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Thangavel, M.
- An Effective Anomaly Intrusion Detection Using Statistical Change Point Detection
Authors
1 Department of Computer Applications at Erode Sengunthar Engineering College, Erode, Tamilnadu, IN
2 School of Computer Technology and Applications, Kongu Engineering College, Erode, Tamilnadu, IN
Source
Networking and Communication Engineering, Vol 2, No 9 (2010), Pagination: 301-307Abstract
Understanding the nature of intrusion attacks is critically important to the development of effective counter measures to anomaly traffic detection problem. Anomaly intrusion traffic attacks combined with traditional network intruders became most serious threats to network security. The existing work monitors available traffic attacks and take appropriate action to mitigate them, before they have had much time to propagate across the network. The proposed working model of statistical traffic anomaly detection method is carried out on the principle traces of non intrusive packet header data with quick detection rate. Traffic is monitored at regular intervals to obtain a signal that can be analyzed through statistical techniques and compared to historical norms to detect anomalies (change detection). The proposed methodology of anomaly intrusion traffic detection envisions statistical change detection theory for real-time data source extracted from Net Con server (Internet Service Provider popularly running at Erode Region). The experimental results suggest little use of address spoofing by attackers, which imply that such attacks will be invisible to indirect backscatter measurement techniques. The proposed traffic anomaly intrusion detection provides an improvement of 12% average through put compared to the existing ones. The propagation delay metric shows a reduction of nearly 9% with other methods of anomaly intrusion detection.
Keywords
Statistical Anomaly Detection, Network Traffic, Intrusion Detection.
- An Enhanced Method for Reducing the Computational Complexity of Distributed Denial of Service using LAMSTAR Principal Component Analysis
Authors
1 Department of Computer Applications, Erode Sengunthar Engineering College, Perundurai, Erode – 638 057, Tamilnadu, IN
2 Department of Computer Science, Erode Arts & Science College, Erode – 638 009, Tamilnadu, IN
Source
Networking and Communication Engineering, Vol 11, No 12 (2019), Pagination: 200-205Abstract
The security of computer networks plays a strategic role in modern computer systems. Distributed Denial of Services (DDOS) act as the ‘‘second line of defense’’ placed inside a protected network and looking for known or potential threats in network traffic and/or audit data recorded by hosts. The system is developed a Distributed Denial of Service attacks detection method using LAMSTAR neural network to classify observed system activities and compared the performance of LAMSTAR method. LAMSAR method gives better performance at the cost of testing time high Computational complexity and Training time than the other classification techniques. The system is further reduced the dimension of the data using principal component analysis by reducing the Computational Complexity of LAMSTAR DDOS which in turn reduces the testing and training time with almost the same performance.