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
[31]   Combating DDoS Attack with Dynamic Detection of Anomalous Hosts in Software Defined Network [J].
Zhao, Rudong ;
Wei, Songjie ;
Ren, Milin .
2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC), 2017, :37-42
[32]   Deep learning-based classification model for botnet attack detection [J].
Abdulghani Ali Ahmed ;
Waheb A. Jabbar ;
Ali Safaa Sadiq ;
Hiran Patel .
Journal of Ambient Intelligence and Humanized Computing, 2022, 13 :3457-3466
[33]   Deep learning-based classification model for botnet attack detection [J].
Ahmed, Abdulghani Ali ;
Jabbar, Waheb A. ;
Sadiq, Ali Safaa ;
Patel, Hiran .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2020, 13 (7) :3457-3466
[34]   A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection [J].
Catillo, Marta ;
Pecchia, Antonio ;
Villano, Umberto .
APPLIED SCIENCES-BASEL, 2023, 13 (02)
[35]   Overview of DDoS Attack Detection in Software-Defined Networks [J].
Wang, Heyu ;
Li, Yixuan .
IEEE ACCESS, 2024, 12 :38351-38381
[36]   Real-time Heart Attack Mobile Detection Service (RHAMDS): An IoT Use Case for Software Defined Networks [J].
Ali, Samr ;
Ghazal, Mohammed .
2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE), 2017,
[37]   Software Defined Network Security Framework for IoT based Smart Home and City Applications [J].
Wang, Song ;
Gomez, Karina Mabell ;
Sithamparanathan, Kandeepan ;
Zanna, Paul .
2019 13TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ICSPCS), 2019,
[38]   Multiclass Machine Learning Based Botnet Detection in Software Defined Networks [J].
Tariq, Farhan ;
Baig, Shamim .
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2019, 19 (03) :150-156
[39]   A Deep Learning Based Method for Network Application Classification in Software-Defined IoT [J].
Umair, Muhammad Basit ;
Iqbal, Zeshan ;
Khan, Farrukh Zeeshan ;
Khan, Muhammad Attique ;
Kadry, Seifedine .
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2022, 30 (03) :463-477
[40]   IoT Network Attack Detection and Mitigation [J].
Gelenbe, Erol ;
Froehlich, Piotr ;
Nowak, Mateusz ;
Papadopoulos, Stavros ;
Protogerou, Aikaterini ;
Drosou, Anastasios ;
Tzovaras, Dimitrios .
2020 9TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2020, :123-128