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» Intrusion Detection with Neural Networks
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CIBCB
2005
IEEE
14 years 1 months ago
Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence
— DNA microarray-based gene expression profiles have been established for a variety of adult cancers. This paper addresses application of an artificial neural network (ANN) wit...
Leif E. Peterson, Mustafa Ozen, Halime Erdem, Andr...
GECCO
2004
Springer
116views Optimization» more  GECCO 2004»
14 years 1 months ago
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
Abstract. In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models c...
Yaochu Jin, Bernhard Sendhoff
IJACTAICIT
2010
163views more  IJACTAICIT 2010»
13 years 5 months ago
Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance
In this work, a Modified Vector Field Histogram (MVFH) has been developed to improve path planning and obstacle avoidance for a wheeled driven mobile robot. It permits the detecti...
Bahaa I. Kazem, Ali H. Hamad, Mustafa M. Mozael
CCS
2007
ACM
14 years 2 days ago
Analyzing network traffic to detect self-decrypting exploit code
Remotely-launched software exploits are a common way for attackers to intrude into vulnerable computer systems. As detection techniques improve, remote exploitation techniques are...
Qinghua Zhang, Douglas S. Reeves, Peng Ning, S. Pu...
CONEXT
2007
ACM
13 years 10 months ago
Detecting worm variants using machine learning
Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until ...
Oliver Sharma, Mark Girolami, Joseph S. Sventek