01854nas a2200241   4500000000100000000000100001008004100002260001200043653001000055653001500065653002200080653002400102653003300126653004000159100001500199700001600214245007000230856009800300300001000398490000600408520118400414022001401598       2017                            d  c12/201710aFuzzy10aClustering10aAnomaly Detection10aIntrusion Detection10aPrincipal Component Analysis10aRobust Spatial Kernel Fuzzy C-Means1 aB S Harish1 aS V A Kumar00aAnomaly based Intrusion Detection using Modified Fuzzy Clustering  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/05/ijimai20174_6_8_pdf_14933.pdf  a54-590 v43 aThis paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. As a result, Intrusion Detection System has become an indispensable component of computer security. The proposed method consists of three steps: Pre-Processing, Feature Selection and Clustering. In pre-processing step, the duplicate samples are eliminated from the sample set. Next, principal component analysis is adopted to select the most discriminative features. In clustering step, the network samples are clustered using Robust Spatial Kernel Fuzzy C-Means (RSKFCM) algorithm. RSKFCM is a variant of traditional Fuzzy C-Means which considers the neighbourhood membership information and uses kernel distance metric. To evaluate the proposed method, we conducted experiments on standard dataset and compared the results with state-of-the-art methods. We used cluster validity indices, accuracy and false positive rate as performance metrics. Experimental results inferred that, the proposed method achieves better results compared to other methods.  a1989-1660