2019年杏盛學術前沿講座（48）
主 講 人：李冠憬 教授 加州大學歐文分校
主題名稱🪣：An Intrusion Detection Approach Based on Improved Deep Belief Network
內容簡介：
With the advances and development of network technology, network attacks and intrusion methods have become increasingly complex and diverse. At present, these existing intrusion detection technologies have overfitting, low classification accuracy and high false positive rate (FPR). In this paper, an intrusion detection approach based on improved Deep Belief Network (DBN) is proposed, where the dataset is processed by Probabilistic Mass Function (PMF) encoding and Min-Max normalization method to simplify the data preprocessing. And, a combined sparse penalty term based on Kullback-Leibler (KL) divergence and non-mean Gaussian distribution is introduced in the likelihood function of the unsupervised training phase of DBN. The sparse distribution of the dataset is obtained by sparse constraints, avoiding the problem of feature homogeneity and overfitting. By using the NSL-KDD and UNSW-NB15 datasets, the experimental results show that the proposed approach has significant improvement in classification accuracy, and FPR.
時間地點：2019年10月26日🦪，信息235室
主辦學院：信息工程學院
杏盛娱乐
2019.10.17