Analysis on Intrusion Detection Systems to Secure IoT Networks

The amalgamation of physical objects and the internet constitutes the Internet of things(IoT). In the subsequent times, more such physical objects will be connected to the internet and this will lead to the escalation in the attacks carried out on IoT devices and network. This is mainly due to the less memory and lack in built-in security controls. This has made IoT Security, the most researched topic in the past decade. Thus to protect IoT nodes and networks, a security mechanism called Intrusion Detection System(IDS) is brought in use.The advent of Internet of things has played a crucial role in making the lives of the people more easier and comfortable. It has improved the lives of the people in terms of several aspects like comfort and efficiency. This has overall consequently lead to what is called smart environments. When the case of real world smart environment is discussed, both security and privacy comes into play in being the key issues to be considered. But the need demands for the Intrusion Detection Systems (IDSs) to be designed for the IoT networks or environments to mitigate attack related issues. These are the attacks that exploits the vulnerabilities present in the network. The work here demonstrates the need of the fact that despite having previous technologies, we still lack a secure system that could safeguard IoT networks. In this paper, a detailed analysis is provided on IoT, security attacks in IoT, IDS, types of IDS, Machine learning and Deep learning techniques and algorithms used with IDS in IoT and current trends and research on use of IDS in securing Internet of Things is discussed. Lastly future research based on the current analysis is also examined. Keywords: IoT, IDS, Network, Security, Machine Learning, Deep Learning