Autonomic Intrusion Detection and Response Using Big Data

被引：14

作者：

Vieira, Kleber ^{[1
]}

Koch, Fernando L. ^{[2
]}

Sobral, Joao Bosco M. ^{[3
]}

Westphall, Carlos Becker ^{[3
]}

de Souza Leao, Jorge Lopes ^{[4
]}

机构：

[1] SENAI Inst Embedded Syst, Florianopolis, SC, Brazil

[2] IBM Serv, New York, NY 10016 USA

[3] Univ Fed Santa Catarina, BR-88040900 Florianopolis, SC, Brazil

[4] Univ Fed Rio de Janeiro, BR-21941901 Rio De Janeiro, RJ, Brazil

来源：

IEEE SYSTEMS JOURNAL | 2020年 / 14卷 / 02期

关键词：

Big Data; Intrusion detection; Knowledge based systems; Phishing; Monitoring; Autonomic computing; big data; cybersecurity; distributed computing; intrusion detection systems;

D O I：

10.1109/JSYST.2019.2945555

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a method for autonomic intrusion detection and response to optimize processes of cybersecurity in large distributed systems. These environments are characterized by technology fragmentation and complex operations making them highly susceptible to attacks like hijacking, man-in-the-middle, denial-of-service, phishing, and others. The autonomic intrusion response system introduces models of operational analysis and reaction based on the combination of autonomic computing and big data. We implemented a proof-of-concept and executed experiments that demonstrate significant improvement in effectiveness and scalability of the method in complex environments.

引用

页码：1984 / 1991

页数：8

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