Discrete-time signal processing
Discrete-time signal processing
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Practical automated detection of stealthy portscans
Journal of Computer Security
How to Own the Internet in Your Spare Time
Proceedings of the 11th USENIX Security Symposium
IEEE Security and Privacy
Monitoring and early warning for internet worms
Proceedings of the 10th ACM conference on Computer and communications security
Worm Detection, Early Warning and Response Based on Local Victim Information
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
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In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms instead of counting the number of suspicious connections or packets from a monitored network. Its low complexity allows it to be used on any network-based intrusion detection system as a real-time detection module for high-speed networks. Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate.