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

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

Jurnal Ilmu Komputer
Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)

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.