Comparative Results with Unsupervised Techniques in Cyber Attack Novelty Detection

Proceedings

Meira, Jorge¹ 

[1] Computing Department, University of Coruña, Coruña 15071, Spain

关键词: unsupervised learning;    anomaly detection;    outlier detection;    novelty detection;   

DOI  :  10.3390/proceedings2181191

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Intrusion detection is a major necessity in current times. Computer systems are constantly being victims of malicious attacks. These attacks keep on exploring new technics that are undetected by current Intrusion Detection Systems (IDS), because most IDS focus on detecting signatures of previously known attacks. This work explores some unsupervised learning algorithms that have the potential of identifying previously unknown attacks, by performing outlier detection. The algorithms explored are one class based: the Autoencoder Neural Network, K-Means, Nearest Neighbor and Isolation Forest. There algorithms were used to analyze two publicly available datasets, the NSL-KDD and ISCX, and compare the results obtained from each algorithm to perceive their performance in novelty detection.

【 授权许可】

CC BY