Abstract
The soft computing methods play a vital role in identifying the malicious activities in the social network. The low cost solutions and the robustness provided by the soft computing in the identifying the unwanted activities make it a predominant area of research. The paper combines the soft computing techniques and frames an enhanced soft computing approach to detect the intrusion that cause security issues in the social network. The proffered method of the paper employs the enhanced soft computing technique that combines the fuzzy logic, decision tree, K means -EM and the machine learning in preprocessing, feature reduction, clustering and classification respectively to develop a security approach that is more effective than the traditional computations in identifying the misuse in the social networks. The intrusion detection system developed using the soft computing approach is tested using the KDD-NSL and the DARPA dataset to note down the security percentage, time utilization, cost and compared with the other traditional methods.
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