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.
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We will focus our work in penetration testing methodology reporting form and detailed information how to compare result and related work information.
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...
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Penetration testing reporting and methodologyRashad Aliyev
This paper covering information about Penetration testing methodology, standards reporting formats and comparing reports. Explained problem of Cyber Security experts when they making penetration tests. How they doing current presentations.
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Security Awareness related to common malwares, (viruses, trojans, worms etc) the damages they cause and basic countermeasures one can adopt to protect against them.
How to Hunt for Lateral Movement on Your NetworkSqrrl
Once inside your network, most cyber-attacks go sideways. They progressively move deeper into the network, laterally compromising other systems as they search for key assets and data. Would you spot this lateral movement on your enterprise network?
In this training session, we review the various techniques attackers use to spread through a network, which data sets you can use to reliably find them, and how data science techniques can be used to help automate the detection of lateral movement.
VAPT defines the security measures that are supposed to be put in place to address cyber threats. There are plenty of strategies that can be adopted in Pen Testing which include Black Box Pen Test, White Box Pen Text, Hidden Pen Test, Internal Pen Test, and Gray Box Testing. It is mandatory that VAPT is conducted in order to deter cyber-attacks that are on the upsurge daily. These VAPT ranges from Mobile, Network Penetration Testing, and Vulnerability Assessments.
There are many merits to VAPT in your business which include early error detection in program codes which will prevent cyber attacks. Most companies lose billions of dollars due to cyber-attacks. With VAPT, it guarantees that all loopholes are tightened before an intrusion transpires.
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.
details of tools and methods used in cyber crime & how to protect your system from crimes...
detail study of password cracking, Denial of service, DDoS, steganography, keylogger, proxy server, phishing etc..
COMPARISON OF MALWARE CLASSIFICATION METHODS USING CONVOLUTIONAL NEURAL NETWO...IJNSA Journal
Malicious software is constantly being developed and improved, so detection and classification of malwareis an ever-evolving problem. Since traditional malware detection techniques fail to detect new/unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the API (Application Program Interface) calls. This research uses a database of 7107 instances of API call streams and 8 different malware types:Adware, Backdoor, Downloader, Dropper, Spyware, Trojan, Virus,Worm. We used a 1-Dimensional CNN by mapping API calls as categorical and term frequency-inverse document frequency (TF-IDF) vectors and compared the results to other classification techniques.The proposed 1-D CNN outperformed other classification techniques with 91% overall accuracy for both categorical and TF-IDF vectors.
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLSIJNSA Journal
The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.
Security Awareness related to common malwares, (viruses, trojans, worms etc) the damages they cause and basic countermeasures one can adopt to protect against them.
How to Hunt for Lateral Movement on Your NetworkSqrrl
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Malware Dectection Using Machine learningShubham Dubey
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COMPARISON OF MALWARE CLASSIFICATION METHODS USING CONVOLUTIONAL NEURAL NETWO...IJNSA Journal
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A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLSIJNSA Journal
The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
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.
Optimised Malware Detection in Digital Forensics IJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and problems on any systems are caused by malware and spam. Networks and systems can be accessed and compromised by malware known as botnets, which compromise other systems through a coordinated attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems from the malicious activity of this malware, a new framework is required that aims to develop an optimised technique for malware detection. Hence, this paper demonstrates new approaches to perform malware analysis in forensic investigations and discusses how such a framework may be developed.
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.
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.
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
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.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
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.
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
An Intrusion Detection based on Data mining technique and its intended import...Editor IJMTER
Intrusion detection is a pivotal and essential requirement of today’s era. There are two
major side of Intrusion detection namely, Host based intrusion detection as well as network based
intrusion detection. In Host based intrusion detection system, it monitors the information arrive at the
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system, IDS does not require biased assessment and able to identify massive pattern of attacks.
Moreover, capacity to handle large connection records of network. In this paper we try to discover
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Malware Detection Approaches using Data Mining Techniques.pptx
1. Malware Detection Approaches
using
Data Mining Techniques
Md. Alamgir Hossain
Institute of Information and Communication Technology(IICT)
Bangladesh University of Engineering technology (BUET)
1
2. Contents
Definition of Malware
Concept of Data Mining
Malware Detection Approaches in Data Mining
Signature-Based Approach
Behavior-Based Approach
Challenges to Detect Malware for the Digital World
Suggestions about Malware Detection for Future
Conclusion
References
2
3. Malware
Malware, or malicious software, is any program or file that is intentionally
harmful to a computer, network or server.
Malware can be different types like Viruses, Worms, Trojan Horses,
Ransomware, and Spyware.
These malicious programs steal, encrypt and delete sensitive data; alter or
hijacking core computing functions and monitor end user’s computer activity.
Malware can infect networks and devices and is designed to harm those devices,
networks and/or their users in some way.
3
4. Data Mining
Data mining, also called knowledge discovery in database (KDD), is the
nontrivial extraction of implicit, previously unknown, and potentially useful
information (Meaningful Patterns) from data in large data repositories/database.
Knowledge Discovering Process:
4
5. Malware Detection Approach (Signature-Based)
Signature-based system finds malware using a predefined list that is called
predefined database.
Malicious objects have characteristics that can be used to generate a unique
digital signature.
The database sources include huge number of the various signatures that
classify malicious objects.
Assembly and binary feature extractions are two main methods of this approach.
It is less effective for the quickly changing nature of portable malware or the
variations of known malware.
5
7. Advantages & Weakness of Signature-Based
Detection
Advantages:
Easy to run
Fast Identification
Broadly accessible
Finding comprehensive malware information
Weakness:
Failing to detect the polymorphic malwares
Replicating information in the huge database
7
8. Malware Detection Approach (Behavior-Based)
It reviews the selected behavior to detect the malware.
It gives a superior comprehension of how malware in produced and
implemented.
Malicious behavior is known using a dynamic analysis that evaluates malicious
intent by the object’s code and structure.
API calls and assembly features are two main methods of this approach.
8
10. Advantages & Weakness of Behavior-Based
Detection
Advantages:
Detecting unconceived types of malware attacks
Data-flow dependency detector
Detecting the polymorphic malwares
Weakness:
Storage complexity for behavioral patterns
Time complexity
10
11. Challenges to Detect Malware for the Digital World
Encryption and Decryption Detection
Meta-Heuristic Detection
Real-Time Malware Detection
Etc.
11
12. Suggestions about Malware Detection for Future
Malware detection in the new platform and architecture like Internet of Things
(IoT) applications, E-Banking, and Social Networks etc.
Improving the malware detection for predicting the polymorphism attacks.
Context-Aware detection can be the new idea for dynamic malware detection
approaches.
Providing a safe condition (security) for Big Data against the malware attack.
Etc.
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13. Conclusion
Both are proposed for windows, and smartphones platform and Embedded
System.
Uses Static, Dynamic and Hybrid data analysis methods.
DBScan (Hybrid Pattern Based Text Mining Approach) is the best method on
respect of accuracy in signature-based approach by using ANN, Malicious
Sequential Pattern Based Malware Detection classification techniques.
CloudIntell (Feature Extraction in Cloud) is the best method on respect of
accuracy in behavior-based approach by using SVM, Decision Tree, Static
Boosting classification technique.
Meta heuristic algorithms can speed up and improve the execution time and
overall accuracy.
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14. References
[1] Souri A, Hosseini R (2018) A state-of-the-art survey of malware detection
approaches using data mining techniques. Human-centric Computing and
Information Sciences 8:1-22.
[2] Fraley JB, Figueroa M(2016) Polymorphic malware detection using topological
feature extraction with data mining. SoutheastCon 2016, pp 1-7.
[3] Malhotra A, Bajaj K (2016) A hybrid pattern-based text mining approach for
malware detection using DBScan. Trans ICT 4:141–149.
[4] Boujnouni ME, Jedra M, Zahid N (2015) New malware detection framework
based on N-grams and support vector domain description. In: 2015 11th
international conference on information assurance and security (IAS), pp 123–128.
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15. References
[5] Wang P, Wang Y-S (2015) Malware behavioral detection and vaccine
development by using a support vector model classifier. Journal of Computer and
System Sciences 81:1012–1026.
[6] Sun H, Wang X, Buyya R, Su J (2017) CloudEyes: cloud-based malware
detection with reversible sketch for resourceconstrained internet of things (IoT)
devices. Software—Practice & Experience 47:421–441.
[7] Tang Y, Xiao B, Lu X (2011) Signature tree generation for polymorphic worms.
IEEE Transactions on Computers 60:565–579.
[8] Palumbo P, Sayfullina L, Komashinskiy D, Eirola E, Karhunen J (2017) A
pragmatic android malware detection procedure. Computers and Security 70:689–
701.
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