基于小波分析的无线传感网实时异常检测算法

发布时间：2018-03-31 11:55

本文选题：无线传感器网络　切入点：安全　出处：《南京师大学报(自然科学版)》2014年01期

【摘要】：异常检测技术能够检测到未知攻击,对于保障无线传感器网络安全具有重要意义.当前的异常检测技术实时性差,误报率高且计算量大,因此,无法直接应用在无线传感器网络中.鉴于此,提出基于小波分析的实时无线传感网异常检测(Wavelet Analysis-Based Real-time Anomaly Detection,WARAD)算法.在整个检测过程中,WARAD算法采用了逆向获取实时网络流量,然后通过对小尺度区间使用小波系数方差法计算Hurst值,从而提高异常检测的实时性、准确率,并降低求解Hurst值的运算复杂度.最后,在MeshIDE平台上实现了基于WARAD算法的异常检测系统,实验结果表明此算法极大地提高了无线传感网环境下异常检测的实时性,并降低了异常检测的误报率和漏报率.
[Abstract]:Anomaly detection technology can detect unknown attacks, which is of great significance to ensure the security of wireless sensor networks. The current anomaly detection technology has poor real-time performance, high false alarm rate and large amount of calculation. It can not be directly applied in wireless sensor networks. In view of this, a wavelet Analysis-Based Real-time Anomaly detection algorithm based on wavelet analysis is proposed. In the whole detection process, the algorithm uses reverse acquisition of real-time network traffic. Then the wavelet coefficient variance method is used to calculate the Hurst value in the small scale interval, which improves the real-time and accuracy of anomaly detection, and reduces the computational complexity of solving the Hurst value. Finally, An anomaly detection system based on WARAD algorithm is implemented on MeshIDE platform. The experimental results show that the algorithm can greatly improve the real-time performance of anomaly detection in wireless sensor network environment, and reduce the false alarm rate and false alarm rate of anomaly detection.
【作者单位】：江苏大学计算机科学与通信工程学院;
【基金】：国家自然科学基金(61202474、61103195) 江苏省自然科学基金(BK20130528) 江苏大学高级专业人才科研启动基金项目(12JDG049) 江苏大学本科生创新计划项目(2012075)
【分类号】：TP212.9;TN915.08

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