摘要
针对当前网络流量异常问题,结合当前的智能算法,和模糊集在处理不确定信息方面的优势,提出一种改进的Vague集网络流量异常监控方法。在传统Vague集的基础上,引入频度因子和相关因子,以提高Vague集识别的正确率,同时采用认知模型对异常进行识别。最后,以Kddcup99作为测试数据,对以上方法进行验证,并与实际的监控进行对比,结果表明构建的算法在数据不完备的情况,仍然具有较高的识别率,验证了本方法的可行性。
In view of the current network traffic anomaly,combined with the intelligent algorithms,and using the advantages of fuzzy sets in dealing with uncertain information,an improved Vague set network traffic anomaly monitoring method is proposed.In this method,based on the traditional Vague set,the frequency factor and the correlation factor are introduced to improve the accuracy of the Vague set recognition,and the cognitive model is used to identify the anomaly.Finally,Kddcup99 is used as the test data to verify the above method and compare with the actual monitoring.The experimental results show that the proposed algorithm still has a high recognition rate in the case of incomplete data,which proves the feasibility of the method.
作者
迟江波
刘利波
CHI Jiangbo;LIU Libo(Continuous Education College,Xinjiang Institute of Light Industry and Technology,Urumqi 830021)
出处
《微型电脑应用》
2019年第12期31-33,共3页
Microcomputer Applications
基金
新疆维吾尔自治区高校科研计划资助(XJEDU2014S087)
