Muntezar Rabbani

Work place: Department of Computer Science and Engineering, Faculty of science and technology, American International University-Bangladesh (AIUB), Dhaka, Bangladesh



Research Interests:


Muntezar Rabbani is currently pursuing a Master of Science degree in the Computer Science program from American International University – Bangladesh (AIUB). With research interests and experience in the emerging fields of Data Science and Artificial Intelligence, he contributes his knowledge and passion for these subjects through his role as an Instructor at AIUB.

Author Articles
Comparative Analysis of Threat Detection Techniques in Drone Networks

By Syed Golam Abid Muntezar Rabbani Arpita Sarker Tasfiq Ahmed Rafi Dip Nandi

DOI:, Pub. Date: 8 Jun. 2024

With the rapid proliferation of drones and drone networks across various application domains, ensuring their security against cyber threats has become imperative. This paper presents a comprehensive analysis and comparative analysis of the state-of-the-art techniques for detecting cyber threats in drone networks. The background provides a primer on drones, networks, drone network architectures, communication mechanisms, and enabling technologies like wireless protocols, satellite navigation, onboard computers, sensors, and flight control systems. The landscape of emerging technologies including blockchain, software-defined networking, machine learning, fog computing, ad-hoc networks, and swarm intelligence is reviewed in the context of transforming drone network capabilities while also introducing potential vulnerabilities. The paper delves into common cyber threats faced by drone networks such as hacking, DoS attacks, data breaches, and GPS spoofing. A detailed literature review of proposed threat detection techniques is provided, categorized into machine learning, multi-agent systems, blockchain, intrusion detection systems, software solutions, and miscellaneous methods. A key gap identified is handling increasingly sophisticated attacks, complex environments, and resource limitations in aerial platforms. The analysis highlights accuracy, overhead and real-time trade-offs between techniques, while factors like model optimization can influence efficacy. A comparative analysis highlights the advantages and limitations of each approach considering metrics like accuracy, scalability, flexibility, and overhead. Key observations include the trade-offs between computational complexity and real-time performance, the challenges in handling evolving attack techniques, and the dependencies between detection accuracy and factors like model selection and training data quality. The analysis provides a comprehensive reference for cyber threat detection in drone networks, benefiting researchers and practitioners aiming to advance this crucial area of drone security through robust detection systems tailored for resource-constrained aerial environments.

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