Cognition based self-organizing maps (CSOM) for Intrusion detection in wireless networks

Abstract

Cognitive networks is the solution for the problems existing on the current networks. Users maintain integrity of the networks and user node activity monitoring is required for provision of security. Cognitive Networks discussed in this paper not only monitor user node activity but also take preventive measures if user node transactions are malicious. The intelligence in cognitive engine is realized using self organizing maps (CSOMs). Gaussian and Mexican Hat neighbor learning functions have been evaluated to realize CSOMs. Experimental study proves the efficiency of Gaussian Learning function is better for cognition engine. The cognition engine realized is evaluated for malicious node detection in dynamic networks. The proposed concept results in better Intrusion detection rate as compared to existing approaches.

Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio‐based internet of things

“…[36] develop self‐organising maps and linear vector quantisation learning‐based approaches that use to reconfigure transceivers based on the input parameters taken from the signal [39]. Some proposals [40] that rely on the concept of self‐organising map combined with the hierarchical neural network have been proposed.…”