Detecting DDoS Attacks within Milliseconds by Using FPGA-based Hardware Acceleration

被引:0
|
作者
Nagy, Balazs [1 ]
Orosz, Peter [2 ]
Tothfalusi, Tamas [1 ]
Kovacs, Laszlo [1 ]
Varga, Pal [2 ]
机构
[1] AITIA Int Inc, Telecommun Div, Budapest, Hungary
[2] Budapest Univ Technol & Econ, Dept Telecommun & Media Informat, Budapest, Hungary
关键词
DDoS; intrusion detection; Data Center Networks; FPGA;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Timely detection and mitigation of Distributed Denial of Service (DDoS) attacks are still challenging for current datacenter and Internet packet exchange operators. Detecting volumetric attacks are in the range of seconds, whereas their mitigation is often in the range of minutes. Besides the fact that the attacks are effective until their mitigation is successful, there are further attacks that remain unnoticed by current equipment. These are hit-and-run attacks that last for a fraction of a second or a few seconds only, pushing the network or the targeted service towards an unstable state and evaporate. This paper presents an FPGA-based DDoS detector and its application. The detector is capable of detecting the top-9 DDoS attack types, the 96.67% of all DDoS attacks, and the so called hit-and-run attacks within milliseconds. The concept is validated through real-life use cases on attacks of a medium-sized datacenter network.
引用
收藏
页数:4
相关论文
共 50 条
  • [11] An FPGA-based Hardware Acceleration For Key Steps of Facet Imaging Algorithm
    Nan, Tianhao
    Zhu, Yongxin
    Li, Wanyi
    Chen, Xintong
    Song, Yuefeng
    Hou, Junjie
    4TH IEEE INTERNATIONAL CONFERENCE ON SMART CLOUD (SMARTCLOUD 2019) / 3RD INTERNATIONAL SYMPOSIUM ON REINFORCEMENT LEARNING (ISRL 2019), 2019, : 86 - 91
  • [12] FPGA-based Hardware Acceleration for Image Copyright Protection Syetem Based on Blockchain
    Li, Wanyi
    Zhu, Yongxin
    Tian, Li
    Nan, Tianhao
    Chen, Xintong
    2020 7TH IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (CSCLOUD 2020)/2020 6TH IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING AND SCALABLE CLOUD (EDGECOM 2020), 2020, : 234 - 239
  • [13] FPGA-based hardware acceleration of electromagnetic wave reverse time migration imaging
    Zhu, Wenzheng
    Kuang, Lei
    SEVENTH ASIA PACIFIC CONFERENCE ON OPTICS MANUFACTURE (APCOM 2021), 2022, 12166
  • [14] Hardware Acceleration of Deep Neural Networks for Autonomous Driving on FPGA-based SoC
    Sciangula, Gerlando
    Restuccia, Francesco
    Biondi, Alessandro
    Buttazzo, Giorgio
    2022 25TH EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD), 2022, : 406 - 414
  • [15] FPGA-based hardware acceleration for local complexity analysis of massive genomic data
    Papadopoulos, Agathoklis
    Kirmitzoglou, Ioannis
    Promponas, Vasilis J.
    Theocharides, Theocharis
    INTEGRATION-THE VLSI JOURNAL, 2013, 46 (03) : 230 - 239
  • [16] OpenNoC: An Open-Source NoC Infrastructure for FPGA-Based Hardware Acceleration
    Reddy, Kuladeep Sai
    Vipin, Kizheppatt
    IEEE EMBEDDED SYSTEMS LETTERS, 2019, 11 (04) : 123 - 126
  • [17] FPGA-based Acceleration of FDAS Module Using OpenCL
    Wang, Haomiao
    Zhang, Ming
    Thiagaraj, Prabu
    Sinnen, Oliver
    2016 INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY (FPT), 2016, : 53 - 60
  • [18] Towards Cognitive Networking Using FPGA-Based Acceleration
    Lent, Ricardo
    IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION, 2021, 5 (03): : 222 - 231
  • [19] FPGA-based Systems for Evolvable Hardware
    Lambert, Cyrille
    Kalganova, Tatiana
    Stomeo, Emanuele
    PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 12, 2006, 12 : 123 - +
  • [20] Compilation for FPGA-based reconfigurable hardware
    Cardoso, JMP
    Neto, HC
    IEEE DESIGN & TEST OF COMPUTERS, 2003, 20 (02): : 65 - 75