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
  • [21] Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review
    Tyagi, Suryakant
    Szenasi, Sandor
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (29) : 73427 - 73456
  • [22] Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook
    Padhi A.
    Agarwal A.
    Saxena S.K.
    Katoch C.D.S.
    VirusDisease, 2023, 34 (3) : 345 - 355
  • [23] Machine learning and deep learning techniques for detecting and mitigating cyber threats in IoT-enabled smart grids: a comprehensive review
    Tirulo, Aschalew
    Chauhan, Siddhartha
    Dutta, Kamlesh
    INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY, 2024, 24 (3-4) : 284 - 321
  • [24] Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques
    Rai, Hari Mohan
    Yoo, Joon
    Razaque, Abdul
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 255
  • [25] A comprehensive review of machine learning techniques on diabetes detection
    Toshita Sharma
    Manan Shah
    Visual Computing for Industry, Biomedicine, and Art, 4
  • [26] A comprehensive review of machine learning techniques on diabetes detection
    Sharma, Toshita
    Shah, Manan
    VISUAL COMPUTING FOR INDUSTRY BIOMEDICINE AND ART, 2021, 4 (01)
  • [27] AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning Techniques
    Lewis, Jovita Relasha
    Pathan, Sameena
    Kumar, Preetham
    Dias, Cifha Crecil
    IEEE ACCESS, 2024, 12 : 163764 - 163786
  • [28] Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications
    Jiao, Zeren
    Hu, Pingfan
    Xu, Hongfei
    Wang, Qingsheng
    ACS CHEMICAL HEALTH & SAFETY, 2020, 27 (06) : 316 - 334
  • [29] Brain Tumor Analysis Empowered with Machine Learning and Deep Learning: A Comprehensive Review with its Recent Computational Techniques
    Dhaniya, R. D.
    Umamaheswari, K. M.
    INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION, 2022, 14 (03) : 631 - 639
  • [30] Advancements in hybrid approaches for brain tumor segmentation in MRI: a comprehensive review of machine learning and deep learning techniques
    Ravikumar Sajjanar
    Umesh D. Dixit
    Vittalkumar K Vagga
    Multimedia Tools and Applications, 2024, 83 : 30505 - 30539