Blackhole Prevention Techniques Using Machine Learning

Abstract

Wireless Sensor Network (WSN) is a collection of tiny devices known as sensor nodes that are deployed in the sensing region of the geographical area. The other name of sensor nodes is motes. In the networking area, one sensor node acts as a sender and the destination mote acts as a receiver. Whenever data is transfer within the network then main focus is to maintain security of data. We cover all the main points and security requirements that are important to manage while transferring the data from one node to another node. Mainly, we focus on the DoS attacks that may occur on the network layer named Blackhole as well as discussed the proposed Machine Learning approaches to handle this attack. We cover the research from 2014 to 2020 onwards. This paper mainly focused on the Blackhole security attack; security is important at the node level as well as data recovery point of view when data is transfer from the source node to the destination node. Machine Learning is the process where the model is trained based on experience and past data. Moreover, WSNs are difficult to manage or design but network design is easy with the help of ML. The main aim of this paper is to cover the ML approaches to handle the Blackhole attack.