Zahra Aref and Ayman Younis have been selected as recipients of IEEE Communications Society Phoenix ISS Awards for academic year 2021-2022. The IEEE Communications Society Phoenix ISS Award was established to encourage engineering student to participate in professional activities. Awards are to be given to full-time or part-time students to cover expenses for students to attend the International Switching Symposium, or other IEEE Communications Society Conferences.
Congratulations Zahra and Younis!
Bios and abstracts of their recent papers are below.
Zahra Aref is a Ph.D. candidate at WINLAB, Department of Electrical and Computer Engineering, Rutgers University, NJ, USA. She is advised by Prof. Narayan B. Mandayam. Zahra’s research is focused on cyber-security, deep reinforcement learning, and human decision-making models. She was awarded as the best TA in the Department of Electrical Engineering, Rutgers University, in Spring 2021. Zahra received her master’s degree in Electrical Engineering/Telecommunication from Isfahan University of Technology, Isfahan, Iran in 2014, and developed high-speed network switches on NetFPGA.
Abstract: Cloud storage is a target of advanced persistent threats (APTs), where a sophisticated adversary attempts to steal sensitive data in a continuous manner. Human monitoring and intervention are the integral part of the reinforcement learning (RL) approaches to defend against APTs. In this paper, prospect theory (PT) is used to model the subjective behavior of the cloud storage defender in assigning computing resources (processing units) to scan and monitor the cloud storage system against an APT attacker bot, which attempts to steal information from the cloud. Under a constraint on the total number of processing units and a lack of knowledge of the opponent’s resource allocation strategy, we study the defense performance of a federated maximum-likelihood deep Q-network (FMLDQ) RL algorithm against a sophisticated branching dueling deep Q-network (BDQ) RL attack algorithm. Specifically, the RL strategy for the defender is affected by subjective decisions in estimating the processing units of the attacker. Simulation results show that when the defender has more resources than the attacker, an EUT-based defense strategy (without human intervention) yields better data protection. On the other hand, when the defender has fewer resources, a PT based defense strategy (with human intervention) is better.
Ayman Younis is a Ph.D. candidate at the Cyber-Physical Systems Laboratory (CPS Lab), Department of Electrical and Computer Engineering, Rutgers University, NJ. He is advised by Prof. Dario Pompili. His research focuses on wireless communications and mobile cloud computing, with emphasis on software-defined testbeds. He received the Best Paper Award at the IEEE/IFIP Wireless On-demand Network Systems and Services Conference (WONS) in 2021.
Paper title:
QLRan: Latency-Quality Tradeoffs and Task Offloading in Multi-node Next Generation RANs
Abstract: Next-Generation Radio Access Network (NG-RAN) is an emerging paradigm that provides flexible distribution of cloud computing and radio capabilities at the edge of the wireless Radio Access Points (RAPs). Computation at the edge bridges the gap for roaming end users, enabling access to rich services and applications. In this paper, we propose a multi-edge node task offloading system, i.e., QLRan, a novel optimization solution for latency and quality tradeoff task allocation in NG-RANs. Considering constraints on service latency, quality loss, and edge capacity, the problem of joint task offloading, latency, and Quality Loss of Result (QLR) is formulated in order to minimize the User Equipment (UEs) task offloading utility, which is measured by a weighted sum of reductions in task completion time and QLR cost. The QLRan optimization problem is proved as a Mixed Integer Nonlinear Program (MINLP) problem, which is a NP-hard problem. To efficiently solve the QLRan optimization problem, we utilize Linear Programming (LP)-based approach that can be later solved by using convex optimization techniques. Additionally, a programmable NG-RAN testbed is presented where the Central Unit (CU), Distributed Unit (DU), and UE are virtualized using the OpenAirInterface (OAI) software platform to characterize the performance in terms of data input, memory usage, and average processing time with respect to QLR levels. Simulation results show that our algorithm performs significantly improves the network latency over different configurations.