A SURVEY ON PROMISING DATASETS AND RECENT MACHINE LEARNING APPROACHES FOR THE CLASSIFICATION OF ATTACKS IN INTERNET OF THINGS

Abstract

Securing Internet of Things (IoT) against attacks is a very interesting area of research. A cyberattack refers to as any form of malicious activity that targets IT systems, networks and/or  people with a view to gaining illegal access to systems and data they contain. Attacks are in various forms as found in computer systems, networks and the cyber space. The immense increment in the amount of internet applications and the appearance of modern networks has created the need for improved security mechanisms. A good example of such modern technology is Internet of Things (IoTs). An IoT is a system that uses the Internet to facilitate communication between sensors and devices. Several approaches have been used to build attacks detection system in the past. The approaches for classifying attacks have been categorised as signature-based and Machine learning based. However, ML techniques have been argued to be more efficient for the identification of attacks or intrusions when compared to signature-based approaches. This study sourced for relevant literature from notable repositories and then surveyed some of the recent datasets that are very promising for ML-based studies in attack classification in IoT environments. The study equally provided a survey of evolving ML-based techniques for the classification of attacks in IoT networks. The study provided clear directions to researchers working in this area of researches by making the necessary information available more easily for the researcher to go about achieving improved ML-based approaches in this area.