Botnet Detection in the Internet-of-Things Networks Using Densenet - Binary Moth Flame Optimization
Keywords:
Internet of Things, IoT botnets, IoT botnet detections, DenseNet, Binary Moth Flame OptimizationAbstract
DDoS attacks based on the Internet of Things (IoT) have increased in number as a result of its recent growth. In this paper, a method for identifying botnet activity in consumer IoT networks and devices is presented. However, highly unbalanced network traffic data in the training set deteriorates the state-of-the-art ML and DL algorithms' classification capabilities, especially in classes with small sample sizes. This study developed a deep learning-based botnet assault detection algorithm called DenseNet - Binary Moth Flame Optimisation (DenseNet-BMFO). In the meantime, the overall performance of the proposed DenseNet-BMFO and other commonly used algorithms is compared using standard evaluation markers. According to the simulation results, the DenseNet-BMFO approach for identifying IoT network intrusion threats is dependable and efficient. The results of the experiments showed that the suggested methodology produced a 98.25% accuracy rate. The results of the experiment show that the suggested model performs better in botnet detection categorization than the existing methods.
Downloads
References
Arshad, Amina, Maira Jabeen, Saqib Ubaid, Ali Raza, Laith Abualigah, Khaled Aldiabat, and Heming Jia. "A novel ensemble method for enhancing Internet of Things device security against botnet attacks." Decision Analytics Journal 8 (2023): 100307.
Nasir, Muhammad Hassan, Junaid Arshad, and Muhammad Mubashir Khan. "Collaborative device-level botnet detection for internet of things." Computers & Security 129 (2023): 103172.
Thota, Swapna, and D. Menaka. "Botnet detection in the internet-of-things networks using convolutional neural network with pelican optimization algorithm." Automatika 65, no. 1 (2024): 250-260.
Sudhakar, and Sushil Kumar. "ABBDIoT: Anomaly-Based Botnet Detection Using Machine Learning Model in the Internet of Things Network." In International Conference on IoT, Intelligent Computing and Security: Select Proceedings of IICS 2021, pp. 235-245. Singapore: Springer Nature Singapore, 2023.
KOLCU, Yağız Onur, Ahmet Haşim YURTTAKAL, and Berker BAYDAN. "INTERNET OF THINGS BOTNET DETECTION VIA ENSEMBLE DEEP NEURAL NETWORKS." International Journal of 3D Printing Technologies and Digital Industry 7, no. 2 (2023): 191-197.
Gharehchopogh, Farhad Soleimanian, Benyamin Abdollahzadeh, Saeid Barshandeh, and Bahman Arasteh. "A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT." Internet of Things 24 (2023): 100952.
Al-Fawa’reh, Mohammad, Jumana Abu-Khalaf, Patryk Szewczyk, and James Jin Kang. "MalBoT-DRL: Malware Botnet Detection Using Deep Reinforcement Learning in IoT Networks." IEEE Internet of Things Journal (2023).
Nazir, Ahsan, Jingsha He, Nafei Zhu, Ahsan Wajahat, Xiangjun Ma, Faheem Ullah, Sirajuddin Qureshi, and Muhammad Salman Pathan. "Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets." Journal of King Saud University-Computer and Information Sciences (2023): 101820.
Taher, Fatma, Mahmoud Abdel-salam, Mohamed Elhoseny, and Ibrahim M. El-hasnony. "Reliable Machine Learning Model for IIoT Botnet Detection." IEEE Access (2023).
Nguyen, Dat-Thinh, and Kim-Hung Le. "The robust scheme for intrusion detection system in internet of things." Internet of Things 24 (2023): 100999.
Bojarajulu, Balaganesh, Sarvesh Tanwar, and Thipendra Pal Singh. "Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model." Computers & Security 126 (2023): 103064.
Alabsi, Basim Ahmad, Mohammed Anbar, and Shaza Dawood Ahmed Rihan. "CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks." Sensors 23, no. 14 (2023): 6507.
de Caldas Filho, Francisco Lopes, Samuel Carlos Meneses Soares, Elder Oroski, Robson de Oliveira Albuquerque, Rafael Zerbini Alves da Mata, Fábio Lúcio Lopes de Mendonça, and Rafael Timóteo de Sousa Júnior. "Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning." Sensors 23, no. 14 (2023): 6305.
Kumar, Atul, and Ishu Sharma. "Augmenting iot healthcare security and reliability with early detection of iot botnet attacks." In 2023 4th International Conference for Emerging Technology (INCET), pp. 1-6. IEEE, 2023.
Essa, Mohamed Saied, and Shawkat Kamal Guirguis. "Evaluation of Tree-Based Machine Learning Algorithms for Network Intrusion Detection in the Internet of Things." IT Professional 25, no. 5 (2023): 45-56.
Yang, Changjin, Weili Guan, and Zhijie Fang. "IoT Botnet Attack Detection Model Based on DBO-Catboost." Applied Sciences 13, no. 12 (2023): 7169.
DK Priya, MP Ramkumar, D Menaka, “Rail Fastener Fault Detection Based on Enhanced YOLOv7 Model”, 2023 International Conference on Circuit Power and Computing Technologies, 2023.
Alani, Mohammed M., Ali Ismail Awad, and Ezedin Barka. "ARP-PROBE: An ARP spoofing detector for Internet of Things networks using explainable deep learning." Internet of Things 23 (2023): 100861.
Saied, Mohamed, Shawkat Guirguis, and Magda Madbouly. "Review of artificial intelligence for enhancing intrusion detection in the internet of things." Engineering Applications of Artificial Intelligence 127 (2024): 107231.
Alkhudaydi, Omar Azib, Moez Krichen, and Ans D. Alghamdi. "A deep learning methodology for predicting cybersecurity attacks on the internet of things." Information 14, no. 10 (2023): 550.
Elsayed, Nelly, Zag ElSayed, and Magdy Bayoumi. "IoT Botnet Detection Using an Economic Deep Learning Model." arXiv preprint arXiv:2302.02013 (2023).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
