The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Author:

Naik Binny,Mehta Ashir,Yagnik Hiteshri,Shah Manan

Abstract

AbstractGiven the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity experts are seriously considering artificial intelligence as a potential field that can aid improvements in conventional cybersecurity techniques. Various progressions in the field of technology have helped to mitigate some of the issues relating to cybersecurity. These advancements can be manifested by Big Data, Blockchain technology, Behavioral Analytics, to name but a few. The paper overviews the effects of applications of these technologies in cybersecurity. The central purpose of the paper is to review the application of AI techniques in analyzing, detecting, and fighting various cyberattacks. The effects of the implementation of conditionally classified “distributed” AI methods and conveniently classified “compact” AI methods on different cyber threats have been reviewed. Furthermore, the future scope and challenges of using such techniques in cybersecurity, are discussed. Finally, conclusions have been drawn in terms of evaluating the employment of different AI advancements in improving cybersecurity.

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference100 articles.

1. Abie H (2000) An overview of firewall technologies. Telektronikk 96(3):47–52

2. Abomhara M, Køien GM (2015) Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. J Cyber Secur Mob 4:65–88

3. Achbarou O, El Kiram MA, Bourkoukou O, Elbouanani S (2018) A new distributed intrusion detection system based on multi-agent system for cloud environment. Int J Commun Netw Inf Secur 10(3):526

4. Al-Yaseen WL, Othman ZA, Nazri MZA (2016) Real-time intrusion detection system using multi-agent system. IAENG Int J Comput Sci 43(1):80–90

5. Al-Zewairi M, Almajali S, Awajan A (2017) Experimental evaluation of a multi-layer feed-forward artificial neural network classifier for network intrusion detection system. In: 2017 International conference on new trends in computing sciences (ICTCS), IEEE, pp 167–172

Cited by 23 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3