Computer Virus Detection Based on Clonal Selection Algorithm

Aiming at the challenges such as the difficulty in identifying new variant viruses in computer virus detection, the slow convergence speed of traditional algorithms, and the limited edge computing resources, a new virus detection paradigm integrating quantum computing and improved CSA is proposed. Firstly, the global search ability of the algorithm is enhanced through dynamic affinity calculation and a two-stage clone amplification strategy. The quantum superposition state mutation operator and the quantum tunneling effect adaptive mutation rate adjustment mechanism are introduced to break through the local optimal bottleneck of the classical algorithm in the high-dimensional solution space. Secondly, the storage structure of the immune memory bank is optimized by utilizing the characteristics of quantum entanglement to achieve parallel retrieval and distributed update of the historical optimal solution, reducing the memory occupation on edge devices to 1/5 of the traditional method. Experiments show that although CAS is slightly inferior to the CNN algorithm in terms of detection accuracy rate, false alarm rate and missed alarm rate, it has certain advantages in detection speed and is significantly better than FCM. The research results have broken through the limitations of traditional detection technologies and achieved effective detection of unknown viruses.