Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT

被引:5
|
作者
Negera, Worku Gachena [1 ]
Schwenker, Friedhelm [2 ]
Debelee, Taye Girma [3 ,4 ]
Melaku, Henock Mulugeta [1 ]
Feyisa, Degaga Wolde [3 ]
机构
[1] Addis Ababa Univ, Addis Ababa Inst Technol, Addis Ababa, Ethiopia
[2] Univ Ulm, Inst Neural Informat Proc, D-89069 Ulm, Germany
[3] Ethiopian Artificial Intelligence Inst, Addis Ababa, Ethiopia
[4] Addis Ababa Sci & Technol Univ, Dept Elect & Comp Engn, Addis Ababa, Ethiopia
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 08期
关键词
botnet; IoT; SDN; SDN-enabled IoT; detection; lightweight model; deep learning; traditional machine learning; INTERNET; THREATS;
D O I
10.3390/app13084699
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The Internet of things (IoT) is being used in a variety of industries, including agriculture, the military, smart cities and smart grids, and personalized health care. It is also being used to control critical infrastructure. Nevertheless, because the IoT lacks security procedures and lack the processing power to execute computationally costly antimalware apps, they are susceptible to malware attacks. In addition, the conventional method by which malware-detection mechanisms identify a threat is through known malware fingerprints stored in their database. However, with the ever-evolving and drastic increase in malware threats in the IoT, it is not enough to have traditional antimalware software in place, which solely defends against known threats. Consequently, in this paper, a lightweight deep learning model for an SDN-enabled IoT framework that leverages the underlying IoT resource-constrained devices by provisioning computing resources to deploy instant protection against botnet malware attacks is proposed. The proposed model can achieve 99% precision, recall, and F1 score and 99.4% accuracy. The execution time of the model is 0.108 milliseconds with 118 KB size and 19,414 parameters. The proposed model can achieve performance with high accuracy while utilizing fewer computational resources and addressing resource-limitation issues.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Lightweight Meta-Learning BotNet Attack Detection
    Fadhilla, Cut Alna
    Alfikri, Muhammad Dany
    Kaliski, Rafael
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (10) : 8455 - 8466
  • [2] IoT Botnet Attack Detection Model Based on DBO-Catboost
    Yang, Changjin
    Guan, Weili
    Fang, Zhijie
    APPLIED SCIENCES-BASEL, 2023, 13 (12):
  • [3] Customized convolutional neural network model for IoT botnet attack detection
    Bojarajulu, Balaganesh
    Tanwar, Sarvesh
    SIGNAL IMAGE AND VIDEO PROCESSING, 2024, 18 (6-7) : 5477 - 5489
  • [4] Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model
    Bojarajulu, Balaganesh
    Tanwar, Sarvesh
    Singh, Thipendra Pal
    COMPUTERS & SECURITY, 2023, 126
  • [5] Network Flow based IoT Botnet Attack Detection using Deep Learning
    Sriram, S.
    Vinayakumar, R.
    Alazab, Mamoun
    Soman, K. P.
    IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), 2020, : 189 - 194
  • [6] DDOSHIELD-IoT: A Testbed for Simulating and Lightweight Detection of IoT Botnet DDoS Attacks
    De Vivo, Simona
    Obaidat, Islam
    Dai, Dong
    Liguori, Pietro
    2024 54TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS, DSN-W 2024, 2024, : 1 - 8
  • [7] Botnet Detection using Software Defined Networking
    Wijesinghe, Udaya
    Tupakula, Udaya
    Varadharajan, Vijay
    2015 22ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT), 2015, : 219 - 224
  • [8] Hybrid Machine Learning Model for Efficient Botnet Attack Detection in IoT Environment
    Ali, Mudasir
    Shahroz, Mobeen
    Mushtaq, Muhammad Faheem
    Alfarhood, Sultan
    Safran, Mejdl
    Ashraf, Imran
    IEEE ACCESS, 2024, 12 : 40682 - 40699
  • [9] Efficient and Intelligent Attack Detection in Software Defined IoT Networks
    Zhang, Yuntong
    Xu, Jingye
    Wang, Zhiwei
    Geng, Rong
    Choo, Kim-Kwang Raymond
    Arturo Perez-Diaz, Jesus
    Zhu, Dakai
    2020 IEEE INTERNATIONAL CONFERENCE ON EMBEDDED SOFTWARE AND SYSTEMS (ICESS), 2020,
  • [10] Lightweight Machine Learning Prediction Algorithm for Network Attack on Software Defined Network
    Ibrahimy, Arya Maulana
    Dewanta, Favian
    Aminanto, Muhammad Erza
    2022 IEEE ASIA PACIFIC CONFERENCE ON WIRELESS AND MOBILE (APWIMOB), 2022, : 55 - 60