受人工免疫启发的脚本病毒检测模型
Artificial Immunity-Inspired Script-Virus Detection Model
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摘要: 为了检测日益增多的脚本病毒变种,提出了一种基于人工免疫原理的脚本病毒检测模型。该模型借鉴了人工免疫的自体耐受和克隆选择机制,将脚本代码提呈为抗原,用抗体模拟病毒检测环境下的检测器,按照免疫学习机制对抗体进行分类,模拟了否定选择算法,避免抗体识别正常抗原,利用抗体的自学习机制发现有害的变种抗原,通过对成熟抗体和记忆抗体进行演化,建立了抗体的动态产生与淘汰机制,从而降低误检率。仿真实验表明,该模型能有效检测脚本病毒变种,为脚本病毒的检测提供了一条新途径。Abstract: To detect increasing mutated script-virus, an artificial immunity-based script-virus detection model is presented. Self-tolerance and clone selection mechanism in artificial immune system are used for reference. Script-code is presented to antigen. Antibody simulates detector in the environment of virus detection. Antibody is classified according to immunity learning mechanism. The negative selection algorithm is simulated to avoid recognizing normal antigen. Self-learning of antibody is used to discover harmful mutated antigens. Mature and memory antibody are evolved to set up dynamic production and elimination mechanism of antibody to decrease false detection rate. The simulation experiment shows that proposed model is able to detect mutated script-virus and provides a new way to detect script-virus.