Biblio
Filters: Author is Mumtaz, Shahid [Clear All Filters]
MT-MTD: Muti-Training based Moving Target Defense Trojaning Attack in Edged-AI network. ICC 2021 - IEEE International Conference on Communications. :1—6.
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2021. The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.
Resource Allocation and Throughput Maximization in Decoupled 5G. 2020 IEEE Wireless Communications and Networking Conference (WCNC). :1–6.
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2020. Traditional downlink (DL)-uplink (UL) coupled cell association scheme is suboptimal solution for user association as most of the users are associated to a high powered macro base station (MBS) compared to low powered small base station (SBS) in heterogeneous network. This brings challenges like multiple interference issues, imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate DL-UL decoupled cell association scheme to address these challenges and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into a class of mixed integer non linear programming (MINLP) problem which is NP-hard and requires an exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA), with less complexity, is proposed as a solution to find near optimal solution. Extensive simulations work have been done to evaluate proposed algorithm. Results show effectiveness of proposed novel decoupled cell association scheme over traditional coupled cell association scheme in terms of users associated/attached, mitigating interference, traffic offloading to address traffic imbalances and sum-rate maximization.