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

被引:9
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
[11]   Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks [J].
Popoola, Segun I. ;
Adebisi, Bamidele ;
Ande, Ruth ;
Hammoudeh, Mohammad ;
Atayero, Aderemi A. .
ELECTRONICS, 2021, 10 (09)
[12]   Lightweight Machine Learning Prediction Algorithm for Network Attack on Software Defined Network [J].
Ibrahimy, Arya Maulana ;
Dewanta, Favian ;
Aminanto, Muhammad Erza .
2022 IEEE ASIA PACIFIC CONFERENCE ON WIRELESS AND MOBILE (APWIMOB), 2022, :55-60
[13]   Botnet attack detection in IoT using hybrid optimisation enabled deep stacked autoencoder network [J].
Kalidindi, Archana ;
Arrama, Mahesh Babu .
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2023, 22 (02) :77-88
[14]   SkipGateNet: A Lightweight CNN-LSTM Hybrid Model With Learnable Skip Connections for Efficient Botnet Attack Detection in IoT [J].
Alshehri, Mohammed S. ;
Ahmad, Jawad ;
Almakdi, Sultan ;
Qathrady, Mimonah Al ;
Ghadi, Yazeed Yasin ;
Buchanan, William J. .
IEEE ACCESS, 2024, 12 :35521-35538
[15]   Machine Learning for Distributed Denial of Service Attack Detection in Software-defined IoT [J].
Nunez Segura, Gustavo ;
Carvajal Barboza, Erick .
2024 IEEE 42ND CENTRAL AMERICA AND PANAMA CONVENTION, CONCAPAN XLII, 2024,
[16]   Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review [J].
Shinan, Khlood ;
Alsubhi, Khalid ;
Alzahrani, Ahmed ;
Ashraf, Muhammad Usman .
SYMMETRY-BASEL, 2021, 13 (05)
[17]   Attack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network [J].
Farhin, Fahiba ;
Sultana, Ishrat ;
Islam, Nahida ;
Kaiser, M. Shamim ;
Rahman, Md Sazzadur ;
Mahmud, Mufti .
2020 JOINT 9TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2020 4TH INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR), 2020,
[18]   A Multi-Class Neural Network Model for Rapid Detection of IoT Botnet Attacks [J].
Alzahrani, Haifaa ;
Abulkhair, Maysoon ;
Alkayal, Entisar .
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (07) :688-696
[19]   An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT) [J].
Bhayo, Jalal ;
Hameed, Sufian ;
Shah, Syed Attique .
IEEE ACCESS, 2020, 8 :221612-221631
[20]   Botnet Attack Detection at the IoT Edge Based on Sparse Representation [J].
Tzagkarakis, Christos ;
Petroulakis, Nikolaos ;
Ioannidis, Sotiris .
2019 GLOBAL IOT SUMMIT (GIOTS), 2019,