ACM SIGKDD Explorations Newsletter
Change-Point Monitoring for the Detection of DoS Attacks
IEEE Transactions on Dependable and Secure Computing
Network intrusion detection in covariance feature space
Pattern Recognition
A Novel Covariance Matrix Based Approach for Detecting Network Anomalies
CNSR '08 Proceedings of the Communication Networks and Services Research Conference
A triangle area based nearest neighbors approach to intrusion detection
Pattern Recognition
Attribute Normalization in Network Intrusion Detection
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
Intrusion detection using GSAD model for HTTP traffic on web services
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
A two-tier system for web attack detection using linear discriminant method
ICICS'10 Proceedings of the 12th international conference on Information and communications security
Anomaly detection in IP networks
IEEE Transactions on Signal Processing
Hierarchical Kohonenen net for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Network intrusion and fault detection: a statistical anomaly approach
IEEE Communications Magazine
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In this paper, a Denial-of-Service (DoS) attack detection system is explored, where a multivariate correlation analysis technique based on Euclidean distance is applied for network traffic characterization and the principal of anomaly-based detection is employed in attack recognition. The effectiveness of the detection system is evaluated on the KDD Cup 99 dataset and the influence of data normalization on the performance of attack detection is analyzed in this paper as well. The evaluation results and comparisons prove that the detection system is effective in distinguishing DoS attack network traffic from legitimate network traffic and outperforms two state-of-the-art systems.