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» LIDS: Learning Intrusion Detection System
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INCDM
2010
Springer
159views Data Mining» more  INCDM 2010»
14 years 9 days ago
Semi-supervised Learning for False Alarm Reduction
Abstract. Intrusion Detection Systems (IDSs) which have been deployed in computer networks to detect a wide variety of attacks are suffering how to manage of a large number of tri...
Chien-Yi Chiu, Yuh-Jye Lee, Chien-Chung Chang, Wen...
HICSS
2003
IEEE
220views Biometrics» more  HICSS 2003»
14 years 24 days ago
Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks
This paper describes a novel approach using Hidden Markov Models (HMM) to detect complex Internet attacks. These attacks consist of several steps that may occur over an extended pe...
Dirk Ourston, Sara Matzner, William Stump, Bryan H...
KDD
2002
ACM
157views Data Mining» more  KDD 2002»
14 years 8 months ago
Learning nonstationary models of normal network traffic for detecting novel attacks
Traditional intrusion detection systems (IDS) detect attacks by comparing current behavior to signatures of known attacks. One main drawback is the inability of detecting new atta...
Matthew V. Mahoney, Philip K. Chan
KDD
1997
ACM
184views Data Mining» more  KDD 1997»
13 years 11 months ago
JAM: Java Agents for Meta-Learning over Distributed Databases
In this paper, we describe the JAM system, a distributed, scalable and portable agent-based data mining system that employs a general approach to scaling data mining applications ...
Salvatore J. Stolfo, Andreas L. Prodromidis, Shell...
RAID
2009
Springer
14 years 2 months ago
Protecting a Moving Target: Addressing Web Application Concept Drift
Because of the ad hoc nature of web applications, intrusion detection systems that leverage machine learning techniques are particularly well-suited for protecting websites. The re...
Federico Maggi, William K. Robertson, Christopher ...