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
  • [21] NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains
    Saurabh, Kumar
    Kumar, Tanuj
    Singh, Uphar
    Vyasl, O. P.
    Khondoker, Rahamatullah
    2022 IEEE WORLD AI IOT CONGRESS (AIIOT), 2022, : 736 - 742
  • [22] INFRDET: IoT network flow regulariser-based detection and classification of IoT botnet
    Garg, Umang
    Kumar, Santosh
    Kumar, Manoj
    INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING, 2023, 14 (06) : 606 - 616
  • [23] A Lightweight Trust Mechanism with Attack Detection for IoT
    Zhou, Xujie
    Tang, Jinchuan
    Dang, Shuping
    Chen, Gaojie
    ENTROPY, 2023, 25 (08)
  • [24] A novel botnet attack detection for IoT networks based on communication graphs
    Munoz, David Concejal
    Valiente, Antonio del-Corte
    CYBERSECURITY, 2023, 6 (01)
  • [25] A Graphical Approach for Botnet Detection in IoT Edge Environments Using a Lightweight Dynamic Louvain Method
    Mohan, H. G.
    Kumar, Jalesh
    INTERNET TECHNOLOGY LETTERS, 2025, 8 (02)
  • [26] An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks
    Sahoo, Kshira Sagar
    Tripathy, Bata Krishna
    Naik, Kshirasagar
    Ramasubbareddy, Somula
    Balusamy, Balamurugan
    Khari, Manju
    Burgos, Daniel
    IEEE ACCESS, 2020, 8 : 132502 - 132513
  • [27] Software Defined Network as Solution to Overcome Security Challenges in IoT
    Shuhaimi, Fatma A. L.
    Jose, Manju
    Singh, Ajay Vikram
    2016 5TH INTERNATIONAL CONFERENCE ON RELIABILITY, INFOCOM TECHNOLOGIES AND OPTIMIZATION (TRENDS AND FUTURE DIRECTIONS) (ICRITO), 2016, : 491 - 496
  • [28] Denial of Service Attack in Software Defined Network
    Bera, Piu
    Saha, Ankita
    Setua, S. K.
    PROCEEDINGS OF 2016 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), 2016, : 497 - 501
  • [29] Combating DDoS Attack with Dynamic Detection of Anomalous Hosts in Software Defined Network
    Zhao, Rudong
    Wei, Songjie
    Ren, Milin
    2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC), 2017, : 37 - 42
  • [30] A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection
    Catillo, Marta
    Pecchia, Antonio
    Villano, Umberto
    APPLIED SCIENCES-BASEL, 2023, 13 (02):