Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Charles A. Kamhoua"'
Autor:
Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua
Publikováno v:
IEEE Access, Vol 12, Pp 169432-169441 (2024)
Due to the popularity of Android mobile devices over the past ten years, malicious Android applications have significantly increased. Systems utilizing machine learning techniques have been successfully applied for Android malware detection to counte
Externí odkaz:
https://doaj.org/article/9fa1039004f747c8a292c8b40fca3669
Autor:
Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua
Publikováno v:
IEEE Access, Vol 12, Pp 162685-162696 (2024)
Adversarial attacks pose significant threats to Android malware detection by undermining the effectiveness of machine learning-based systems. The rapid increase in Android apps complicates the management of malicious software that can compromise user
Externí odkaz:
https://doaj.org/article/6c702a9e703944e6b8adeb5b7dd9c758
Publikováno v:
IEEE Transactions on Network and Service Management. 19:3438-3452
Publikováno v:
2023 57th Annual Conference on Information Sciences and Systems (CISS).
Publikováno v:
IEEE Transactions on Network and Service Management. 19:112-129
Defensive deception techniques have emerged as a promising proactive defense mechanism to mislead an attacker and thereby achieve attack failure. However, most game-theoretic defensive deception approaches have assumed that players maintain consisten
Publikováno v:
2023 International Conference on Computing, Networking and Communications (ICNC).
Publikováno v:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference.
Publikováno v:
Game Theory and Machine Learning for Cyber Security
In this chapter, we introduce a cyber deception defense approach and propose a scalable allocation algorithm to place honeypots over an attack graph. We formulate a two‐person zero‐sum strategic game between the network defender and an attacker.
Publikováno v:
Game Theory and Machine Learning for Cyber Security. :1-19
Scalable Algorithms for Identifying Stealthy Attackers in a Game‐Theoretic Framework Using Deception
Autor:
Marcus Gutierrez, Anjon Basak, Sridhar Venkatesan, Christopher Kiekintveld, Ahmed H. Anwar, Charles A. Kamhoua
Publikováno v:
Game Theory and Machine Learning for Cyber Security
Identifying an attacker in as much detail as possible (e.g., what their goals, capabilities, and tactics are) can lead to better defensive strategies for the defender and may eventually help with the attribution of attacks. On the other hand, attacke