Security is a nonfunctional information system attribute that plays a crucial role in wide sensor network application domains. Security risk can be quantified as the combination of the probability that a sensor networ...Security is a nonfunctional information system attribute that plays a crucial role in wide sensor network application domains. Security risk can be quantified as the combination of the probability that a sensor network system may fail and the evaluation of the severity of the damage caused by the failure. In this paper, we devise a methodology of Rough Outlier Detection (ROD) for the detection of security-based risk factor, which originates from violations of attack requirements (namely, attack risks). The methodology elaborates dimension reduction method to analyze the attack risk probability from high dimensional and nonlinear data set, and combines it with rough redundancy reduction and the distance measurement of kernel function which is obtained using the ROD. In this way, it is possible to determine the risky scenarios, and the analysis feedback can be used to improve the sensor network system design. We illustrate the methodology in the DARPA case set study using step-by-step approach and then prove that the method is effective in lowering the rate of false alarm.展开更多
基金the Jiangsu 973 Scientific Project，the National Natural Science Foundation of China，the Jiangsu Natural Science Foundation，the Aerospace Innovation Fund，the Lianyungang Science & Technology Project
文摘Security is a nonfunctional information system attribute that plays a crucial role in wide sensor network application domains. Security risk can be quantified as the combination of the probability that a sensor network system may fail and the evaluation of the severity of the damage caused by the failure. In this paper, we devise a methodology of Rough Outlier Detection (ROD) for the detection of security-based risk factor, which originates from violations of attack requirements (namely, attack risks). The methodology elaborates dimension reduction method to analyze the attack risk probability from high dimensional and nonlinear data set, and combines it with rough redundancy reduction and the distance measurement of kernel function which is obtained using the ROD. In this way, it is possible to determine the risky scenarios, and the analysis feedback can be used to improve the sensor network system design. We illustrate the methodology in the DARPA case set study using step-by-step approach and then prove that the method is effective in lowering the rate of false alarm.
