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» Negative selection algorithms without generating detectors
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GECCO
2004
Springer
13 years 11 months ago
An Investigation of R-Chunk Detector Generation on Higher Alphabets
Abstract. We propose an algorithm for generating all possible generatable r-chunk detectors, which do not cover any elements in self set S. In addition, the algorithm data structur...
Thomas Stibor, Kpatcha M. Bayarou, Claudia Eckert
GECCO
2005
Springer
126views Optimization» more  GECCO 2005»
13 years 11 months ago
Is negative selection appropriate for anomaly detection?
Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algori...
Thomas Stibor, Philipp H. Mohr, Jonathan Timmis, C...
GECCO
2008
Springer
206views Optimization» more  GECCO 2008»
13 years 6 months ago
Improving accuracy of immune-inspired malware detectors by using intelligent features
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...
PCM
2004
Springer
106views Multimedia» more  PCM 2004»
13 years 11 months ago
An Immunological Approach to Raising Alarms in Video Surveillance
Inspired by the human immune system, and in particular the negative selection algorithm, we propose a learning mechanism that enables the detection of abnormal activities. Three ty...
Lukman Sasmita, Wanquan Liu, Svetha Venkatesh
GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
13 years 11 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai