An Efficient Intelligent Intrusion Detection System for Internet of Things

The Internet of things (IoT) allows billions of computer and networking devices to be interconnected. Digital entities such as sensors, Radio-frequency identification (RFID), Internet and localization technologies enable everyday objects to be transformed into intelligent objects, which can communicate with one another. The built-in sensors in intelligent objects track and collect various types of data about equipment, the environment and human social life. Although the IoT is useful, safety susceptibility is a major concern. There are universal and continuous interconnections between people, devices, sensors and services. Even if the security system is well built, smartly configured, implemented effectively and managed correctly, it must be human-made and are not resistant to security threats. Therefore, the conception of cyber security solutions requires a human element. To fight attacks, this paper presents an Intrusion Detection System (IDS) based on Smart Techniques such as k-nearest neighbors (KNN) and Random Forest (RF) algorithms which take data as input for spotting harmful activities. The methodology of IDS was created for identifying malicious activities of network traffic utilizing a set of features. Designing efficient IDS involves a data source that incorporates a collection of attributes for evaluating its performance while identifying normal and attack instances. This is one of the challenges that we strive to address in this study. Based on KNN and RF algorithms has presented a collection of features for spotting harmful activities that take advantage of IoT applications to utilize these procedures. The data sources are taken from the UNSW-NB15 source files.