Artificial intelligence for cybersecurity: Literature review and future research directions

被引:121
作者
Kaur, Ramanpreet [1 ]
Gabrijelcic, Dusan [1 ]
Klobucar, Tomaz [1 ]
机构
[1] Jozef Stefan Inst, Lab Open Syst & Networks, Ljubljana, Slovenia
关键词
Detection; Protection; Response; Recovery; Identify; Learning; Cyberattacks; Taxonomy; CYBER-PHYSICAL SYSTEMS; INTERNET-OF-THINGS; DECISION-SUPPORT; GENERATION; NETWORK; VULNERABILITIES; CLASSIFICATION; CYBERATTACKS; METHODOLOGY; INTRUSIONS;
D O I
10.1016/j.inffus.2023.101804
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the security posture against various security issues and cyberattacks. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review resulted in 2395 studies, of which 236 were identified as primary. This article classifies the identified AI use cases based on a NIST cybersecurity framework using a thematic analysis approach. This classification framework will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in different contexts. The review also identifies future research opportunities in emerging cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for the successful adoption of AI-based cybersecurity in today's era of digital transformation and polycrisis.
引用
收藏
页数:29
相关论文
共 246 条
[41]  
Chen JQ, 2016, PROCEEDINGS OF 2016 FUTURE TECHNOLOGIES CONFERENCE (FTC), P1040, DOI 10.1109/FTC.2016.7821732
[42]   Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences [J].
Chen, Yuqi ;
Poskitt, Christopher M. ;
Sun, Jun ;
Adepu, Sridhar ;
Zhang, Fan .
34TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2019), 2019, :962-973
[43]   Computational-Intelligence-Inspired Adaptive Opportunistic Clustering Approach for Industrial IoT Networks [J].
Chithaluru, Premkumar ;
Al-Turjman, Fadi ;
Kumar, Manoj ;
Stephan, Thompson .
IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (09) :7884-7892
[44]   Intrusion detection approach based on optimised artificial neural network [J].
Choras, Michal ;
Pawlicki, Marek .
NEUROCOMPUTING, 2021, 452 :705-715
[45]   Ontology driven AI and Access Control Systems for Smart Fisheries [J].
Chukkapalli, Sai Sree Laya ;
Aziz, Shaik Barakhat ;
Alotaibi, Nouran ;
Mittal, Sudip ;
Gupta, Maanak ;
Abdelsalam, Mahmoud .
SAT-CPS'21: PROCEEDINGS OF THE 2021 ACM WORKSHOP ON SECURE AND TRUSTWORTHY CYBER-PHYSICAL SYSTEMS, 2021, :59-68
[46]   Website categorization via design attribute learning [J].
Cohen, Doron ;
Naim, Or ;
Toch, Eran ;
Ben-Gal, Irad .
COMPUTERS & SECURITY, 2021, 107
[47]   A Novel Online Incremental Learning Intrusion Prevention System [J].
Constantinides, Christos ;
Shiaeles, Stavros ;
Ghita, Bogdan ;
Kolokotronis, Nicholas .
2019 10TH IFIP INTERNATIONAL CONFERENCE ON NEW TECHNOLOGIES, MOBILITY AND SECURITY (NTMS), 2019,
[48]   On the Evaluation of Sequential Machine Learning for Network Intrusion Detection [J].
Corsini, Andrea ;
Yang, Shanchieh Jay ;
Apruzzese, Giovanni .
ARES 2021: 16TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY, 2021,
[49]   Source Authentication of Distribution Synchrophasors for Cybersecurity of Microgrids [J].
Cui, Yi ;
Bai, Feifei ;
Yan, Ruifeng ;
Saha, Tapan ;
Ko, Ryan K. L. ;
Liu, Yilu .
IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (05) :4577-4580
[50]   Compiler Fuzzing through Deep Learning [J].
Cummins, Chris ;
Petoumenos, Pavlos ;
Murray, Alastair ;
Leather, Hugh .
ISSTA'18: PROCEEDINGS OF THE 27TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS, 2018, :95-105