Review of Intrusion Detection Systems Taxonomy, Techniques, Methods and Future Research Directions

被引:0
作者
Mikulas, Matus [1 ]
Kotuliak, Ivan [1 ]
机构
[1] Slovak Univ Technol Bratislava, Fac Informat & Informat Technol, Bratislava, Slovakia
来源
2024 NEW TRENDS IN SIGNAL PROCESSING, NTSP 2024 | 2024年
关键词
review; IDS; anomaly; taxonomy; machine learning; future directions; INTELLIGENCE;
D O I
10.23919/NTSP61680.2024.10726305
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the field of intrusion detection, research has made enormous progress over the past decade, and there are many articles devoted to this issue. Currently, however, intrusion detection covers a very wide spectrum of new technologies, and mapping this area, current trends and open problems could greatly help researchers find their way and choose the path to take. There are also many different reviews and studies in this domain, which provide different taxonomy and categorization for the same terms what is causing confusion, especially for new researchers in this domain. The article is providing comprehensive overview of different Intrusion Detection System (IDS) methodologies, techniques and taxonomy. Also, the most important principles are described so that the reader, after reading it, clearly understands what IDS is and what its different categories are, how it works. A big emphasis is placed on various open problems for future research.
引用
收藏
页码:105 / 112
页数:8
相关论文
共 50 条
  • [21] Ransomware Detection Using Machine Learning: A Review, Research Limitations and Future Directions
    Ispahany, Jamil
    Islam, Md. Rafiqul
    Islam, Md. Zahidul
    Khan, M. Arif
    IEEE ACCESS, 2024, 12 : 68785 - 68813
  • [22] Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions
    Do, Nguyet Quang
    Selamat, Ali
    Krejcar, Ondrej
    Herrera-Viedma, Enrique
    Fujita, Hamido
    IEEE ACCESS, 2022, 10 : 36429 - 36463
  • [23] Systematic literature review on intrusion detection systems: Research trends, algorithms, methods, datasets, and limitations
    Issa, Melad Mohammed
    Aljanabi, Mohammad
    Muhialdeen, Hassan M.
    JOURNAL OF INTELLIGENT SYSTEMS, 2024, 33 (01)
  • [24] Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems
    Santos, Kelson Carvalho
    Miani, Rodrigo Sanches
    Silva, Flavio de Oliveira
    JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 2024, 32 (02)
  • [25] A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions
    Abdullah, Talal A. A.
    Zahid, Mohd Soperi Mohd
    Ali, Waleed
    SYMMETRY-BASEL, 2021, 13 (12):
  • [26] Shocks and IS user behavior: a taxonomy and future research directions
    Meier, Marco
    Maier, Christian
    Thatcher, Jason Bennett
    Weitzel, Tim
    INTERNET RESEARCH, 2023, 33 (03) : 853 - 889
  • [27] Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems
    Macas, Mayra
    Wu, Chunming
    2020 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM 2020), 2020,
  • [28] Cloud computing research: A review of research themes, frameworks, methods and future research directions
    Senyo, Prince Kwame
    Addae, Erasmus
    Boateng, Richard
    INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2018, 38 (01) : 128 - 139
  • [29] Edge AI: A Taxonomy, Systematic Review and Future Directions
    Gill, Sukhpal Singh
    Golec, Muhammed
    Hu, Jianmin
    Xu, Minxian
    Du, Junhui
    Wu, Huaming
    Walia, Guneet Kaur
    Murugesan, Subramaniam Subramanian
    Ali, Babar
    Kumar, Mohit
    Ye, Kejiang
    Verma, Prabal
    Kumar, Surendra
    Cuadrado, Felix
    Uhlig, Steve
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2025, 28 (01):
  • [30] Mobile malware attacks: Review, taxonomy & future directions
    Qamar, Attia
    Karim, Ahmad
    Chang, Victor
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 97 : 887 - 909