The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

被引:0
|
作者
Mijwil M.M. [1 ]
Salem I.E. [1 ]
Ismaeel M.M. [1 ]
机构
[1] Computer Techniques Engineering Department, Baghdad College of Economic Sciences University, IRAQ, Baghdad
来源
Iraqi Journal for Computer Science and Mathematics | 2023年 / 4卷 / 01期
关键词
Artificial Intelligence; Cybersecurity; Data Science; Deep Learning; Machine Learning;
D O I
10.52866/ijcsm.2023.01.01.008
中图分类号
学科分类号
摘要
People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt systems. Cybersecurity refers to a collection of technical, organizational, and executive means for preventing the unauthorized use or misuse of electronic information and communication systems to ensure the continuity of their work, guarantee the confidentiality and privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this article explores the cybersecurity practices that protect computer systems from attacks, hacking, and data thefts and investigates the role of artificial intelligence in this domain. This article also summarizes the most significant literature that explore the roles and effects of machine learning and deep learning techniques in cybersecurity. Results show that machine learning and deep learning techniques play significant roles in protecting computer systems from unauthorized entry and in controlling system penetration by predicting and understanding the behaviour and traffic of malicious software. © 2023 Authors. All rights reserved.
引用
收藏
页码:87 / 101
页数:14
相关论文
共 50 条
  • [31] A review of deep learning and machine learning techniques for hydrological inflow forecasting
    Latif, Sarmad Dashti
    Ahmed, Ali Najah
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2023, 25 (11) : 12189 - 12216
  • [32] A review of deep learning and machine learning techniques for hydrological inflow forecasting
    Sarmad Dashti Latif
    Ali Najah Ahmed
    Environment, Development and Sustainability, 2023, 25 : 12189 - 12216
  • [33] Review of Machine Learning and Deep Learning Techniques for Medical Image Analysis
    Saratkar, Saniya
    Raut, Rohini
    Thute, Trupti
    Chaudhari, Aarti
    Thakre, Gaitri
    2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024, 2024, : 1437 - 1443
  • [34] A Review on Text Sentiment Analysis With Machine Learning and Deep Learning Techniques
    Mamani-Coaquira, Yonatan
    Villanueva, Edwin
    IEEE ACCESS, 2024, 12 : 193115 - 193130
  • [35] The application of traditional machine learning and deep learning techniques in mammography: a review
    Gao, Ying'e
    Lin, Jingjing
    Zhou, Yuzhuo
    Lin, Rongjin
    FRONTIERS IN ONCOLOGY, 2023, 13
  • [36] Review of bankruptcy prediction using machine learning and deep learning techniques
    Qu, Yi
    Quan, Pei
    Lei, Minglong
    Shi, Yong
    7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2019): INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE, 2019, 162 : 895 - 899
  • [37] A Review on Heart Diseases Using Machine Learning and Deep Learning Techniques
    Mallikarjunamallu, K.
    Syed, Khasim
    Lecture Notes in Networks and Systems, 2024, 995 : 651 - 679
  • [38] Machine learning and deep learning techniques for poultry tasks management: a review
    Subramani T.
    Jeganathan V.
    Kunkuma Balasubramanian S.
    Multimedia Tools and Applications, 2025, 84 (2) : 603 - 645
  • [39] Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review
    Pritee, Zinniya Taffannum
    Anik, Mehedi Hasan
    Alam, Saida Binta
    Jim, Jamin Rahman
    Kabir, Md Mohsin
    Mridha, M. F.
    COMPUTERS & SECURITY, 2024, 140
  • [40] Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review
    Nath, Dipjyoti
    Ankit
    Neog, Debanga Raj
    Gautam, Sachin Singh
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2024, 31 (05) : 2945 - 2984