Hybrid Negative Selection Approach for Anomaly Detection
نویسندگان
چکیده
This paper describes b-v model which is enhanced version of the negative selection algorithm (NSA). In contrast to formerly presented approaches, binary and real-valued detectors are incorporated. The reason behind developing this hybrid is willingness to overcome the scalability problems, which are a key problem, when only one type of detectors is used. Although high-dimensional datasets are a great challenge for NSA, however, equally important are quality of generated detectors and duration of learning as well as classification stages. Thus, also there are discussed various versions of b-v model to increase its efficiency. Versatility of proposed approach was intensively tested by using popular testbeds concerning domains like computer’s security (intruders and spam detection), recognition of handwritten words, etc.
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