An Intrusion Detection System for Fog Computing and IoT based Logistic Systems using a Smart Data Approach

Abstract

The Internet of Things (IoT) is widely used in advanced logistic systems. Safety and security of such systems are utmost important to guarantee the quality of their services. However, such systems are vulnerable to cyber-attacks. Development of lightweight anomaly based intrusion detection systems (IDS) is one of the key measures to tackle this problem. In this paper, we present a new distributed and lightweight IDS based on an Artificial Immune System (AIS). The IDS is distributed in a three-layered IoT structure including the cloud, fog and edge layers. In the cloud layer, the IDS clusters primary network traffic and trains its detectors. In the fog layer, we take advantage of a smart data concept to analyze the intrusion alerts. In the edge layer, we deploy our detectors in edge devices. Smart data is a very promising approach for enabling lightweight and efficient intrusion detection, providing a path for detection of silent attacks such as botnet attacks in IoT-based systems.