Statistical Methods in Malware Mitigation
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 6360
Special Issue Editors
Interests: machine learning; malware analysis; software testing; social network analysis; graph-based clustering
Special Issue Information
Dear Colleagues,
“Antivirus is dead”, as Brian Dye (senior vice president of Symantec) said in 2014. Malware grows exponentially, and the current detection techniques can hardly be scaled up to match the requirements they are supposed to meet. The introduction of machine learning and other statistical methods during the last decade has been a particularly disruptive step in fighting this arms race, but adversarial machine learning has proved that we are still far away from making a significant contribution to mitigating malware for good. Nevertheless, strengthening statistical models and making them robust can be the piece of the puzzle that we have been missing to detect the so-called “invariant” that will lead us to distinguish between malicious and legitimate software.
This Special Issue focuses on a compendium of modern statistical techniques that aim to find the next disruptive step in the malware detection and classification arms race. We aim to understand the perspective of both the attacker and the defender. Our different techniques include machine learning, bio-inspired algorithms, Markov and Monte Carlo methods, information theory-based approaches, and also adversarial techniques to either evade detecting or measuring the robustness of malware detection and classification techniques. We also cover different application scenarios apart from desktop malware, such as the Internet of Things and network security.
Dr. Héctor D. Menéndez
Prof. Dr. Guillermo Suárez-Tangil
Guest Editors
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Keywords
- Machine learning for malware mitigation
- Markov and Monte Carlo methods for Malware mitigation
- Bio-inspired methods for malware mitigation
- Information theory-based methods for malware mitigation
- Adversarial techniques and malware mitigation
- Malware mitigation and android
- Malware mitigation methods in IoT environments
- Malware mitigation methods in network security
- Adversarial techniques in different security contexts
- The security of machine learning
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