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» Detecting Anomalies in Graphs
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145
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TJS
2010
182views more  TJS 2010»
15 years 2 months ago
A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Yasser Yasami, Saadat Pour Mozaffari
144
Voted
RAID
2009
Springer
15 years 10 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 ...
109
Voted
INFOCOM
2008
IEEE
15 years 10 months ago
Detecting Anomalies Using End-to-End Path Measurements
—In this paper, we propose new “low-overhead” network monitoring techniques to detect violations of path-level QoS guarantees like end-to-end delay, loss, etc. Unlike existin...
K. V. M. Naidu, Debmalya Panigrahi, Rajeev Rastogi
134
Voted
ISSTA
2010
ACM
15 years 7 months ago
Learning from 6, 000 projects: lightweight cross-project anomaly detection
Real production code contains lots of knowledge—on the domain, on the architecture, and on the environment. How can we leverage this knowledge in new projects? Using a novel lig...
Natalie Gruska, Andrzej Wasylkowski, Andreas Zelle...
117
Voted
USS
2004
15 years 5 months ago
On Gray-Box Program Tracking for Anomaly Detection
Many host-based anomaly detection systems monitor a process ostensibly running a known program by observing the system calls the process makes. Numerous improvements to the precis...
Debin Gao, Michael K. Reiter, Dawn Xiaodong Song