Abstract
Excessive detectors, high time complexity, and loopholes are main problems which current negative selection algorithms have face and greatly limit the practical applications of negative selection algorithms. This paper proposes a real-valued negative selection algorithm based on clonal selection. Firstly, the algorithm analyzes the space distribution of the self set and gets the set of outlier selves and several classification clusters. Then, the algorithm considers centers of clusters as antigens, randomly generates initial immune cell population in the qualified range, and executes the clonal selection algorithm. Afterwards, the algorithm changes the limited range to continue the iteration until the non-self space coverage rate meets expectations. After the algorithm terminates, mature detector set and boundary self set are obtained. The main contributions lie in (1) introducing the clonal selection algorithm and randomly generating candidate detectors within the stratified limited ranges based on clustering centers of self set; generating big-radius candidate detectors first and making them cover space far from selves, which reduces the number of detectors; then generating small-radius candidate detectors and making them gradually cover boundary space between selves and non-selves, which reduces the number of holes; (2) distinguishing selves and dividing them into outlier selves, boundary selves, and internal selves, which can adapt to the interference of noise data from selves; (3) for anomaly detection, using mature detector set and boundary self set to test at the same time, which can effectively improve the detection rate and reduce the false alarm rate. Theoretical analysis and experimental results show that the algorithm has better time efficiency and detector generation quality according to classic negative selection algorithms.
Highlights
The negative selection algorithm (NSA) first proposed by American scholar Forrest [1] is one of the most important anomaly detection algorithms in artificial immune field
The main contributions lie in (1) introducing the clonal selection algorithm and randomly generating candidate detectors within the stratified limited ranges based on clustering centers of self set; generating big-radius candidate detectors first and making them cover space far from selves, which reduces the number of detectors; generating small-radius candidate detectors and making them gradually cover boundary space between selves and non-selves, which reduces the number of holes; (2) distinguishing selves and dividing them into outlier selves, boundary selves, and internal selves, which can adapt to the interference of noise data from selves; (3) for anomaly detection, using mature detector set and boundary self set to test at the same time, which can effectively improve the detection rate and reduce the false alarm rate
Theoretical analysis and experimental results show that the algorithm has better time efficiency and detector generation quality according to classic negative selection algorithms
Summary
The negative selection algorithm (NSA) first proposed by American scholar Forrest [1] is one of the most important anomaly detection algorithms in artificial immune field. The idea of negative selection algorithm comes from the negative selection behavior of T lymphocytes in immune tolerance of thymus [2]. An immune explanation for this behavior is as follows. In the thymus tolerance issue, T lymphocytes which identify self antigens will be in apoptosis or inactivated, and those cells which do not identify selves will mature after a period of tolerance and exercise their immune function in peripheral lymphoid tissues. The proposition of negative selection algorithm greatly promotes research and application in the anomaly detection field of artificial immune systems. The idea of negative selection algorithm is often applied in these areas such as fault detection, virus detection, network intrusion detection, and machine learning [2,3,4]
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