文章基本信息

标题：Transfer learning for detecting unknown network attacks
本地全文：下载
作者：Juan Zhao ; Sachin Shetty ; Jan Wei Pan 等
期刊名称：EURASIP Journal on Information Security
印刷版ISSN：1687-4161
电子版ISSN：1687-417X
出版年度：2019
卷号：2019
期号：1
页码：1-13
DOI：10.1186/s13635-019-0084-4
出版社：Hindawi Publishing Corporation
摘要：Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in real time. To address these problems, we proposed utilizing transfer learning for detecting new and unseen attacks by transferring the knowledge of the known attacks. In our previous work, we have proposed a transfer learning-enabled framework and approach, called HeTL, which can find the common latent subspace of two different attacks and learn an optimized representation, which was invariant to attack behaviors’ changes. However, HeTL relied on manual pre-settings of hyper-parameters such as relativeness between the source and target attacks. In this paper, we extended this study by proposing a clustering-enhanced transfer learning approach, called CeHTL, which can automatically find the relation between the new attack and known attack. We evaluated these approaches by stimulating scenarios where the testing dataset contains different attack types or subtypes from the training set. We chose several conventional classification models such as decision trees, random forests, KNN, and other novel transfer learning approaches as strong baselines. Results showed that proposed HeTL and CeHTL improved the performance remarkably. CeHTL performed best, demonstrating the effectiveness of transfer learning in detecting new network attacks.
关键词：Network attacks detection; Machine learning; Transfer learning