Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning-based game-theoretical approach

As mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)-based column generation (CG-ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.