Hidden Markov Model with Machine Learning-Based Black hole Attack Identification in Wireless Sensor Networks

Wireless Sensor Network (WSN) is lying to several attacks because the Black hole is problematic to distinguish and protect. Impostor internments and re-programs block the packets rather than transmitting and obtaining them at the base station (BS) in a black hole attack. Consequently, any data that arrives in the black hole area is taken and cannot reach the recipient, triggering high packet losses and decreasing network performance. To overcome this problem, a hidden Markov model with machine learning (HMML)-based black hole attack identification in WSN is introduced. This approach uses the Hidden Markov Model (HMM) to detect the blackhole node in the WSN. This model examines the shortest routes between the sender and the recipient to discover the black hole route. This model contains states as well as the observation sequence. Forward and received count and reply of non-adjacent nodes are used for verifying the shortest routes. Here, the supervised and unsupervised training machine learning methods decide for detecting black hole nodes.