Authors :
Hakizimana Jean d’Amour; Dr. Wilson Musoni
Volume/Issue :
Volume 10 - 2025, Issue 3 - March
Google Scholar :
https://tinyurl.com/2s3evk9a
Scribd :
https://tinyurl.com/48jnyy2f
DOI :
https://doi.org/10.38124/ijisrt/25mar1471
Google Scholar
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Abstract :
The increasing proliferation of Internet of Things (IoT) devices has led to significant security concerns, primarily
due to their simple internal structures and energy-efficient designs, which prioritize compactness. With billions of IoT devices
currently in use worldwide, their sheer volume presents a substantial risk. These devices often come with hardware and
software limitations, as they are designed for convenience, mass production, and cost-effectiveness, with security typically
being a secondary consideration. The rapid expansion of IoT devices makes it increasingly challenging to monitor and address
their vulnerabilities. This paper explores the prevalent security risks, attacks, and inherent weaknesses of IoT devices, along
with the challenges of protecting them against emerging cyber threats. Since IoT devices frequently serve as entry points to
other interconnected systems and are prone to exploitation for botnet formation or man-in-the-middle attacks, they are
attractive targets for cybercriminals. The paper also outlines various methods of remediation and mitigation, such as
implementing robust access control systems, adopting secure communication protocols, and ensuring timely updates and
patches. By gaining a deeper understanding of the security challenges facing IoT devices and applying effective mitigation
strategies, individuals and organizations can better protect their connected devices and networks, ensuring their safety,
privacy, and security.
Keywords :
Internet of Things (IoT), Botnets, Distributed Denial of Service (DDoS), Threat Mitigation, Detection Methods.
References :
- Alhammadi, N. (2021). Review of the common DDoS attack.
- Andersen, M. F. (2022). Detecting malware and cyber-attacks using ISP data. https://doi.org/10.54337/aau483028127
- Bezerra, V. H., Da Costa, V. G. T., Barbon, S., Junior, Miani, R. S., & Zarpelão, B. B. (2019). IOTDS: A one-class classification approach to detect botnets in Internet of Things devices. Sensors, 19(14), 3188. https://doi.org/10.3390/s19143188
- CUJO AI. (2023). The 2022–2023 IoT Botnet Report. Retrieved from https://www.mdpi.com/1424-8220/24/11/3571
- CyberPress. (2025, January). New IoT Botnet Launching Large-Scale DDoS Attacks Hijacking IoT Devices. Retrieved from https://cyberpress.org/new-iot-botnet-launching-large-scale-ddos-attacks/
- CyberSec Sentinel. (2025, January). Matrix Botnet Exploits IoT Devices for Widespread DDoS Attacks. Retrieved from https://cybersecsentinel.com/matrix-botnet-exploits-iot-devices-for-widespread-ddos-attacks/
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- Iwuanyanwu, U., Oyewole, O. O., Fakeyede, O. G., Okeleke, E. C., & Apeh, A. J. (2023). IoT device security risks: A comprehensive overview and mitigation strategies. Deleted Journal, 3(1), 38–43. https://doi.org/10.26480/jtin.01.2023.38.43
- Khan, S. A., Li, Z., Jung, W., Feng, Y., Zhao, D., Xin, C., & Zhou, G. (2024). DeepShield: Lightweight privacy-preserving inference for real-time IoT botnet detection. 2024 IEEE 37th International System-on-Chip Conference (SOCC), 1–6.
- Kulbacki, M., Chaczko, Z., Barton, S. K., Wajs-Chaczko, P., Nikodem, J., Rozenblit, J. W., Klempous, R., Ito, A., & Kulbacki, M. (2024). A review of the weaponization of IoT: Security threats and countermeasures. https://doi.org/10.1109/saci60582.2024.10619778
- Mali, K. (2020, October 3). Speedcast: IoT devices expected to grow to 75 billion by 2025. TechGraph. https://techgraph.co/tech/speedcast-iot-devices-expected-to-grow-to-75-billion-by-2025/
- Ministry of Information Technology and Communications, Republic of Rwanda. (2024). National Cybersecurity Strategy 2024-2029. Retrieved from https://dig.watch/resource/national-cybersecurity-strategy-of-the-republic-of-rwanda-2024-2029
- Mohammed, K. A., Wael, E., & Mhd, S. (2023, July 12). Securing IoT devices against emerging security threats: Challenges and mitigation techniques. Published online: 12 Jul 2023, 3–20.
- Regan, C., Nasajpour, M., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Choo, K. R. (2022). Federated IoT attack detection using decentralized edge data. Machine Learning With Applications, 8, 100263. https://doi.org/10.1016/j.mlwa.2022.100263
- Staal, T., & Staal, T. (2022). The impact of the Internet of Things on the demand of cloud resources. The Netherlands: 2022.
The increasing proliferation of Internet of Things (IoT) devices has led to significant security concerns, primarily
due to their simple internal structures and energy-efficient designs, which prioritize compactness. With billions of IoT devices
currently in use worldwide, their sheer volume presents a substantial risk. These devices often come with hardware and
software limitations, as they are designed for convenience, mass production, and cost-effectiveness, with security typically
being a secondary consideration. The rapid expansion of IoT devices makes it increasingly challenging to monitor and address
their vulnerabilities. This paper explores the prevalent security risks, attacks, and inherent weaknesses of IoT devices, along
with the challenges of protecting them against emerging cyber threats. Since IoT devices frequently serve as entry points to
other interconnected systems and are prone to exploitation for botnet formation or man-in-the-middle attacks, they are
attractive targets for cybercriminals. The paper also outlines various methods of remediation and mitigation, such as
implementing robust access control systems, adopting secure communication protocols, and ensuring timely updates and
patches. By gaining a deeper understanding of the security challenges facing IoT devices and applying effective mitigation
strategies, individuals and organizations can better protect their connected devices and networks, ensuring their safety,
privacy, and security.
Keywords :
Internet of Things (IoT), Botnets, Distributed Denial of Service (DDoS), Threat Mitigation, Detection Methods.