Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm
Laisen Nie, Wentao Sun, Shupeng Wang, Zhaolong Ning, Joel J. P. C. Rodrigues, Yixuan Wu, Shengtao Li
Abstract
Internet of Things (IoT) is an expanded application of Internet, which are used to provide various services for users. Up to now, IoTs have received more attention, because it can provide ubiquitous connectivity for various devices. With the development of IoTs, its security has become a main issue. Attackers use various techniques to implement cyber attacks for the IoT, which threats the users' privacy seriously. As a security mechanism, intrusion detection techniques can detect various illicit behaviors before attackers invade the network. An intrusion detection system can implement effective defense functions to keep the network away from attacks. This paper proposes an intrusion detection algorithm based on deep reinforcement learning, which pursued the trends of traffic flows by extracting statistical features of prior network traffic for traffic prediction at first. Then, we use traffic predictors to employ intrusion detection. The evaluations verify the effectiveness of our algorithm in detecting Distributed Denial of Service attacks (DDoS).