无线传感网络中大数据访问安全等级盲检测

Blind Detection of Big Data Access Security Level in Wireless Sensor Networks

大数据具有复杂性和开放性的特点,使得在无线传感网络中进行大数据访问时存在较高的安全风险。为此,提出无线传感网络中大数据访问安全等级盲检测方法。提取大数据访问特征后对特征序列进行重构,利用低通滤波器对特征实施去噪处理。建立大数据访问特征的自相关矩阵关联,引入盲检测流程,提出用户访问安全等级信任值度量算法,确定大数据访问安全等级。实验结果表明,在不当访问行为数量为2000个时,所提方法的检测时间为0.101 s,能够提高检测效率和准确性,且抗干扰能力强。

The complexity and openness of big data make Big data access in wireless sensor networks a high security risk.Therefore,a blind detection method of big data access security level in wireless sensor networks is proposed.After extracting big data access features,the feature sequence is reconstructed,and low-pass filter is used to denoise the features.Then the main tasks include establishing the autocorrelation matrix association of Big data access features,introducing the blind detection process,proposing the user access security level trust value measurement algorithm,as well as determining the Big data access security level.The experimental results show that when the number of improper access behaviors is 2000,the detection time of the proposed method is 0.101 s,which can improve detection efficiency and accuracy,and has strong anti-interference ability.