A Systematic Mapping Study on Intrusion Response Systems

被引:0
作者
Rezapour, Adel [1 ]
Ghasemigol, Mohammad [2 ]
Takabi, Daniel [2 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Sci & Res Branch, Birjand Branch, Birjand 9717811111, Iran
[2] Old Dominion Univ, Sch Cybersecur, Norfolk, VA 23529 USA
关键词
Bot (Internet); Taxonomy; Data mining; Systematics; Surveys; Reviews; Intrusion detection; Decision making; Intrusion detection system; intrusion response system; systematic mapping study; APPROPRIATE COUNTER-MEASURES; COST-SENSITIVE ASSESSMENT; OPTIMAL COUNTERMEASURES; RISK-ASSESSMENT; ATTACK; MODEL; GAME; PREVENTION; SELECTION; MECHANISM;
D O I
10.1109/ACCESS.2024.3381998
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative approach not only streamlines the process of literature review in the IRS field but also has the potential to be adapted and implemented across a variety of research fields. As a result of this methodology, 287 papers related to the IRS were identified from a pool of 6143 studies extracted by the developed web robot based on initial keywords. This highlights its effectiveness in navigating and extracting valuable insights from the extensive body of literature. Furthermore, this research methodology allows the identification of prominent researchers, journals, conferences, and high-quality papers in the field of study.
引用
收藏
页码:46524 / 46550
页数:27
相关论文
共 290 条
  • [91] A Performance Evaluation of Deep Reinforcement Learning for Model-Based Intrusion Response
    Iannucci, Stefano
    Barba, Ovidiu Daniel
    Cardellini, Valeria
    Banicescu, Ioana
    [J]. 2019 IEEE 4TH INTERNATIONAL WORKSHOPS ON FOUNDATIONS AND APPLICATIONS OF SELF* SYSTEMS (FAS*W 2019), 2019, : 158 - 163
  • [92] Iannucci S, 2016, IEEE IC COMP COM NET
  • [93] Model-Based Response Planning Strategies for Autonomic Intrusion Protection
    Iannucci, Stefano
    Abdelwahed, Sherif
    [J]. ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS, 2018, 13 (01)
  • [94] Towards Autonomic Intrusion Response Systems
    Iannucci, Stefano
    Abdelwahed, Sherif
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), 2016, : 229 - 230
  • [95] Ibrahim K., 2011, Ph.D. dissertation
  • [96] Ikuomola A. J., 2010, PROC 2 COMPUT SCI EL, P1
  • [97] Ikuomola AJ, 2013, J INF ASSUR SECUR, V8, P147
  • [98] Cloud-Based Intrusion Detection and Response System: Open Research Issues, and Solutions
    Inayat, Zakira
    Gani, Abdullah
    Anuar, Nor Badrul
    Anwar, Shahid
    Khan, Muhammad Khurram
    [J]. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2017, 42 (02) : 399 - 423
  • [99] Intrusion response systems: Foundations, design, and challenges
    Inayat, Zakira
    Gani, Abdullah
    Anuar, Nor Badrul
    Khan, Muhammad Khurram
    Anwar, Shahid
    [J]. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2016, 62 : 53 - 74
  • [100] Irugu D., 2015, Int. J. Comput. Sci. Trends Technol., V3, P55