Detection and Mitigation of Denial of Service Attacks in Internet of Things Networks

被引:5
作者
Sanli, Mustafa [1 ]
机构
[1] Aselsan, Ankara, Turkiye
关键词
Denial of service; Field programmable gate array; Internet of things; INTRUSION DETECTION SYSTEM; DDOS DEFENSE; MODEL;
D O I
10.1007/s13369-023-08669-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The increasing number of sensors and Internet of Things (IoT) devices have made the Denial of Service (DoS) attacks in IoT networks a significant security threat. The inherent characteristics of IoT networks such as the large number of end nodes, the heterogeneous nature of IoT networks, resource limitations in routers, and the multiplicity of application areas in daily life, make the investigation of attacks in these networks a major and important research problem. This paper presents a new approach for detecting and mitigating DoS attacks in IoT networks. The proposed approach is implemented on an Field Programmable Gate Array (FPGA)-based platform and tested for performance against different types of DoS attacks. The approach can respond quickly to attacks specific to IoT networks and can be easily implemented on hardware with low resource requirements.
引用
收藏
页码:12629 / 12639
页数:11
相关论文
共 49 条
  • [1] DDoS Attack Mitigation in Internet of Things Using Software De ned Networking
    Ahmed, M. Ejaz
    Kim, Hyoungshick
    [J]. 2017 THIRD IEEE INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (IEEE BIGDATASERVICE 2017), 2017, : 271 - 276
  • [2] Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection
    Al Shorman, Amaal
    Faris, Hossam
    Aljarah, Ibrahim
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 11 (07) : 2809 - 2825
  • [3] Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions
    Al-rimy, Bander Ali Saleh
    Maarof, Mohd Aizaini
    Shaid, Syed Zainudeen Mohd
    [J]. COMPUTERS & SECURITY, 2018, 74 : 144 - 166
  • [4] Ensemble Detection Model for IoT IDS
    Alhowaide, Alaa
    Alsmadi, Izzat
    Tang, Jian
    [J]. INTERNET OF THINGS, 2021, 16
  • [5] An intrusion detection framework for energy constrained IoT devices
    Arshad, Junaid
    Azad, Muhammad Ajmal
    Abdeltaif, Muhammad Mahmoud
    Salah, Khaled
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 136 (136)
  • [6] DeepDetect: Detection of Distributed Denial of Service Attacks Using Deep Learning
    Asad, Muhammad
    Asim, Muhammad
    Javed, Talha
    Beg, Mirza O.
    Mujtaba, Hasan
    Abbas, Sohail
    [J]. COMPUTER JOURNAL, 2020, 63 (07) : 983 - 994
  • [7] Bisson D., The 5 most significant ddos attacks of 2016
  • [8] Braga R, 2010, C LOCAL COMPUT NETW, P408, DOI 10.1109/LCN.2010.5735752
  • [9] Carcano A, 2010, LECT NOTES COMPUT SC, V6027, P138
  • [10] An Artificial Immune-based Distributed Intrusion Detection Model for the Internet of Things
    Chen, Run
    Liu, Caiming
    Chen, Chao
    [J]. ADVANCED RESEARCH ON MATERIAL ENGINEERING, ARCHITECTURAL ENGINEERING AND INFORMATIZATION, 2012, 366 : 165 - +