Cyberattack Detection on IoT Devices in the Context of Large Data Volumes and Network Complexity: Cyberattack Detection on IoT Devices in the Context of Large Data Volumes and Network Complexity

Mochamad Fachrudin Zikrullah; Dr TUKIYAT, M.Si; Dr MURNI HANDAYANI, S.Si., M.Sc

Authors

Mochamad Fachrudin Zikrullah Universitas Pamulang
Tukiyat
Murni Handayani

Keywords:

IoT, cybersecurity, Deteksi Serangan Cyber, Machine Learning, Deep learning, Algoritma Asosiasi, Algoritma FP-Growth, Algoritma Apriori, Algoritma ECLAT, Dataset CIC IOT 2023, Internet of Things, Serangan Cyber

Abstract

The Internet of Things (IoT) has become an essential part of everyday life, enabling devices to communicate and work together seamlessly, boosting productivity, efficiency, and convenience across various domains such as healthcare, transportation, manufacturing, and smart homes. However, as IoT adoption grows rapidly, so do the challenges related to cybersecurity. The vast amounts of data generated by these devices and the increasing complexity of IoT networks create vulnerabilities that cybercriminals are quick to exploit. Factors like the diversity of IoT devices, differing communication protocols, and inconsistent security standards only add to the problem. Cyberattacks such as Distributed Denial of Service (DDoS), malware, and data sniffing are becoming increasingly sophisticated, threatening the security and functionality of IoT ecosystems. To combat these issues, it is crucial to develop robust and adaptive methods that can detect and mitigate these threats in real-time. This paper reviews current methods for detecting cyberattacks on IoT devices, with a focus on integrating machine learning, data analytics, and blockchain technologies. Traditional rule-based systems, while effective against known threats, struggle to keep up with the complexity and ever-evolving nature of modern cyberattacks. Machine learning techniques, especially deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, have shown exceptional capabilities in analyzing large datasets to identify patterns and anomalies. Additionally, blockchain technology offers enhanced security through its decentralized and tamper-resistant nature, ensuring data integrity across IoT networks. The study explores IoT-related threats, discusses methodologies to counter them, and presents case studies to highlight the practical application of these advanced techniques. It emphasizes the need for scalable, efficient, and adaptable solutions to secure IoT ecosystems against the growing sophistication of cyber threats.

References

[1] F. Alwahedi, A. Aldhaheri, M. A. Ferrag, A. Battah, and N. Tihanyi, “Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models,” Internet of Things and Cyber-Physical Systems, vol. 4, pp. 167–185, Jan. 2024, doi: 10.1016/J.IOTCPS.2023.12.003.

[2] A. Bhardwaj, S. Bharany, A. W. Abulfaraj, A. Osman Ibrahim, and W. Nagmeldin, “Fortifying home IoT security: A framework for comprehensive examination of vulnerabilities and intrusion detection strategies for smart cities,” Egyptian Informatics Journal, vol. 25, no. December 2023, p. 100443, 2024, doi: 10.1016/j.eij.2024.100443.

[3] S. K. Dash et al., “Enhancing DDoS attack detection in IoT using PCA,” Egyptian Informatics Journal, vol. 25, no. August 2023, p. 100450, 2024, doi: 10.1016/j.eij.2024.100450.

[4] A. Alzahrani and M. Z. Asghar, “Cyber vulnerabilities detection system in logistics-based IoT data exchange,” Egyptian Informatics Journal, vol. 25, no. September 2023, p. 100448, 2024, doi: 10.1016/j.eij.2024.100448.

[5] M. Alazab, R. Abu Khurma, P. A. Castillo, B. Abu-Salih, A. Martín, and D. Camacho, “An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron,” Egyptian Informatics Journal, vol. 25, no. December 2023, p. 100423, 2024, doi: 10.1016/j.eij.2023.100423.

[6] H. Harb et al., “An intelligent optimization strategy for nurse-patient scheduling in the Internet of Medical Things applications,” Egyptian Informatics Journal, vol. 25, no. August 2023, p. 100451, 2024, doi: 10.1016/j.eij.2024.100451.

[7] B. Amma N.G., “En-RfRsK: An ensemble machine learning technique for prognostication of diabetes mellitus,” Egyptian Informatics Journal, vol. 25, no. March 2023, p. 100441, 2024, doi: 10.1016/j.eij.2024.100441.

[8] S. Asaithambi, L. Ravi, M. Devarajan, A. S. Almazyad, G. Xiong, and A. W. Mohamed, “Enhancing enterprises trust mechanism through integrating blockchain technology into e-commerce platform for SMEs,” Egyptian Informatics Journal, vol. 25, no. December 2023, p. 100444, 2024, doi: 10.1016/j.eij.2024.100444.

[9] J. K. Adeniyi et al., “A biometrics-generated private/public key cryptography for a blockchain-based e-voting system,” Egyptian Informatics Journal, vol. 25, no. December 2023, p. 100447, 2024, doi: 10.1016/j.eij.2024.100447.

[10] S. Sadhwani, U. K. Modi, R. Muthalagu, and P. M. Pawar, “SmartSentry: Cyber Threat Intelligence in Industrial IoT,” IEEE Access, vol. 12, no. December 2023, pp. 34720–34740, 2024, doi: 10.1109/ACCESS.2024.3371996.

[11] L. L. Dhirani, E. Armstrong, and T. Newe, “Industrial iot, cyber threats, and standards landscape: Evaluation and roadmap,” Sensors, vol. 21, no. 11, pp. 1–30, 2021, doi: 10.3390/s21113901.

[12] E. Dolan and R. Widayanti, “Implementation of Authentication Systems on Hotspot Network Users to Improve Computer Network Security,” International Journal of Cyber and IT Service Management, vol. 2, no. 1, pp. 88–94, 2022, doi: 10.34306/ijcitsm.v2i1.93.

[13] A. B. Li, H. Chen, and X. L. Xie, “Visible watermarking for 3D models based on 3D Boolean operation,” Egyptian Informatics Journal, vol. 25, no. December 2023, 2024, doi: 10.1016/j.eij.2023.100436.

[14] Z. Guo, H. Li, and K. Li, “Dual subpopulation artificial bee colony algorithm based on individual gradation,” Egyptian Informatics Journal, vol. 25, no. September 2023, p. 100452, 2024, doi: 10.1016/j.eij.2024.100452.

[15] W. Li, H. Li, Y. Wang, and Y. Han, “Optimizing flexible job shop scheduling with automated guided vehicles using a multi-strategy-driven genetic algorithm,” Egyptian Informatics Journal, vol. 25, no. December 2023, p. 100437, 2024, doi: 10.1016/j.eij.2023.100437.

[16] R. D. Hapsari and K. G. Pambayun, “ANCAMAN CYBERCRIME DI INDONESIA: Sebuah Tinjauan Pustaka Sistematis,” Jurnal Konstituen, vol. 5, no. 1, pp. 1–17, Oct. 2023, doi: 10.33701/jk.v5i1.3208.

[17] S. Mishra, A. Albarakati, and S. K. Sharma, “Cyber Threat Intelligence for IoT Using Machine Learning,” Processes, vol. 10, no. 12, 2022, doi: 10.3390/pr10122673.

[18] F. Guo and H. Li, “A niche-based evolutionary algorithm with dual cooperative archive for solving constrained multi-objective optimization problems,” Egyptian Informatics Journal, vol. 25, no. September 2023, p. 100422, 2024, doi: 10.1016/j.eij.2023.100422.

[19] M. Zhu, J. Jiang, and W. Gao, “A fast ADMM algorithm for sparse precision matrix estimation using lasso penalized D-trace loss,” Egyptian Informatics Journal, vol. 25, no. December 2023, p. 100425, 2024, doi: 10.1016/j.eij.2023.100425.

[20] W. Xiong, D. Zhu, R. Li, Y. Yao, C. Zhou, and S. Cheng, “An effective method for global optimization – Improved slime mould algorithm combine multiple strategies,” Egyptian Informatics Journal, vol. 25, no. September 2023, p. 100442, 2024, doi: 10.1016/j.eij.2024.100442.

[21] F. H. Almukhtar, S. Wahhab Kareem, and F. Sami Khoshaba, “Design and development of an effective classifier for medical images based on machine learning and image segmentation,” Egyptian Informatics Journal, vol. 25, no. March 2023, p. 100454, 2024, doi: 10.1016/j.eij.2024.100454.

[22] A. Neelkanth Chaudhari, “Cyber Physical Recommender Systems for IoT Based Applications,” American Journal of Science, Engineering and Technology, vol. 5, no. 2, p. 82, 2020, doi: 10.11648/j.ajset.20200502.14.

[23] H. M. Reeve, A. M. Mescher, and A. F. Emery, “Experimental and numerical investigation of polymer preform heating,” American Society of Mechanical Engineers, Heat Transfer Division, (Publication) HTD, vol. 369, no. 6, pp. 321–332, 2001, doi: 10.1115/imece2001/htd-24365.

[24] Z. Li et al., “A Cyber-Physical Traffic Signaling System for Controlled Waterway in Inland River Based on Edge-centric IoT A Cyber-Physical Tra ffi c Signaling System for Controlled Waterway in Inland River Based on Edge-centric IoT,” 2023.

[25] M. Es-sabry et al., “An efficient 32-bit color image encryption technique using multiple chaotic maps and advanced ciphers,” Egyptian Informatics Journal, vol. 25, no. September 2023, p. 100449, 2024, doi: 10.1016/j.eij.2024.100449.

[26] S. Kumari, V. Tulshyan, and H. Tewari, “Cyber Security on the Edge: Efficient Enabling of Machine Learning on IoT Devices,” Information (Switzerland), vol. 15, no. 3, pp. 1–28, 2024, doi: 10.3390/info15030126.

[27] N. Wirtz et al., Securing CEI “By-Design.” 2021. doi: 10.1561/9781680836875.ch14.

Cyberattack Detection on IoT Devices in the Context of Large Data Volumes and Network Complexity