A Cyber Intrusion Detection Method based on Focal Loss Neural Network
作者单位：School of AutomationBeijing Institute of Technology
会议名称：《第三十九届中国控制会议》
会议日期：2020年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Intrusion Detection Neural Network Sample Imbalance Focal Loss
摘 要：In recent years, the applications of cyber security have been more and more widespread. Intrusion detection system as the main research direction of cyber security has attracted much attention from industrial and academic area. The performance of traditional prior knowledge based methods is degraded significantly when the system is placed in the great variable *** Learning detection method, which depends on the neural network, has high flexibility in complex environments. The sample imbalance problem of intrusion detection dataset usually confuses the engineers and researchers. In this paper, we propose an intrusion detection method which is focal loss based neural network to reduce the influence of sample imbalance *** focal loss pays more attention on the wrong predicted samples. In other words it shrinks the affects of well trained large sample categories in the total loss. In order to illustrate the performance of the proposed method, we implement two intrusion detection systems while training in different loss functions: cross entropy loss and focal loss. The experiment results show that the proposed method can effectively increase the detection performance of few sample categories.