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.
The growth of wireless communication technologies and its applications leads to many security issues. Malicious node detection is one among the major security issues. Adoption of cognition can detect and Prevent malicious activities in the wireless networks. To achieve cognition into wireless networks, we are using reinforcement learning techniques. By using the existing reinforcement techniques, we have proposed GreedyQ cognitive (GQC) and SoftSARSA cognitive (SSC) algorithms for malicious node detection and the performances among these algorithms are evaluated and the result shows SSC algorithm is best algorithm. The proposed algorithms perform better in malicious node detection as compared to the existing algorithms.
The current day networks use Proactive networks for adaption to the dynamic scenarios. The use of cognition technique based on the Observe, Orient, Decide and Act loop (OODA) is proposed to construct proactive networks. The network performance degradation in knowledge acquisition and malicious node presence is a problem that exists. The use of continuous time dynamic neural network is considered to achieve cognition. The variance in service rates of user nodes is used to detect malicious activity in heterogeneous networks. The improved malicious node detection rates are proved through the experimental results presented in this paper.
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