Abstract
Internet of things (IoT) is an emerging paradigm that integrates several technologies. IoT network constitutes of many interconnected devices that include various sensors, actu-ators, services and other communicable objects. The increasing demand for IoT and its services have created several security vulnerabilities. Conventional security approaches like intrusion detection systems are not up to the expectation to fulfil the security challenges of IoT networks, due to the conventional technologies used in them. This article presents a survey of intrusion detection and prevention system (IDPS), using state of art technologies, in the context of IoT security. IDPS constitutes of two parts: intrusion detection system and intrusion prevention system. An intrusion detection system (IDS) is used to detect and analyze both inbound and outbound network traffic for malicious activities. An intrusion prevention system (IPS) can be aligned with IDS by proactively inspecting a system’s incoming traffic to mitigate harmful requests. The alignment of IDS and IPS is known as intrusion detection and prevention systems (IDPS). The amalgamation of new technologies, like software-defined network (SDN), machine learning (ML), and manufacturer usage description (MUD), in IDPS is putting the security on the next level. In this study IDPS and its performance benefits are analyzed in the context of IoT security. This survey describes all these prominent technologies in detail and their integrated applications to complement IDPS in the IoT network. Future research directions and challenges of IoT security have been elaborated in the end.
Highlights
Internet revolutionizes our daily life and provides so many services that have become no more luxurious but the ultimate need for life
Our research focuses on software-defined network (SDN) based intrusion detection and prevention systems (IDPS) systems for Internet of things (IoT) security using Machine Learning (ML) and device profile based techniques like Manufacturer Usage Description (MUD)
There is a lot of survey work done on the intrusion detection systems for the IoT but to the best of our knowledge, no work has been done on IDPS for IoT devices using the device profile base techniques like MUD for comprehensive security for IoT
Summary
Internet revolutionizes our daily life and provides so many services that have become no more luxurious but the ultimate need for life. Painstaking research has been done on developing the new generation of IDPS systems based on emerging technologies like Software Defined Networking (SDN) and Machine Learning (ML). MUD is a developing concept to define IoT device behaviour for network communication [4] This automatically identifies the device and helps the security system to figure out the abnormal or malicious nodes within the network. The main application of using ML in SDN networks is the control of the entire network rather than just focusing on localized policy or certain rules [5] Such techniques show great potential for network traffic classification and solving prediction problems [6]. Our research focuses on SDN based IDPS systems for IoT security using ML and device profile based techniques like MUD.
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