IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Learning program behavior profiles for intrusion detection
ID'99 Proceedings of the 1st conference on Workshop on Intrusion Detection and Network Monitoring - Volume 1
Distributed multi-intelligent agent framework for detection of stealthy probes
Design and application of hybrid intelligent systems
Hybrid multi-agent framework for detection of stealthy probes
Applied Soft Computing
Detecting energy-greedy anomalies and mobile malware variants
Proceedings of the 6th international conference on Mobile systems, applications, and services
A Framework for Detecting Internet Applications
Information Networking. Towards Ubiquitous Networking and Services
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
An efficient SVM-Based method to detect malicious attacks for web servers
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
ADWICE – anomaly detection with real-time incremental clustering
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
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Intrusion detection is a critical component of secure information systems. This paper addresses the issue of identifying important input features in building an intrusion detection system (IDS). Since elimination of the insignificant and/or useless inputs leads to a simplification of the problem, faster and more accurate detection may result. Feature ranking and selection, therefore, is an important issue in intrusion detection.The important aspect of our technique is to identify key intrusion detection features that aid in achieving faster detection (real time detection) and higher accuracy rate (low false alarm rate).In this paper we rank the importance of input features using Neural networks by analyzing the detection accuracy, false positive rate and false negative rate. Results from the DARPA intrusion detection evaluation are provided. We also discuss our methodology for identifying important input features and provide the results obtained for five classes (normal, probe, denial of service, user to super-user and remote to local) for the DARPA dataset.