Volume 14 , Issue 1 , PP: 64-78, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Abdullah Al-Fuwaiers 1 * , Shailendra Mishra 2
Doi: https://doi.org/10.54216/JCIM.140105
The integration of drones into various industries brings about cybersecurity challenges due to their reliance on internet connectivity. To address this, we propose a comprehensive cybersecurity architecture leveraging machine learning (ML) algorithms and Internet of Things (IoT) technologies within the Internet of Drones (IoD) framework. Our architecture employs IoT-enabled sensors strategically placed across the drone ecosystem to collect and analyze data on system behaviors, communication patterns, and environmental variables. This data is then processed by a centralized platform equipped with sophisticated ML algorithms for pattern identification and anomaly detection. A key feature is the dynamic learning mechanism, enabling real-time intrusion detection by adapting to evolving threats. By combining IoT and ML, the system proactively defends against cyberattacks by distinguishing between typical and abnormal activity. Emphasis is placed on data integrity and confidentiality through secure communication protocols and cryptographic algorithms. Extensive simulations and tests validate the framework's effectiveness in various IoD scenarios, demonstrating its ability to swiftly identify intrusions and informing future enhancements. This comprehensive study meticulously examines the pressing cybersecurity concerns within the burgeoning drone industry. It proposes a robust architectural framework designed to enhance security for drone-enabled applications in our increasingly interconnected world. By harnessing the synergies between Internet of Things (IoT) and Machine Learning (ML) technologies, this innovative approach aims to fortify the integrity and reliability of drone systems.
Cyber security , IoT , Drone , neural networks , IDS , machine learning
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