The term intrusion refers to a series of behaviours that exposes computer networks and systems' security to compromises. Corrective action on the network cannot go on without intrusion detection. IDS and IDS is the framework used to detect network traffic intrusions, which is how the network control mechanism identifies potential intrusions. Security breaches are designed to undermine one or more of the network's three primary security goals: privacy, availability, and trust. To get access to a system, an attacker must follow a predetermined set of procedures. Once inside, they can begin gathering data such as the protocol being used and the network resources available. There are many ways for a hacker to find out what systems are available on the network and how vulnerable they are to attacks. The rapid advancement of network technology necessitated IDS to focus on the detection of assaults using contextual analysis from signature matching processes. Using machine learning to detect and prevent intrusions, the IDS is a critical part of protecting data systems. Network intrusion detection is the focus of this paper, which examines and shows various machine learning techniques.

The Internet of Things (IoT) can simply be referred to as the network of things comprising software, sensors, electronics, allowing data to be collected and transmitted. The next step in the field of technology is the Internet of Things, bringing tremendous improvements to manufacturing, medicine, environmental treatment, and urban growth. In shaping this vision, multiple challenges need to be faced, such as technology interoperability problems, protection and data confidentiality standards and, last but not least, the implementation of energy efficient management systems. These devices with minimal human interference are capable of producing, sharing and consuming data. The networking of related as well as heterogeneous devices is often known to be IoT. The Internet of Things allows things to connect and interact with each other, thus minimizing human involvement in simple daily tasks. Addressing protection at all times or at any position for many users, companies, governments, and enterprises is really necessary and responsive. In this paper a secure IOT architecture for routing in a network with RPL Rapid Node Link Routing (RNLR) Model is proposed that performs traffic management and traffic analysis for secure communication using IoT routing protocol. It mainly aims to locate the malicious users in a IOT routing protocols. the proposed mechanism is compared with the state of the art work and compared results shows the proposed work performs well.