A study on IDS for preventing Denial of Service attack using outliers techniques
作者： Ibrahim Salim M. , T. Abdul Razak
DOI: 10.1109/ICETECH.2016.7569352
关键词:
摘要: Denial of service attack permits the intruders to access network services thereby preventing legitimate users services. To overcome deficits DoS attack, it is very essential design an intrusion detection system. Intrusion system (IDS) software that operates as a security mechanism protect computer from attacks. With increasing number data being transmitted gradually one another, IDS identify intrusions in such large datasets effectively. Data mining efficient tool applied outline and prevent massive intruders. Outliers are patterns do not match well-defined notion normal behavior. Outlier aims find conform expected It widely used for developing cyber security. This paper presents study outlier technique how develop DOS attack.
sci-hub.se 参考文章(36)
Carla E. Brodley, Terran Lane, Sequence Matching and Learning in Anomaly Detection for Computer Security ,(1997)
Bavani Arunasalam, Pei Sun, Sanjay Chawla, Mining for outliers in sequential databases siam international conference on data mining. pp. 94- 105 ,(2006)
Wei Da, Han Shao Ting, Distributed Intrusion Detection Based on Outlier Mining Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering. pp. 343- 348 ,(2013) , 10.1007/978-3-642-31698-2_49
David Marchette, A Statistical Method for Profiling Network Traffic ID'99 Proceedings of the 1st conference on Workshop on Intrusion Detection and Network Monitoring - Volume 1. pp. 119- 128 ,(1999)
Vern Paxson, Bro: a system for detecting network intruders in real-time Computer Networks. ,vol. 31, pp. 2435- 2463 ,(1999) , 10.1016/S1389-1286(99)00112-7
Wei Lu, Hengjian Tong, Detecting Network Anomalies Using CUSUM and EM Clustering international symposium on advances in computation and intelligence. pp. 297- 308 ,(2009) , 10.1007/978-3-642-04843-2_32
Raymond T. Ng, Edwin M. Knorr, Finding Intensional Knowledge of Distance-Based Outliers very large data bases. pp. 211- 222 ,(1999)
Vikas Pareek, Aditi Mishra, Arpana Sharma, Rashmi Chauhan, Shruti Bansal, A Deviation Based Outlier Intrusion Detection System Recent Trends in Network Security and Applications. pp. 395- 401 ,(2010) , 10.1007/978-3-642-14478-3_39
Wenke Lee, S.J. Stolfo, K.W. Mok, A data mining framework for building intrusion detection models ieee symposium on security and privacy. pp. 120- 132 ,(1999) , 10.1109/SECPRI.1999.766909
Jimmy Xiangji Huang, Jun Miao, Ben He, High performance query expansion using adaptive co-training Information Processing and Management. ,vol. 49, pp. 441- 453 ,(2013) , 10.1016/J.IPM.2012.08.002