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

By Ivan Firdausi, Charles Lim, Alva Erwin, A. Nugroho. 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2010

311 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4e45a3f474bdb4b8263e3027bd0bf3197d920a9d

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Citations in the corpus

Malware Analysis and Classification: A Survey (2014, methodology): “[46] presented a proof of concept of a malware detection method.”
Review: machine learning techniques applied to cybersecurity (2019, methodology): “Apart from Naive Bayes and SVM, other machine learning methods have been applied such as decision trees, successfully applied to malware detection in [155], where out-performed SVM, Naive Bayes and MLP, as well as in [21, 156–159].”
A Survey on Malware Detection Using Data Mining Techniques (2017, methodology): “This is often the case for the high dimensional datasets in malware classiﬁcation [Firdausi et al. 2010; Santos et al. 2013].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “One of the best and effective considered practice is to use automated machine learning techniques to detect new and previously unseen cyber threats [13].”
Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, methodology): “In [28], Multilayer Perceptron Neural Networks are used for detecting malware.”
Neural Nets Can Learn Function Type Signatures From Binaries (2017, background): “Such tasks include malware classification [42, 5, 30, 20, 36, 15] and function identification [37, 7, 43].”