SPbun/Concept-Drift-Malware-Detection

Concept-Drift-Malware-Detection

Papers focus on PE Malware

Carlin D, O’Kane P, Sezer S. A cost analysis of machine learning using dynamic runtime opcodes for malware detection[J]. Computers & Security, 2019, 85: 138-155.
Alsulami, Bander. Behavioral Malware Detection and Classification Using Windows Prefetch Files. Diss. Drexel University, 2019.
Suprem A. Concept Drift Detection and Adaptation with Weak Supervision on Streaming Unlabeled Data[J]. arXiv preprint arXiv:1910.01064, 2019.
Raff E, Fleming W, Zak R, et al. KiloGrams: Very Large N-Grams for Malware Classification[J]. arXiv preprint arXiv:1908.00200, 2019.
Anderson H S, Kharkar A, Filar B, et al. Learning to evade static PE machine learning malware models via reinforcement learning[J]. arXiv preprint arXiv:1801.08917, 2018.
Xue D, Li J, Lv T, et al. Malware Classification Using Probability Scoring and Machine Learning[J]. IEEE Access, 2019, 7: 91641-91656.
Lu S, Ying L, Lin W, et al. New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning[J]. arXiv preprint arXiv:1907.08356, 2019.
The Need for Speed An Analysis of Brazilian Malware Classifiers
Demertzis K, Tziritas N, Kikiras P, et al. The next generation cognitive security operations center: adaptive analytic lambda architecture for efficient defense against adversarial attacks[J]. Big Data and Cognitive Computing, 2019, 3(1): 6.
Jordaney R, Sharad K, Dash S K, et al. Transcend: Detecting concept drift in malware classification models[C]//26th {USENIX} Security Symposium ({USENIX} Security 17). 2017: 625-642.
Yang S. Trusted CI Webinar: Anticipatory Cyber Defense via Predictive Analytics, Machine Learning and Simulation[J]. 2019.

Papers focus on Android malware