一种基于多层感知机的无监督异常检测方法

为了解决入侵检测系统应用过程中需要大量有导师数据进行训练的问题,提出了一种采用多层感知机进行无监督异常检测的方法,网络能够实现编码和还原的功能,从而在最小均方误差原则下学习样本的主要特征,给出了具体的学习算法.依据这些算法构建的系统经过仿真实验取得了较好的结果,验证了基于多层感知机的无监督异常检测方法能够在无需大量有导师信号的情况下检测出入侵,有利于入侵检测系统的推广和应用.

Unsupervised anomaly detection based on a multi-layer perceptron

A method of unsupervised anomaly detection using a multi_layer perceptron was proposed to solve the problem that a mass of supervised data is needed to apply intrusion detection in computer systems. The network can realize functions of encoding and decoding. The main characteristics of the samples were learned under the principle of least mean square errors. The detailed learning algorithm was discussed. Tests indicate the feasibility of these algorithms. The method of unsupervised anomaly detection based on a multi_layer perceptron can detect intrusions without a mass of supervised data and is fit for application in intrusion detection systems.