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] Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies
    Radak, Mehran
    Lafta, Haider Yabr
    Fallahi, Hossein
    JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, 2023, 149 (12) : 10473 - 10491
  • [32] 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
  • [33] 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
  • [34] Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies
    Mehran Radak
    Haider Yabr Lafta
    Hossein Fallahi
    Journal of Cancer Research and Clinical Oncology, 2023, 149 : 10473 - 10491
  • [35] Advancements in hybrid approaches for brain tumor segmentation in MRI: a comprehensive review of machine learning and deep learning techniques
    Sajjanar, Ravikumar
    Dixit, Umesh D.
    Vagga, Vittalkumar K.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (10) : 30505 - 30539
  • [36] 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
  • [37] A Review on Text Sentiment Analysis With Machine Learning and Deep Learning Techniques
    Mamani-Coaquira, Yonatan
    Villanueva, Edwin
    IEEE ACCESS, 2024, 12 : 193115 - 193130
  • [38] 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
  • [39] A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
    Aiyanyo, Imatitikua D.
    Samuel, Hamman
    Lim, Heuiseok
    APPLIED SCIENCES-BASEL, 2020, 10 (17):
  • [40] Intelligent assessment of power quality disturbances: A comprehensive review on machine learning and deep learning solutions
    Jain, Shaurya
    Satsangi, Amol
    Kumar, Rajat
    Panwar, Divyani
    Amir, Mohammad
    COMPUTERS & ELECTRICAL ENGINEERING, 2025, 123