Nhan Nguyen-Thanh | Searcharxiv

arXiv2019

Recommendation System-based Upper Confidence Bound for Online Advertising

In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the product recommendation in the online advertising. Through extensive testing with RecoGym, an OpenAI Gym-based reinforcement learning environment for the product recommendation in online advertising, the proposed method outperforms the widespread reinforcement learning schemes such as $ε$-Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3).

cs.IR↗

arXiv2016

Mitigating selfish primary user emulation attacks in multi-channel cognitive radio networks: A surveillance game

Primary User Emulation Attack (PUEA), in which attackers emulate primary user signals causing restriction of secondary access on the attacked channels, is a serious security problem in Cognitive Radio Networks (CRNs). An user performing a PUEA for selfishly occupying more channels is called a selfish PUEA attacker. Network managers could adopt a surveillance process on disallowed channels for identifying illegal channel occupation of selfish PUEA attackers and hence mitigating selfish PUEA. Determining surveillance strategies, particularly in multi-channel context, is necessary for ensuring network operation fairness. In this paper, we formulate a game, called multi-channel surveillance game, between the selfish attack and the surveillance process in multi-channel CRNs. The sequence-form representation method is adopted to determine the Nash Equilibrium (NE) of the game. We show that performing the obtained NE surveillance strategy significantly mitigates selfish PUEA.

cs.GT↗