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» Utilizing Neural Networks For Effective Intrusion Detection
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ESEM
2007
ACM
13 years 11 months ago
The Effects of Over and Under Sampling on Fault-prone Module Detection
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which ar...
Yasutaka Kamei, Akito Monden, Shinsuke Matsumoto, ...
CN
2007
168views more  CN 2007»
13 years 7 months ago
Network anomaly detection with incomplete audit data
With the ever increasing deployment and usage of gigabit networks, traditional network anomaly detection based Intrusion Detection Systems (IDS) have not scaled accordingly. Most,...
Animesh Patcha, Jung-Min Park
ACSAC
2007
IEEE
14 years 1 months ago
Improving Signature Testing through Dynamic Data Flow Analysis
The effectiveness and precision of network-based intrusion detection signatures can be evaluated either by direct analysis of the signatures (if they are available) or by using bl...
Christopher Kruegel, Davide Balzarotti, William K....
ACSAC
2005
IEEE
14 years 1 months ago
Verify Results of Network Intrusion Alerts Using Lightweight Protocol Analysis
We propose a method to verify the result of attacks detected by signature-based network intrusion detection systems using lightweight protocol analysis. The observation is that ne...
Jingmin Zhou, Adam J. Carlson, Matt Bishop
ACSAC
2003
IEEE
13 years 11 months ago
A Stateful Intrusion Detection System for World-Wide Web Servers
Web servers are ubiquitous, remotely accessible, and often misconfigured. In addition, custom web-based applications may introduce vulnerabilities that are overlooked even by the ...
Giovanni Vigna, William K. Robertson, Vishal Kher,...