A Scalable and Energy-Efficient Anomaly Detection Scheme in Wireless SDN-Based mMTC Networks for IoT

As a typical Internet-of-Things (IoT) scenario, massive machine-type communications (mMTC) services are expected to grow exponentially and create a multibillion-dollar industry spanning a broad range of vertical sectors. In literature, wireless software-defined network (SDN) is viewed as a promising approach to facilitate the degree of reconfigurability on extended sets of mMTC devices via centralized software updates. However, most of the current anomaly detection scheme (ADS) in SDN suffers from the high risk of overwhelming of the controller as well as excessive energy consumption if directly applied in the network with enormous devices. To address the scalability issues in centralized ADS, we propose a localized ADS scheme, called scalable and energy-efficient anomaly detection scheme (SEE-ADS), comprising of a detection activation module, a lightweight predetection module, a heavyweight anomaly detection module, and a dynamic strategy selection module. Through the cooperation among these modules, the proposed ADS is capable of detecting attacks dynamically and effectively without the risk of energy depletion via discontinuous activation of the heavyweight detection. The lower complexity is fulfilled by developing a localized and adaptive heavyweight detection module, called a localized evolving semisupervised learning-based anomaly detection scheme (LESLA). Besides, the proposed scheme makes full use of feedback from the previous heavyweight activation and the indication of predetection on each packet. The simulation results show that the proposed scheme greatly reduces the overall energy consumption over heavyweight detection. Furthermore, the proposed scheme shows higher sensitivity on abnormal packets and similar false alarm compared with the literature work.