Towards Crossfire Distributed Denial of Service Attack Protection Using Intent-Based Moving Target Defense Over Software-Defined Networking

被引:10
|
作者
Hyder, Muhammad Faraz [1 ]
Fatima, Tasbiha [2 ]
机构
[1] NED Univ Engn & Technol, Dept Software Engn & Technol, Karachi 75270, Pakistan
[2] NED Univ Engn & Technol, Dept Comp Sci & Informat Technol, Karachi 75270, Pakistan
关键词
Denial-of-service attack; Computer crime; Security; Software; Ports (computers); Hardware; Cloud computing; Crossfire DDoS; network function virtualization; intent-based networking; moving target defense; software defined networking; SDN;
D O I
10.1109/ACCESS.2021.3103845
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Crossfire is an indirect target area link-flooding Distributed Denial of Service (DDoS) attack determined to affect the neighbors of the real target. Currently, Crossfire DDoS attacks are acquiring impetus because of their indistinguishability and undetectability. SDN (Software Defined Networking) is a progressing technique because of its adaptability and programmability. Moving Target Defense (MTD) is an arising security strategy to counter attacks by progressively changing the attacked plane. IBN (Intent-based Networking) is another promising methodology for providing dynamic network management. IBN-based MTD can provide efficient MTD solutions because of the concentrated control and observing capacities of the intents when translated into rules inside the SDN control plane. In this paper, a framework for the security of Crossfire DDoS attacks is proposed by making use of Intent-based Traffic modifications through the Open Networking Operating System (ONOS) Rest API and Domain Name System (DNS) port redirection. In this paper, we exploited Intent-based MTD to divert traffic from the principal host to virtual shadow hosts to counter this attack. Traffic redirection helps in masquerading the attacker headed for shadow host and consequently getting the erroneous path towards the network and, hence, the Crossfire attack couldn't be executed as expected. The proposed technique is simulated using Mininet and ONOS SDN controllers. The outcomes showed traffic is successfully redirected at a low computational expense. Therefore, Crossfire DDoS is efficiently mitigated as promising results are found.
引用
收藏
页码:112792 / 112804
页数:13
相关论文
共 50 条
  • [41] Prevention Mechanism for Infrastructure based Denial-of-Service attack over Software Defined Network
    Singh, Sandeep
    Khan, R. A.
    Agrawal, Alka
    2015 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION & AUTOMATION (ICCCA), 2015, : 348 - 353
  • [42] Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN)
    Oo, Myo Myint
    Kamolphiwong, Sinchai
    Kamolphiwong, Thossaporn
    Vasupongayya, Sangsuree
    JOURNAL OF COMPUTER NETWORKS AND COMMUNICATIONS, 2019, 2019
  • [43] DNN-based Denial of Quality of Service Attack on Software-defined Hybrid Edge-Cloud Systems
    Minh Nguyen
    Gately, Jacob
    Kar, Swati
    Dey, Soumyabrata
    Debroy, Saptarshi
    2022 IEEE 22ND ANNUAL WIRELESS AND MICROWAVE TECHNOLOGY CONFERENCE (WAMICON), 2022,
  • [44] Design and Performance Analysis of Software Defined Networking based Web Services Adopting Moving Target Defense
    Kim, Dong Seong
    Kim, Minjune
    Cho, Jin-Hee
    Lim, Hyuk
    Moore, Terrence J.
    Nelson, Frederica F.
    2020 50TH ANNUAL IEEE-IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS-SUPPLEMENTAL VOLUME (DSN-S), 2020, : 43 - 44
  • [45] Moving Target Defense for In-Vehicle Software-Defined Networking: IP Shuffling in Network Slicing with Multiagent Deep Reinforcement Learning
    Yoon, Seunghyun
    Cho, Jin-Hee
    Kim, Dong Seong
    Moore, Terrence J.
    Nelson, Frederica F.
    Lim, Hyuk
    Leslie, Nandi
    Kamhoua, Charles A.
    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS II, 2020, 11413
  • [46] Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges
    Yan, Qiao
    Yu, F. Richard
    Gong, Qingxiang
    Li, Jianqiang
    IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (01): : 602 - 622
  • [47] A robust tuned classifier-based distributed denial of service attacks detection for quality of service enhancement in software-defined network
    Kaur, Gaganjot
    Gupta, Prinima
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 43 (03) : 2693 - 2710
  • [48] Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment
    Bhushan, Kriti
    Gupta, B. B.
    JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (05) : 1985 - 1997
  • [49] Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment
    Kriti Bhushan
    B. B. Gupta
    Journal of Ambient Intelligence and Humanized Computing, 2019, 10 : 1985 - 1997
  • [50] Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
    Ali, Inayat
    Hong, Seungwoo
    Cheung, Taesik
    Applied Sciences (Switzerland), 2024, 14 (19):