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Summary: The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have many micro devices that are easy to capture. In node capture attacks, the adversary physically captures sensors and extracts all information including key information from their memories, trying to compromise the system's security protection. However, the robust and random nature of many WSN security designs makes it difficult to compromise the system even with the capture of some sensors. In this paper, we approach WSN security from an adversarial point of view and investigate low-cost and efficient algorithms to identify sensors in a WSN in the shortest time with the lowest cost. Instead of randomly capturing sensors, an intelligent attacker should choose the next target based on the known topology so far. Because the identification of such has been proven to be NP-hard (non-deterministic polynomial-time hard), we propose to transform the problem into a set covering problem and develop a greedy minimum cost node capture attack algorithm (MCA) to lower cost of attack. Extensive simulations have been implemented to evaluate the performance of MCA and to compare it with several related schemes. It is shown that MCA lowers the cost of compromising WSNs by 16%. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]