N-gram-based detection of new malicious code

By Tony Abou-Assaleh, Nick Cercone, Vlado Keselj, Ray Sweidan. Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004., 2004

301 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/507a073882d6fd752de51449581ee0fc7d7aa758

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Citations in the corpus

Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (2018, background): “Although past research work on binary malware detection has explored the use of traditional learning algorithms on n-gram-based, system-call-based, or behavior-based features [1], [19], [21], [26], more recent work”
BitShred: feature hashing malware for scalable triage and semantic analysis (2011, methodology): “In our implementation, we experiment with using n-grams as proposed in [9, 25, 38] because the feature space is extremely large, and dynamic behavior analysis from Bayer et al.”
A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, background): “Although signatures cannot be directly used to discover new exploits [54], they can do so indirectly due to component overlap between malwares [55], [56].”
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey (2009, methodology): “In Abou-Assaleh et al. (2004) a new executable file was compared with the profiles of malicious and benign classes, and was assigned to the most similar.”
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].”
Deep neural network based malware detection using two dimensional binary program features (2015, methodology): “Numerous static features have been proposed for extracting features from binaries: printable strings [31], import tables [33], byte n-grams [4], opcodes [33], informational entropy [33].”
A Survey on Malware Detection Using Data Mining Techniques (2017, background): “In malware detection, SVMs also show good performance in discriminating malware from benign ﬁles [Abou-Assaleh et al. 2004; Wang et al. 2006b; Ye et al. 2009, 2009c; Firdausi et al. 2010; Anderson et al. 2012].”