Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection

Firdausi, Ivan; Lim, Charles; Erwin, Alva; Nugroho, Anto Satriyo

doi:10.1109/act.2010.33

Cited by 259 publications

(136 citation statements)

References 2 publications

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“…The graph structure, in-degree, out-degree of nodes and root denotes the features of malware activity. As per [31,24], J48 decision trees given better TPR, FPR and accuracy results in comparison with other classifiers. Firdausi et al [24] propounded a malware detection system which monitors the behavior of malicious files in controlled environment using a free online dynamic analysis tool named Anubis.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…As per [31,24], J48 decision trees given better TPR, FPR and accuracy results in comparison with other classifiers. Firdausi et al [24] propounded a malware detection system which monitors the behavior of malicious files in controlled environment using a free online dynamic analysis tool named Anubis. Then the generated results are parsed into vector model for classification on the basis of the trained classifier.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

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Comparative Analysis of Feature Extraction Methods of Malware Detection

Ranveer¹,

Hiray²

2015

IJCA

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Recent years have encountered massive growth in malwares which poses a severe threat to modern computers and internet security. Existing malware detection systems are confronting with unknown malware variants. Recently developed malware detection systems investigated that the diverse forms of malware exhibit similar patterns in their structure with minor variations. Hence, it is required to discriminate the types of features extracted for detecting malwares. So that potential of malware detection system can be leveraged to combat with unfamiliar malwares. We mainly focus on the categorization of features based on malware analysis. This paper highlights general framework of malware detection system and pinpoints strengths and weaknesses of each method. Finally we presented overview of performance of present malware detection systems based on features.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Section: Feature Extraction Methodsmentioning

confidence: 99%

Comparative Analysis of Feature Extraction Methods of Malware Detection

Ranveer¹,

Hiray²

2015

IJCA

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“…Often, suspicious software is further analyzed if it shows a similar behavior to already known malware. Machine learning and classification approaches are used for analysis, e.g., Firdausi et al [20] analyzed various classification algorithms for the use in malware analysis, Rieck et al [21] presented an approach based on SVM and Gavrilut et al [22] used the perceptron algorithm for classification.…”

Section: Related Workmentioning

confidence: 99%

“…Firdausi et al [20] and Rieck et al [21] provided a behavior based approach by collecting syscalls and building call graphs of the application. This would be a possible way for security tests if sufficient access to the SUT is available, e.g., during component tests.…”

Section: Related Workmentioning

confidence: 99%

Generic Approach for Security Error Detection Based on Learned System Behavior Models for Automated Security Tests

Schanes

Hubler

Fankhauser

et al. 2013

2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops

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Abstract-The increasing complexity of software and IT systems creates the necessity for research on technologies addressing current key security challenges. To meet security requirements in IT infrastructures, a security engineering process has to be established. One crucial factor contributing to a higher level of security is the reliable detection of security vulnerabilities during security tests. In the presented approach, we observe the behavior of the system under test and introduce machine learning methods based on derived behavior metrics. This is a generic method for different test targets which improves the accuracy of the security test result of an automated security testing approach. Reliable automated determination of security failures in security test results increases the security quality of the tested software and avoids costly manual validation.

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“…Many emulators provide a convenient user interface where the user can specify which characteristics of the file to identify and report. The research methodology in [11] combines the features extracted from dynamic analysis with machine learning (classification) techniques for effective and efficient malware detection.…”

Section: Behavior Based Malware Detectionmentioning

confidence: 99%

Clustering versus SVM for malware detection

Narra

Troia

Visaggio

et al. 2015

J Comput Virol Hack Tech

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Clustering versus SVM for Malware Detection by Usha NarraPrevious work has shown that we can effectively cluster certain classes of malware into their respective families. In this research, we extend this previous work to the problem of developing an automated malware detection system. We first compute clusters for a collection of malware families. Then we analyze the effectiveness of classifying new samples based on these existing clusters. We compare results obtained using -means and Expectation Maximization (EM) clustering to those obtained using Support Vector Machines (SVM). Using clustering, we are able to detect some malware families with an accuracy comparable to that of SVMs. One advantage of the clustering approach is that there is no need to retrain for new malware families.

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Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection

Cited by 259 publications

References 2 publications

Comparative Analysis of Feature Extraction Methods of Malware Detection

Comparative Analysis of Feature Extraction Methods of Malware Detection

Generic Approach for Security Error Detection Based on Learned System Behavior Models for Automated Security Tests

Clustering versus SVM for malware detection

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