Infection Categorization Using Deep Autoencoder

被引：0

作者：

Wang, Ming-Hung ^{[1
]}

Tsai, Meng-Han ^{[2
,3
,4
]}

Yang, Wei-Chieh ^{[2
,3
,4
]}

Lei, Chin-Laung ^{[2
]}

机构：

[1] Feng Chia Univ, Taichung, Taiwan

[2] Natl Taiwan Univ, Taipei, Taiwan

[3] Inst Informat Ind, Taipei, Taiwan

[4] Natl Ctr Cyber Secur Technol, Taipei, Taiwan

来源：

IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) | 2018年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a framework to cluster the infections according to the form of attacking using user and entity behavior analytics. We integrate outside (open-source) and inside (traffic behavior) intelligence and construct a deep autoencoder to develop infection clustering models. According to the evaluation of real infections inside a tier-1 network, we demonstrate the capability of our framework to categorize infections by their intrusion characteristics.

引用

页数：2

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