Anomaly Detection in DNS Query Logs using Improved Binary Black Hole Optimization Algorithm
S. Suganya, Dr.Kathiresan V · International Journal of Engineering and Technology · 2017
Domain Name System (DNS) log information supplies a unique perspective on domain names usage by both legitimate users and anomaly users.More than analyzing DNS queries in traditional manner, this research work aims in design and development of binding approach based on Improved Binary Black Hole Optimization Algorithm IBBHOA for feature selection in SVM classifier.At first unremitting black hole optimization algorithm is portrayed.Next an improved binary black hole optimization algorithm is presented.After that binding approach based on IBBHOA for feature selection in SVM Classifier is carried out.SVM classifier is chosen for performing the classification task for characterizing DNS lookup behaviors by means of log-mining.DNS query logs are obtained from the dataset from various sources.Feature selection is performed and then the SVM classifier is used to classify anomaly behaviors, DNS failed requests, time taken for feature selection and time taken for classification are the performance metrics chosen for comparison.