计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 350-355.doi: 10.11896/jsjkx.210500031
魏辉, 陈泽茂, 张立强
WEI Hui, CHEN Ze-mao, ZHANG Li-qiang
摘要： 针对现有的基于系统调用的异常入侵检测方法使用单一轨迹模式无法准确反映进程行为的问题,基于系统调用轨迹的顺序和频率模式对进程行为进行建模,设计了一个数据驱动的异常检测框架。该框架可以同时检测系统调用轨迹的顺序异常和定量异常,借助组合窗口机制,通过满足离线训练和线上检测对提取轨迹信息的不同需求,可以实现离线细粒度学习和线上异常实时检测。在ADFA-LD入侵检测标准数据集上进行了针对未知异常检测性能的对比实验,结果表明,相比4类传统机器学习方法和4类深度学习方法,该框架的综合检测性能提高了10%左右。
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