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

被引:2
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 34 条
[1]   A survey of network anomaly detection techniques [J].
Ahmed, Mohiuddin ;
Mahmood, Abdun Naser ;
Hu, Jiankun .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2016, 60 :19-31
[2]   Intrusion Detection Systems: A State-of-the-Art Taxonomy and Survey [J].
Alkasassbeh, Mouhammd ;
Baddar, Sherenaz Al-Haj .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2023, 48 (08) :10021-10064
[3]   Securing massive IoT in 6G: Recent solutions, architectures, future directions [J].
Alotaibi, Asma ;
Barnawi, Ahmed .
INTERNET OF THINGS, 2023, 22
[4]  
[Anonymous], 1999, Technical Report, DTIC Document
[5]  
[Anonymous], 2012, International Journal of Network Security & Its Applications
[6]  
Axelsson S, 2000, Intrusion detection systems: a survey and taxonomy, V99, P1
[7]   Network Anomaly Detection: Methods, Systems and Tools [J].
Bhuyan, Monowar H. ;
Bhattacharyya, D. K. ;
Kalita, J. K. .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2014, 16 (01) :303-336
[8]   A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection [J].
Buczak, Anna L. ;
Guven, Erhan .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (02) :1153-1176
[9]   A Survey of Intrusion Detection Systems in Wireless Sensor Networks [J].
Butun, Ismail ;
Morgera, Salvatore D. ;
Sankar, Ravi .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2014, 16 (01) :266-282
[10]   Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges [J].
Campos, Enrique Marmol ;
Saura, Pablo Fernandez ;
Gonzalez-Vidal, Aurora ;
Hernandez-Ramos, Jose L. ;
Bernabe, Jorge Bernal ;
Baldini, Gianmarco ;
Skarmeta, Antonio .
COMPUTER NETWORKS, 2022, 203