Journal Menu
By: Jermina F., Naveen A, Dharaneeshwaran K, Harish Kumar.D, and Santhosh kumar.P.
1. Assistant Professor/ Department of CSE(Cyber Security) Karpagam College of Engineering Coimbatore, India
2. Student Department of CSE(Cyber Security) Karpagam College of Engineering Coimbatore, India
The rapid advancement and widespread adoption of internet technologies have led to a surge in cyber-attacks, with botnet attacks emerging as particularly detrimental. Identifying botnet activities is increasingly challenging due to the diverse attack vectors and the evolving nature of malware. As the Internet of Things (IoT) continues to expand, network devices become more vulnerable to these sophisticated attacks, resulting in significant security breaches and financial losses. In order to tackle this issue, we suggest a hybrid machine learning model for efficient botnet detection in Internet of Things (IoT) settings. This model utilizes a unique stacking technique that combines Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Recurrent Neural Networks (RNN) into a unified system known as ACLR. When compared to other models, ours performs better, with a testing accuracy of 96.98%. A high Precision-Recall Area Under the Curve (PR-AUC) score of 0.9950 and a high Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.9934 demonstrate the ACLR model’s superiority in botnet detection. Comparative analysis with existing state-of-the-art techniques highlights the effectiveness of the ACLR model in capturing the complex patterns of botnet activities, thereby offering a promising solution for enhancing cybersecurity measures in IoT environments.
Botnet Detection,Hybrid Machine Learning,Artificial Neural Networks (ANN),Convolutional Neural Networks (CNN),Long Short-Term Memory (LSTM),Recurrent,Neural Networks (RNN),Internet of Things (IoT),Cybersecurity,Receiver,Operating Characteristic (ROC) Curve,Precision-Recall (PR) Curve
Citation:
Refrences:
- Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM computing surveys (CSUR). 1999 Sep 1;31(3):264-323.
- Guerra-Manzanares A, Bahsi H, Nomm S. Hybrid feature selection models for machine learning based botnet detection in IoT networks. In2019 International Conference on Cyberworlds (CW) 2019 Oct 2 (pp. 324-327). IEEE.
- Xing Y, Shu H, Zhao H, Li D, Guo L. Survey on botnet detection techniques: Classification, methods, and evaluation. Mathematical Problems in Engineering. 2021;2021(1):6640499.
- McDermott CD, Majdani F, Petrovski AV. Botnet detection in the internet of things using deep learning approaches. In2018 international joint conference on neural networks (IJCNN) 2018 Jul 8 (pp. 1-8). IEEE .
- Di Mauro M, Galatro G, Liotta A. Experimental review of neural-based approaches for network intrusion management. IEEE Transactions on Network and Service Management. 2020 Sep 15;17(4):2480-95.
- Chen SC, Chen YR, Tzeng WG. Effective botnet detection through neural networks on convolutional features. In2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) 2018 Aug 1 (pp. 372-378). IEEE .
- Zhen L, Kamarudin NH, Kok VJ, Qamar F. Anomaly Detection Model in Network Security Situational Awareness based on Machine Learning: Limitation, Techniques, Future Trends. IEEE Access. 2025 Jul 16.
- Kwon D, Kim H, Kim J, Suh SC, Kim I, Kim KJ. A survey of deep learning-based network anomaly detection. Cluster Computing. 2019 Jan 16;22(Suppl 1):949-61.
- Costa J, Dessai N, Gaonkar S, Aswale S, Shetgaonkar P. Iot-botnet detection using long short-term memory recurrent neural network. Int. J. Eng. Res. 2020 Aug;9(8):531-6.
- Cui J, Long J, Min E, Liu Q, Li Q. Comparative study of CNN and RNN for deep learning based intrusion detection system. InInternational Conference on Cloud Computing and Security 2018 Jun 8 (pp. 159-170). Cham: Springer International Publishing.
- Pokhrel S, Abbas R, Aryal B. IoT security: botnet detection in IoT using machine learning. arXiv preprint arXiv:2104.02231. 2021 Apr 6.
- Zhang H. Development of an intelligent intrusion detection system for IoT networks using deep learning. Discover Internet of Things. 2025 Jul 7;5(1):74.
- Chen SC, Chen YR, Tzeng WG. Effective botnet detection through neural networks on convolutional features. In2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) 2018 Aug 1 (pp. 372-378). IEEE.
- Vinayakumar R, Soman KP, Poornachandran P. Applying convolutional neural network for network intrusion detection. In2017 International conference on advances in computing, communications and informatics (ICACCI) 2017 Sep 13 (pp. 1222-1228). IEEE.
- Ullah I, Mahmoud QH. Design and development of RNN anomaly detection model for IoT networks. IEEE Access. 2022 May 18;10:62722-50.
- Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE transactions on knowledge and data engineering. 2013 Jun 26;26(1):97-107. Pokhrel S, Abbas R, Aryal B. IoT security: botnet detection in IoT using machine learning. arXiv preprint arXiv:2104.02231. 2021 Apr 6.
- Alotaibi Y, Ilyas M. Ensemble-learning framework for intrusion detection to enhance internet of things’ devices security. Sensors. 2023 Jun 14;23(12):5568.
- Ali M, Shahroz M, Mushtaq MF, Alfarhood S, Safran M, Ashraf I. Hybrid machine learning model for efficient botnet attack detection in iot environment. IEEE Access. 2024 Mar 12;12:40682-99 .
- Al-Shurbaji T, Anbar M, Manickam S, Hasbullah IH, ALfriehate N, Alabsi BA, Alzighaibi AR, Hashim H. Deep learning-based intrusion detection system for detecting IoT botnet attacks: a review. IEEE Access. 2025 Jan 7.