Citation :

S. Divya, D. Kavitha, "An Integrated Approach to Detect Attacks in Cloud Environment using Wavelet Transforms and Cubic Kernel Classifiers," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 7, pp. 69-95, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I7P106

Abstract

Cyber-attacks on the Internet of Things are increasingly common due to low-power devices. Effective detection algorithms are crucial for distinguishing between benign and malicious behaviour in IoT networks and video surveillance systems. These algorithms analyse network traffic patterns and detect cyber threats. However, using feature extraction-based algorithms to predict different characteristics of network traffic poses challenges in improving classifier accuracy. This paper presents a feature extraction-based cubic kernel classifier designed to detect three major attacks: Botnet, Denial of Service (DoS), and Man-in-the-Middle (MiM) that disrupt normal operations in IoT and communication networks. In our proposed method, (i) Features are extracted using three wavelet transforms: Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Transverse Quadratic Wavelet Transform (TQWT). These wavelet transforms outperform traditional methods in detecting anomalies and patterns due to their ability to analyse both time and frequency localization (ii) Highly significant features are selected using Pearson’s correlation coefficient, and (iii) The selected features are classified as benign or malicious using Cubic Support Vector Machine (SVM), Cubic K-Nearest Neighbors (KNN). The use of a cubic kernel function effectively models complex patterns and non-linear relationships within the network and improves accuracy compared to traditional classifiers. Experimental results from Kitsune attack datasets show that the proposed TQWT-based cubic SVM (TCSVM) outperforms other methods, achieving 99.5% accuracy for both Botnet and DoS attacks, and 99% for the MiM attack.

Keywords

Cloud computing, Cyber-attacks, Wavelet transforms, Kernel classifiers, Cyber systems, Internet of Things.

References

[1] Ogugua Chimezie Obi et al., “Review of Evolving Cloud Computing Paradigms: Security, Efficiency, and Innovations,” Computer Science and IT Research Journal, vol. 5, no. 2, pp. 270-292, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Muhammad Dawood et al., “Cyberattacks and Security of Cloud Computing: A Complete Guideline,” Symmetry, vol. 15, no. 11, pp. 1-33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Rajakumaran Gayathri et al., “Detection and Mitigation of IoT-based Attacks using SNMP and Moving Target Defense Techniques,” Sensors, vol. 23, no. 3, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Hamidreza Fereidouni, Olga Fadeitcheva, and Mehdi Zalai “IoT and Man-in-the-Middle Attacks,” Security and Privacy, vol. 8, no. 2, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Shefali Madan, Anita, and Ashif Ali, “DDos Attacks in Cloud Environment,” International Journal of Health Sciences, vol. 6, no. S4, pp. 5836-5847, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Ziyad R. Alashhab et al., “Distributed Denial of Service Attacks Against Cloud Computing Environment: Survey, Issues, Challenges and Coherent Taxonomy,” Applied Sciences, vol. 12, no. 23, pp. 1-32, 2022.
[CrossRef] [Google Scholar] [Publisher Link]   

[7] R.S. Aashmi, and T. Jaya, “Detecting and Preventing of Attacks in Cloud Computing using Hybrid Algorithm,” Intelligent Automation and Soft Computing, vol. 35, no. 1, pp. 79-95, 2023.
[CrossRef] [Google Scholar] [Publisher Link]   

[8] Ravindra Kumar Chouhan, Mithilesh Atulkar, and Naresh Kumar Nagwani “A Distributed Attack Detection System for SDN using Stack of Classifiers,” International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 81-90, 2023.
[CrossRef] [Publisher Link] 

[9] Ahmad Ahmad et al., “Tuning Data Preprocessing Techniques for Improved Wind Speed Prediction,” Energy Reports, vol. 11, pp. 287-303, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[10]  Hari Gonaygunta, “Machine Learning Algorithms for Detection of Cyber Threats using Logistic Regression,” International Journal of Smart Sensor and Adhoc Network, vol. 3, no. 4, pp. 35-42, 2023.
[CrossRef] [Google Scholar]

[11] Álvaro Michelena et al., “A Novel Intelligent Approach for Man-in-the-Middle Attacks Detection over Internet of Things Environments based on Message Queuing Telemetry Transport,” Expert Systems, vol. 41, no. 2, pp. 1-15, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Morgan Reece et al., “Defending Multi-Cloud Applications Against Man-in-the-Middle Attacks,” Proceedings of the 29^th ACM Symposium on Access Control Models and Technologies, Association for Computing Machinery, New York, NY, United States, pp. 47-52, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Asha Varma Songa, and Ganesh Reddy Karriu, “An Integrated SDN Framework for early Detection of DDoS Attacks in Cloud Computing,” Journal of Cloud Computing, vol. 13, no. 1, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Mahdi Nsaif Jasim, and Methaq Talib Gaata, “K-Means Clustering-based Semi-Supervised for DDoS Attacks Classification,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 6, pp. 3570-3576, 2022.
[Google Scholar]

[15] Haya Alubaidan et al., “DDoS Detection in Software-Defined Network using Machine Learning,” International Journal of Cybernetics and Informatics, vol. 12, no. 4, pp. 93-104, 2023.
[Google Scholar]

[16] Lal Mohan Pattnaik et al., “Cloud DDoS Attack Detection Model with Data Fusion and Machine Learning Classifiers,” EAI Endorsed Transactions on Scalable Information Systems, vol. 10, no. 6, pp. 1-9, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Gottapu Sankara Rao, and P. Krishna Subbarao, “A Novel Framework for Detection of DoS/ DDoS Attack using Deep Learning Techniques,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 1, pp. 450-466, 2024.
[Google Scholar] [Publisher Link]

[18] Furqan Rustam et al., “Denial of Service Attack Classification using Machine Learning with Multi-Features,” Electronics, vol. 11, no. 22, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Yisroel Mirsky et al., “Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection,” arXiv, pp. 1-15, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Ashley Woodiss-Field, Michael N. Johnstone, and Paul Haskell-Dowland, “Examination of Traditional Botnet Detection on IoT-based Bots,” Sensors, vol. 24, no. 3, pp. 1-25, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[21] Shamshair Ali et al., “A Novel Approach of Botnet Detection using Hybrid Deep Learning for Enhancing Security in IoT Networks,” Alexandria Engineering Journal, vol. 103, pp. 88-97, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[22] S.K. Khaja Shareef et al., “Enhanced Botnet Detection in IoT Networks using Zebra Optimization and Dual-Channel GAN Classification,” Scientific Reports, vol. 14, no. 1, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Swapna Thota, and D. Menaka, “Botnet Detection in the Internet-of-Things Networks using Densenet-Binary Moth Flame Optimization,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 20S, pp. 647-656, 2024. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/5195

[24] Mohammad Al-Fawa’reh et al., “MalBot-DRL: Malware Botnet Detection using Deep Reinforcement Learning in IoT Networks,” IEEE Internet of Things Journal, vol. 11, no. 6, pp. 9610-9629, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Muhammad Aamir, and Syed Mustafa Ali Zaidi, “Clustering based Semi-Supervised Machine Learning for DDoS Attack Classification,” Journal of King Saud University-Computer and Information Sciences, vol. 33, no. 4, pp. 436-446, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Tuğba Aytaç, Muhammed Ali Aydın, and Abdül Halim Zaim, “Detection DDoS Attacks using Machine Learning Methods,” Electrica, vol. 20, no. 2, pp. 159-167, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Sabah Alzahrani, and Liang Hong, “A Survey of Cloud Computing Detection Techniques Against DDoS Attacks,” Journal of Information Security, vol. 9, no. 1, pp. 45-69, 2017.
 [CrossRef] [Google Scholar] [Publisher Link]

[28] Abdel-Rahman Al-Ghuwairi et al., “Intrusion Detection in Cloud Computing based on Time Series Anomalies Utilizing Machine Learning,” Journal of Cloud Computing, vol. 12, no. 1, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[29] Abdulaziz Almazyad, Laila Halman, and Alaa Alsaeed, “Probe Attack Detection using an Improved Intrusion Detection System,” Computers, Materials and Continua, vol. 74, no. 3, pp. 4770-4784, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[30] Waheed G. Gadallah, Nagwa M. Omar, and Hosny M. Ibrahim, “Machine Learning-based Distributed Denial of Service Attacks Detection Technique using New Features in Software-Defined Networks,” International Journal of Computer Network and Information Security, vol. 13, no. 3, pp. 15-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[31] Hüseyin Polat et al., “A Novel Approach for Accurate Detection of the DdoS Attacks in SDN-based SCADA Systems based on Deep Recurrent Neural Networks,” Expert Systems with Applications, vol. 197, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[32] N. Sivasankari, and S. Kamalakannan, “Fuzzy Logic-based Man-in-the-Middle Attack Detection and Improving Routing Efficiency in the IoT Network,” 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), Dharwad, India, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[33] Metehan Gelgi et al., “Systematic Literature Review of IoT Botnet DDoS Attacks,” Sensors, vol. 24, no. 11, pp. 1-37, 2024.
[CrossRef] [Google Scholar] [Publisher Link] 

[34] Segun I. Popoola et al., “Federated Deep Learning for Zero-Day Botnet Attack Detection in IoT Edge Devices,” IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3930-3944, 2022.
[CrossRef] [Google Scholar] [Publisher Link]