleles in the range of 2.5%, 5%, 10% and 20%, we have achieved 94.6% sensitivity and 99.8% specificity for automated detection of mutations present at the 5% level with high quality data.
In conclusion, we have demonstrated that standard protocols for fluorescent dye terminator Sanger sequencing in conjunction with the new algorithm delivered in Variant Finder software may enable the identification of de novo somatic mutations to a level of 5%. This technology will also be useful for the confirmation of minor variants identified by NGS platforms.
IRJET- Zombie - Venomous File: Analysis using Legitimate Signature for Securi...IRJET Journal
The document discusses a proposed method for detecting viruses and malware that evade existing antivirus software. It uses a combination of analyzing files with VirusTotal's database of known threats and applying natural language processing techniques like suffix trees and TF-IDF to identify malicious patterns in files. An evaluation shows the proposed method can detect viruses that existing antivirus and VirusTotal miss, achieving a 97% accuracy rate in testing.
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.
Integrated Feature Extraction Approach Towards Detection of Polymorphic Malwa...CSCJournals
Some malware are sophisticated with polymorphic techniques such as self-mutation and emulation based analysis evasion. Most anti-malware techniques are overwhelmed by the polymorphic malware threats that self-mutate with different variants at every attack. This research aims to contribute to the detection of malicious codes, especially polymorphic malware by utilizing advanced static and advanced dynamic analyses for extraction of more informative key features of a malware through code analysis, memory analysis and behavioral analysis. Correlation based feature selection algorithm will be used to transform features; i.e. filtering and selecting optimal and relevant features. A machine learning technique called K-Nearest Neighbor (K-NN) will be used for classification and detection of polymorphic malware. Evaluation of results will be based on the following measurement metrics-True Positive Rate (TPR), False Positive Rate (FPR) and the overall detection accuracy of experiments.
A cost analysis of typical computer viruses and defensesUltraUploader
This document analyzes the costs of typical computer virus defenses including virus scanners, monitors, cryptographic checksums, and integrity shells. It finds that the largest cost for scanners and cryptographic checksums comes from regularly scanning systems, while monitors and integrity shells have lower costs since they detect viruses before infection can spread. Integrity shells are optimal for defense since they detect all viruses without requiring updates like other methods. Overall costs are calculated based on licensing fees, employee time for scans, potential cleanup costs from infections, and other factors.
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODSijaia
This document presents a static malware detection system using data mining techniques. The system extracts raw features from Windows Portable Executable (PE) files including PE header information, DLLs, and API functions. It then selects important features using Information Gain and reduces dimensions using Principal Component Analysis. Three classifiers (SVM, J48, Naive Bayes) are trained on the transformed feature vectors to classify files as malicious or benign. When evaluated on a dataset of over 247,000 files, the system achieved a detection rate of 99.6%.
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.
Anomalous payload based network intrusion detectionUltraUploader
This document summarizes a payload-based anomaly detection system called PAYL that models normal application payloads for network traffic. PAYL computes a byte frequency distribution profile during training and uses Mahalanobis distance during detection to measure similarity to the profile. It was shown to achieve nearly 100% accuracy with a 0.1% false positive rate on some datasets. The system aims to detect new worms and exploits at a network gateway before propagation.
This document summarizes a research paper about binary obfuscation techniques that aim to make reverse engineering of software more difficult. The paper proposes replacing control transfer instructions like jumps and calls with signals (traps) that are handled by signal handling code to perform the control transfer. It also inserts dummy control transfers and junk instructions after traps to confuse disassemblers. Experimental results show this obfuscation causes disassemblers to miss 30-80% of instructions and make mistakes on over half of control flow edges, while increasing execution time.
Antivirus software testing for the new milleniumUltraUploader
This document discusses the need for standardized testing of antivirus software to properly evaluate claims by vendors of providing "faster, better, cheaper" protection. It outlines the current state of antivirus testing, including certification programs run by ICSA, Westcoast Labs, and universities. The tests evaluate detection of viruses in the wild and ability to disinfect. The document argues for a functional approach to testing that is not specific to any vendor or product.
A week is a long time in computer ethicsUltraUploader
Over the course of a week, numerous news articles reported on various ethical issues arising from the use of technology. Issues discussed included intellectual property theft through peer-to-peer file sharing and concerns over globalization. Other topics included the growing problems of identity theft, computer viruses and hacking, junk mail and spam, censorship and surveillance. The week demonstrated the many challenges posed by technology and how each issue often has countervailing perspectives around issues like freedom of expression, prevention versus cure of computer misuse, and information access versus information control.
The document discusses automatic deobfuscation of binary code. It presents a local semantic analysis approach that rewrites binary code in a simpler form without relying on manual identification of obfuscation patterns. The approach uses compiler optimization techniques like constant propagation, folding, and stack optimization on virtual machine handler functions to drastically simplify the obfuscated code. It is able to reduce handler functions from 100-200 instructions to at most 10 instructions within a single basic block.
Viruses spread by infecting executable programs, which then infect other programs when they are run. As infected programs are executed by different users who have authority over other programs and files, the virus can propagate throughout the system. Standard protection mechanisms in time-sharing systems are not sufficient to prevent the spread of viruses in this manner.
Anti malware tools intrusion detection systemsUltraUploader
This document provides an overview of using intrusion detection systems (IDS), specifically Snort, to detect malware. It discusses getting and installing Snort on Windows and Linux, and recommended additions like MySQL, BASE, and ACID. The document outlines what the finished installation interface might look like when using BASE or ACID. It then discusses how to create effective malware signatures/rules for Snort by examining malware samples and extracting detection strings from their code or encoded attachments. Creating rules for multi-vector malware may require multiple signatures to detect it in different propagation states.
Are the current computer crime laws sufficient or should the writing of virus...UltraUploader
This document discusses whether current computer crime laws are sufficient to address the writing of virus code or if this activity should be prohibited. It begins with background on cybercrime and viruses, defining viruses, worms, and payloads. It describes how malware is released and the threats posed by viruses and worms. It outlines current US federal and state computer crime laws and their limitations in addressing virus writing. The document argues that directly prohibiting virus writing may be needed and examines how a new statute could address this and potential issues it may raise regarding free speech.
A survey of cryptologic issues in computer virologyUltraUploader
The document discusses how cryptographic techniques can be used maliciously in computer virology to evade antivirus detection. It covers how encryption can be used to randomly generate IP addresses for worm propagation, polymorphically mutate viral code, and armor code to prevent analysis. As an example, it analyzes how a programming error in the random number generator of the Sapphire/Slammer worm led to poor randomness and biased propagation.
A sense of 'danger' for windows processesUltraUploader
This document summarizes research on using Dendritic Cell Algorithms (DCA) for malware detection. The researchers collected API call traces from real malware and benign Windows processes to evaluate the accuracy of classical DCA (cDCA) and deterministic DCA (dDCA) for classifying processes as malware or benign. They also studied the effects of antigen multiplier and time-windows on the detection accuracy of the algorithms.
Bird binary interpretation using runtime disassemblyUltraUploader
The document describes BIRD (Binary Interpretation using Runtime Disassembly), a binary analysis and instrumentation infrastructure for the Windows/x86 platform. BIRD combines static and dynamic disassembly to guarantee that every instruction in a binary is analyzed before execution. It provides services to convert binary code to assembly and insert instrumentation code without affecting program semantics. The prototype took 12 student months to develop and can successfully analyze applications like Microsoft Office, Internet Explorer, and IIS with low overhead of below 4%.
Accurately detecting source code of attacks that increase privilegeUltraUploader
The document discusses developing a system to detect source code for attacks that increase privilege before they are executed. The system separates incoming data into categories like C code or shell code. Features are extracted from each sample and used to estimate if it contains attack code. The system has been evaluated on large databases of normal and attack software written by many authors, with results showing accurate detection of attack code.
Applications of genetic algorithms to malware detection and creationUltraUploader
This document summarizes and analyzes previous research on applying genetic algorithms to malware detection and creation. Section 2 summarizes a paper that compared the performance of genetic algorithm-based classifiers to non-genetic classifiers for detecting malware. It found genetic algorithms performed comparably to other methods in classification accuracy but with lower processing overhead. Sections 3 and 4 summarize papers applying genetic algorithms to optimize parameters for real-time malware detection and to evolve malware signatures similar to antibodies. Section 5 discusses using genetic algorithms to evolve malware. The document analyzes the effectiveness of genetic algorithms for malware detection tasks and issues around using them to evolve malware.
Agisa towards automatic generation of infection signaturesUltraUploader
This document describes AGIS, a system for automatically generating infection signatures to detect compromised systems. AGIS monitors the runtime behavior of suspicious code to detect infections based on security policy violations. It then uses dynamic and static analysis to identify the characteristic behaviors of the malware in terms of system/API calls. Important instructions related to the infection's malicious behavior are extracted from executables to generate signatures that can be used by scanners to detect the infection. AGIS was implemented on Windows and evaluated against real malware samples, demonstrating its ability to detect new infections and generate high-quality signatures.
This document summarizes the authors' experience over two years fuzzing VoIP devices to discover vulnerabilities. They used their in-house tool KIF to conduct stateful protocol fuzzing on a variety of VoIP equipment. The testing uncovered many vulnerabilities related to weak input validation, including buffer overflows and format string issues. Some vulnerabilities allowed compromising internal networks by exploiting unfiltered web interfaces on VoIP phones. The authors disclosed vulnerabilities responsibly and provided mitigation techniques.
Approaching zero the extraordinary underworld of hackers, phreakers, virus ...UltraUploader
This document provides a summary of the prologue of the book "Approaching Zero" which details the story of a teenage computer hacker known as "Fry Guy". The summary is as follows:
Fry Guy is able to hack into one of the most secure computer systems in the US containing credit histories by impersonating an employee over the phone. He then uses the account information to access the system from his home computer. The prologue provides background on how Fry Guy became interested in computers and hacking from a young age and would spend hours exploring phone and computer systems on his own.
The document provides a detailed chronology and analysis of the Morris worm, one of the earliest computer worms to spread via the Internet. It summarizes that on November 2, 1988, a self-replicating program was released that infected hundreds or thousands of computers running UNIX via vulnerabilities in sendmail, finger, and rsh/rexec. It then analyzes the worm's code to describe how it spread, hid itself, and avoided detection by system administrators as it rapidly propagated across the Internet.
Automated classification and analysis of internet malwareUltraUploader
The document summarizes research on analyzing how existing anti-virus software classifies malware. It finds that anti-virus products provide labels for malware that are inconsistent across products, incomplete in covering all malware, and lack concise semantics. To address these limitations, the research proposes a new technique for classifying malware based on its behavior and system changes, and automatically grouping similar behaviors. It evaluates the approach using large and diverse malware datasets.
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Self Evolving Antivirus Based on Neuro-Fuzzy Inference SystemIJRES Journal
With today’s world filled with information and data, it is very important for one to know which information or data is harmless and which is harmful. Right from cellular phones to big MNCs and Server companies require a security system that is as competent and adaptive as its ever-updating and evolving viruses or malware. The paper talks about the development and implementation of a new idea Adaptive anti-virus based on Anfis logic. An adaptive anti-virus system that will catch up to the speed at which the viruses update and evolve.
This document discusses detecting malware using n-grams and machine learning algorithms. It analyzes executable files to extract n-gram sequences from the opcode, creates a feature vector table (FVT) of n-grams and their frequencies. This FVT is used to train and test machine learning classifiers like J48, SVM, and Random Forest. Dimensionality reduction using PCA is also applied before classification. The models are evaluated based on metrics like accuracy, misclassification rate, and precision on n-gram datasets of different sizes. Random Forest performs best with over 95% accuracy on 2-grams.
System Event Monitoring for Active AuthenticationCoveros, Inc.
The authors use system event monitoring to distinguish between the behavioral characteristics of normal and anomalous computer system users. Identifying anomalous behavior at the system event level diminishes privacy concerns and supports the identification of cross-application behavioral patterns.
IRJET- A Review on Application of Data Mining Techniques for Intrusion De...IRJET Journal
This document reviews the application of data mining techniques for intrusion detection. It discusses different types of intrusion detection systems including misuse detection, anomaly detection, host-based IDS, network-based IDS, and hybrid IDS. It also covers the drawbacks of conventional IDS and various data mining algorithms that have been used for intrusion detection like decision trees, naive Bayes classifiers, k-means clustering, and SVM. The document concludes that integrating various data mining algorithms can help improve accuracy for attack prediction and classification in intrusion detection systems.
This document discusses artificial immune systems and their applications in mobile ad hoc networks (MANETs). It describes various artificial immune system algorithms inspired by theoretical immunology, including negative selection, artificial immune networks, clonal selection, danger theory, and dendritic cell algorithms. These algorithms can be used for intrusion detection in MANETs to provide self-healing, self-defensive, and self-organizing capabilities to address security challenges in infrastructure-less mobile networks. Several studies have investigated applying artificial immune system approaches like negative selection and clonal selection to detect node misbehavior and classify nodes as self or non-self in MANETs.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
The systems connected to the network are vulnerable to many malicious programs which threatens the
confidentiality, integrity and availability of a system. Many malicious programs such as viruses, worms, trojan horses, adware,
scareware exists. A new malicious program has gained momentum known as spyware. Traditional techniques such as
Signature-based Detection and Heuristic-based Detection have not performed well in detecting Spyware. Based on the recent
studies it has been proven that data mining techniques yield better results than these traditional techniques. This paper presents
detection of spyware using data mining approach. Here binary feature extraction takes place from executable files, which is
then followed by feature reduction process so that it can be used as training set to generate classifiers. Hence, the generated
classifiers classify new and previously unseen binaries as benign files or spywares.
A zero-day (also known as 0-day) vulnerability is a computer-software vulnerability that is unknown to those who would be interested in mitigating the vulnerability.
The document discusses different types of antivirus testing methods and potential ways to exploit weaknesses in those methods. It describes "wildcore" testing using real malware samples and "zoo" testing using large malware collections. It also outlines "retrospective" testing using older signature databases. The document suggests hacks like automatically signing samples, customizing settings, and detecting other antivirus products' false positives to manipulate test results. Feedback from the antivirus industry is mixed, with some condoning common practices while others find them problematic.
DETECTION OF MALICIOUS EXECUTABLES USING RULE BASED CLASSIFICATION ALGORITHMSAAKANKSHA JAIN
Slide present statistical mining of Malicious-Executable dataset collected from various antivirus log-files and other sources.
Further classifications of malicious code as per their impact on user's system & distinguishes threats on the muse in their connected severity.
Implementation of the algorithms JRIP ,PART and RIDOR in additional economical manner to acquire a level of accuracy to the classification results.
MultiAgent artificial immune system for network intrusion detectionAboul Ella Hassanien
This thesis implements a multi-agent anomaly network intrusion detection system inspired by biological immunity to detect and classify network attacks. It proposes five approaches, including using a genetic algorithm to generate anomaly detectors, discretizing continuous features to create homogeneity between different feature types, and applying feature selection techniques. The approaches are evaluated on datasets like NSL-KDD to generate detectors for identifying anomalous network connections using measures like Euclidean, Minkowski, and Hamming distance. While initial results are promising, further work is needed to optimize feature selection and evaluate the approaches on additional datasets and attack types.
Artificial immune system against viral attackUltraUploader
This document discusses an artificial immune system approach for detecting computer viruses. It begins by providing background on artificial immune systems and how they can be applied to computer security similar to how the human immune system distinguishes self from non-self. It then describes the proposed artificial immune system-based virus detection system, which includes a signature extractor that generates signatures for non-self programs that do not match self programs, and a signature selector that analyzes the signatures to determine if they belong to viruses or self programs. The system aims to detect unknown viruses through an adaptive process of learning virus signatures.
Abstract: The exponential growth of the internet and new technology lead today's world in a hectic situation both positive as well as the negative module. Cybercriminals gamble in the dark net using numerous techniques. This leads to cybercrime. Cyber threats like Malware attempt to infiltrate the computer or mobile device offline or internet, chat(online), and anyone can be a potential target. Malware is also known as malicious software is often used by cybercriminals to achieve their goal by tracking internet activity, capturing sensitive information, or blocking computer access. Reverse engineering is one of the best ways to prevent and is a powerful tool to keep the fight against cyber attacks. Most people in the cyber world see it as a black hat—It is said as being used to steal data and intellectual property. But when it is in the hands of cybersecurity experts, reverse engineering dons the white hat of the hero. Looking at the program from the outside in –often by a third party that had no hand in writing the code. It allows those who practice it to understand how a given program or system works when no source code is available. Reverse engineering accomplishing several tasks related to cybersecurity: finding system vulnerabilities, researching malware &analyzing the complexity of restoring core software algorithms that can further protect against theft. It is hard to hack certain software.
Keywords: Malware, threat, vulnerablity, detection, reverse engineering, analysis.
Title: Malware analysis and detection using reverse Engineering
Author: B.Rashmitha, J. Alwina Beauty Angelin, E.R. Ramesh
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 10, Issue 2, Month: April 2022 - June 2022
Page: (1-4)
Published Date: 01-April-2022
Research Publish Journals
Available at: www.researchpublish.com
You can Direct download full research paper at given below link:
https://www.researchpublish.com/papers/malware-analysis-and-detection-using-reverse-engineering
Academia Link: https://www.academia.edu/76069664/Malware_analysis_and_detection_using_reverse_Engineering_Available_at_www_researchpublish_com_journal_name_International_Journal_of_Computer_Science_and_Information_Technology_Research
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
This document summarizes a research paper that proposes using an ensemble of k-nearest neighbor (k-NN) classifiers with genetic programming to improve network intrusion detection. The researchers trained classifiers on the KDD Cup 1999 dataset, which contains network traffic labeled as normal or an attack of various types. They preprocessed the data to remove redundancy and applied feature selection before training. The ensemble of k-NN classifiers classified data into five categories - one normal and four attack types - and achieved 99.97% accuracy on testing after genetic programming optimized the ensemble.
Malware Detection Using Data Mining Techniques Akash Karwande
This document discusses techniques for malware detection using data mining. It begins by defining the problem of malware as one of the most serious issues faced on the internet. It then discusses types of malware like viruses, worms, trojans, and rootkits. It describes how rootkits can hide themselves and their activities. The document outlines static and dynamic analysis methods for malware detection and describes signature-based and behavior-based detection techniques. It shows results from using the Weka tool achieving over 97% success in rootkit detection. Advanced techniques discussed include n-grams and analyzing API/system calls.
Review of Intrusion and Anomaly Detection Techniques IJMER
Intrusion detection is the act of detecting actions that attempt to compromise the
confidentiality, integrity or availability of a resource. With the tremendous growth of network-based
services and sensitive information on networks, network security is getting more and more importance
than ever. Intrusion poses a serious security threat in a huge network environment. The increasing use of
internet has dramatically added to the growing number of threats that inhabit within it. Intrusion
detection does not, in general, include prevention of intrusions. Now a days Network intrusion detection
systems have become a standard component in the area of security infrastructure. This review paper tries
to discusses various techniques which are already being used for intrusion detection.
Similar to Automatically generated win32 heuristic virus detection (20)
This document is the manual for PHP, the PHP Documentation Group's copyright from 1997 to 2002. It contains information about installing and configuring PHP on various operating systems like Unix, Linux, Windows, etc. It also covers PHP syntax, functions, classes, and other features. The manual is distributed under the GNU General Public License and parts of it are also distributed under the Open Publication License. It was translated into Italian with contributions from multiple people.
Broadband network virus detection system based on bypass monitorUltraUploader
The document describes a Broadband Network Virus Detection System (VDS) based on bypass monitoring that can detect viruses on high-speed networks. The VDS uses four detection engines to analyze network traffic for viruses based on binary content, URLs, emails, and scripts. It accurately logs statistical information on detected viruses like name, source/target IPs, and spread frequency. The VDS mirrors network traffic to a detection engine in real-time without needing to reassemble packets into files. This allows it to efficiently detect viruses directly in network packets or data streams on gigabit-speed networks.
This document discusses botnets and their applications. It begins with an overview of botnets, how they are controlled through command and control servers, and how rootkits can help conceal botnet activity. It then explores how botnets can be used for spam, phishing, click fraud, identity theft, and distributed denial-of-service attacks. Detection and mitigation techniques are also summarized, including network intrusion detection, honeynets, DNS monitoring, and modeling botnet propagation across timezones. Recent botnets like AgoBot, PhatBot, and Bobax are also examined in the context of spam distribution. Open research questions around botnet membership detection, click fraud detection, and phishing detection are presented.
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Bot software infects millions of computers worldwide without the owners' knowledge and turns them into zombies that perform malicious tasks as part of a bot network. These bot networks, which can include thousands of infected computers, are used to spread viruses and worms, send spam emails, install spyware, and launch denial-of-service attacks. While initially just an automated way to spread malware, bot networks are now also used for criminal activities like identity theft due to their ability to stealthily command a large number of compromised computers. Security experts warn that the proliferation of bot networks poses serious risks and is very difficult to stop given their automation and scale.
Blended attacks exploits, vulnerabilities and buffer overflow techniques in c...UltraUploader
The document discusses blended threats that combine exploits and vulnerabilities with computer viruses. It begins with definitions of blended attacks and buffer overflows. It then describes three generations of buffer overflow techniques as well as other vulnerabilities exploited by blended threats, such as URL encoding and MIME header parsing. The document also discusses past threats like the Morris worm and CodeRed that blended exploits with viruses, and techniques used to combat future blended threats through defense in depth.
Win32/Blaster was a worm that exploited a vulnerability in Windows RPC to infect systems running Windows 2000 and Windows XP. It installed itself to automatically run on startup and then attempted to infect other systems on the local network and randomly selected IP addresses. The infection process involved exploiting the RPC vulnerability to execute a remote shell, downloading the worm binary, and executing it. It also launched a SYN flooding DDoS attack against Windows Update sites each month after the 16th. The worm spread quickly after the vulnerability was disclosed and highlighted the increasing automation and harm of worms.
Biologically inspired defenses against computer virusesUltraUploader
This document discusses two biologically inspired approaches to computer virus detection and removal: a neural network virus detector that learns to identify infected and uninfected programs, and a computer immune system that can automatically identify, analyze, and remove new viruses from a system. The neural network technique has been incorporated into an IBM commercial antivirus product, while the computer immune system is still in prototype form. Both aim to replace human analysis of viruses to allow faster response times needed to address increasing rates of new virus creation and spread.
1. The document discusses biological viruses and computer viruses, providing background on how biological viruses work by hijacking cellular mechanisms of DNA replication, transcription and translation. It defines a computer virus as a piece of code with self-replicating ability that relies on other programs to exist, similar to biological viruses. 2. Computer viruses can cause damage by infecting programs which then infect other programs, potentially spreading like an epidemic across connected computers. 3. The document argues that a better understanding of biological and computer mechanisms can help improve defenses against viruses.
Biological aspects of computer virologyUltraUploader
This document discusses biological aspects of computer viruses and how factors that influence the spread of biological pathogens can also affect the propagation of computer malware. It analyzes three major factors that influence the spread of a computer worm: the infection propagator, which examines characteristics of exploited vulnerabilities like prevalence and age; the target locator, which focuses on how worms find new targets; and the worm's virulence, which looks at aspects that increase its infectiousness. The document suggests studying computer virus propagation through the lens of epidemiology models used for infectious diseases.
Biological models of security for virus propagation in computer networksUltraUploader
This document discusses how biological models of disease propagation and defense mechanisms in living organisms can inspire new approaches to computer network security and virus detection. Specifically, it describes how genetic regulatory networks that turn off harmful genes, protein interaction networks that model cellular processes, and epidemiological models of disease spread can provide models for automatically detecting and containing computer viruses without relying solely on pre-defined virus signatures. The authors propose several new security models drawing on these biological analogies, such as using surrogate code to maintain system functionality when parts are shut off, modeling network interactions to determine how viruses propagate, and evolving network services in real-time to reconstitute functionality after attacks.
Beyond layers and peripheral antivirus securityUltraUploader
This white paper from Trend Micro discusses strategies for effective antivirus security beyond just protecting desktops. It argues that while desktop protection is still important, viruses often spread faster than antivirus updates can be deployed to endpoints. It therefore recommends taking additional measures across the network like stopping viruses at email/file servers, firewalls, and through education. The paper provides an overview of virus impacts and outlines Trend Micro's solutions that can block new threats before pattern updates and help repair damage.