| With the continuous development of information networks, computer security problem has attracted widespread attention, including a computer virus is one of the biggest security threat. With the rapid development of computer virus technology, the traditional virus detection methods have been difficult to meet the threat, people began to study some intelligent virus detection technology to deal with all kinds of unknown malicious software, in which artificial immune system is one of the key areas of attention.According to detection of unknown malware problem, this paper proposes a combining negative and positive selection algorithm of the artificial immune algorithm. Based on negative selection algorithm with certain selection algorithm the basic idea, put forward a kind of immature detector first negative selection with the characteristics of the self-centered data source to a number of mature detectors, then this batch of mature detectors and no self-centered feature data sources are certainly choose to get a selection of mature detector method to improve the test efficiency of detector; At the same time, the cloning algorithm is applied to the improved artificial immune algorithm, the first after the resistance of negative and positive selection algorithm to get the mature detector variation of proliferation, then put these mutations mature detector as memory detector in immature detector set to tolerance, to get better mature detector. In this paper, the proposed method can more effectively improve the efficiency of detection.Improved artificial immune algorithm for the experimental certification, the experiment concluded that improved algorithm error detection rate is far lower than only using negative error detection rate of the algorithm. Through comparison the algorithm and negative selection algorithm show without reducing the rate of detection at the same time, the improved algorithm can effectively reduce the error detection rate of the detectors. |
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