Research Area:  Machine Learning
With the increasing penetration of security threats, the severity of their impact on the underlying network has increased manifold. Hence, a robust anomaly detection technique, Fuzzified Cuckoo based Clustering Technique (F-CBCT), is proposed in this paper which operates in two phases: training and detection. The training phase is supported using Decision Tree followed by an algorithm based on hybridization of Cuckoo Search Optimization and K-means clustering. In the designed algorithm, a multi-objective function based on Mean Square Error and Silhouette Index is employed to evaluate the two simultaneous distance functions namely-Classification measure and Anomaly detection measure. Once the system is trained, detection phase is initiated in which a fuzzy decisive approach is used to detect anomalies on the basis of input data and distance functions computed in the previous phase. Experimental results in terms of detection rate (96.86%), false positive rate (1.297%), accuracy (97.77%) and F-Measure (98.30%) prove the effectiveness of the proposed model.
Network Anomaly Detection
Author(s) Name:  SahilGarg and ShaliniBatra
Journal name:  Computers & Electrical Engineering
Publisher name:  ELSEVIER
Volume Information:  Volume 71, October 2018, Pages 798-817
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0045790617311175