Anomaly-based intrusion detection: privacy concerns and other problems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Robust design of server capability in M/M/1 queues with both partly random arrival and service rates
Computers and Operations Research
An empirical analysis of NATE: Network Analysis of Anomalous Traffic Events
Proceedings of the 2002 workshop on New security paradigms
Intrusion detection using an ensemble of intelligent paradigms
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Factor-analysis based anomaly detection and clustering
Decision Support Systems
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
DDoS attack detection method using cluster analysis
Expert Systems with Applications: An International Journal
DADICC: Intelligent system for anomaly detection in a combined cycle gas turbine plant
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
As the influence of the internet continues to expand as a medium for communications and commerce, the threat from spammers, system attackers, and criminal enterprises has grown accordingly. This paper proposes a random effects logistic regression model to predict anomaly detection. Unlike the previous studies on anomaly detection, a random effects model was applied, which accommodates not only the risk factors of the exposures but also the uncertainty not explained by such factors. The specific factors of the risk category such as retained 'protocol type' and 'logged in' are included in the proposed model. The research is based on a sample of 49,427 random observations for 42 variables of the KDD-cup 1999 (Data Mining and Knowledge Discovery competition) data set that contains 'normal' and 'anomaly' connections. The proposed model has a classification accuracy of 98.94% for the training data set, while that for the validation data set is 98.68%.