Network Intrusion Detection and Prevention Systems for Attacks in IoT Systems

Abstract

Internet of things (IoT) has transformed greatly the improved way of business through machine-to-machine (M2M) communications. This vast network and its associated technologies have opened the doors to an increasing number of security threats which are dangerous to IoT and 5G wireless networks. The first part of this chapter presents instruction detection system (IDS) which detect the various attacks in 6LoWPAN layer. An IDS is to detect and analyze both inbound and outbound network traffic for abnormal activities. An IPS complements an IDS configuration by proactively inspecting a system's incoming traffic to weed out malicious requests. A typical IPS configuration uses web application firewalls and traffic filtering solutions to secure applications. An IPS prevents attacks by dropping malicious packets, blocking offending IPs and alerting security personnel to potential threats. Machine learning (ML)-based instruction detection and prevention system (IDPS) is proposed and implemented in Contiki simulation environment.