基于KTLAD的电力数据网业务流量异常检测

KTLAD Based Traffic Anomaly Detection Algorithm of Electric Power Data Network

针对电力数据网对流量异常检测的时效性要求,提出一种改进的局部异常因子异常检测方法 KTLAD.该方法基于密度进行检测,计算每个流量包与附近流量包的分隔程度,无需预先设置流量的具体异常状态,相对传统方法具有很高的灵活性.仿真结果验证了KTLAD在电力数据网中业务流量异常检测中的可行性,并且有效地降低了时间成本.

Due to the efficiency requirements of traffic anomaly detection in electric power data network,an improved anomaly detection algorithm named k-d tree based Lof anomaly detection( KTLAD) based on LOF was proposed. Based on density detection,the algorithm calculated the separating level of each traffic package with nearby ones without pre-set specific abnormal state of traffic. Comparing to the traditional algorithms,the proposed algorithm was more flexible. Simulation results showed that the KTLAD was feasible in traffic anomaly detection in electric power data network and reduced time cost effectively.