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Trust Optimization for Byzantine Attacks in Cognitive Networks


Natasha Saini*, Nitin Pandey and Ajeet Pal Singh  


This research mainly focuses trust as an intrinsic factor in cognitive networks. The attacker model for this paper is structured using an intervention attribute, which explicates the possible implication an intrusion can have on an individual node in a network also trust is taken into consideration. The study will also incorporate security mechanisms into cognitive network fundamentals. The proposed solution includes elements of the Byzantine Failure Model, an integer-programming model and a Python decision tree algorithm. The Byzantine model is a system attribute that ensures it can detect elements within it that have been compromised to give misleading output. The use of Python machine learning algorithms is to ensure that security parameters are fluid and can preempt attacks before they happen. Trust is taken into consideration, which focuses on various attributes of information security, and risk can be minimized. The algorithm can be used to provide some lightweight security in the world and risk is minimized for various applications. To the best of our knowledge our work is first of its kind providing ingrained security feature in Cognitive networks.


Cognitive Network, Cognitive Radio Network, Network security


Amity Institute of Information &Technology, Noida, Amity Institute of Information &Technology, Noida, Raj Kumar Goel Institute of Technology, Ghaziabad

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