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Cybersecurity is rapidly becoming one of the most important industries in the world, in regards to the national, financial, and environmental well-being of every nation. There are currently about half a million cyber attacks every minute, and the attacks will continue to increase in complexity and frequency as hackers adapt their strategies to the ever-changing cyber physical landscape. It is critical to train and educate the future workforce on the fundamental aspects of cyber and mobile security, and to improve their ability to identify, prevent, and respond to emerging threats. The purpose of this paper is to discuss the development of a collection of cybersecurity labs - called Eureka Experiences - designed to teach sophisticated concepts in an engaging, efficient, and affordable virtualization environment. This presentation will also address the future research and development of with these labs, and propose possible strategies for adapting them to a wide range of learners. 

Cooperative jamming is deemed as a promising physical layer based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macro base stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users. In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marco user (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of a local user (LU) in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seeks the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. Numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet. 

Smart City is a key component in Internet of Things (IoTs), so it has attracted much attention. The emergence of Mobile Crowd Sensing (MCS) systems enables many smart city applications. In an MCS system, sensing tasks are allocated to a number of mobile users. As a result, the sensing related context of each mobile user plays a significant role on service quality. However, some important sensing context is ignored in the literature. This motivates us to propose a Context-aware Multi-Armed Bandit (C-MAB) incentive mechanism to facilitate quality-based worker selection in an MCS system. We evaluate a worker’s service quality by its context (i.e., extrinsic ability and intrinsic ability) and cost. Based on our proposed C-MAB incentive mechanism and quality evaluation design, we develop a Modified Thompson Sampling Worker Selection (MTS-WS) algorithm to select workers in a reinforcement learning manner. MTS-WS is able to choose effective workers because it can maintain accurate worker quality information by updating evaluation parameters according to the status of task accomplishment. We theoretically prove that our C-MAB incentive mechanism is selection efficient, computationally efficient, individually rational, and truthful. Finally, we evaluate our MTS-WS algorithm on simulated and real-world datasets in comparison with some other classic algorithms. Our evaluation results demonstrate that MTS-WS achieves the highest cumulative utility of the requester and social welfare.