Detecting Denial of Service Attacks (DoS) over the Internet of Drones (IoD) Based on Machine Learning
Albandari Alsumayt,
Naya Nagy,
Shatha Alsharyofi,
Noor Al Ibrahim,
Renad Al-Rabie,
Resal Alahmadi,
Roaa Ali Alesse,
Amal A. Alahmadi
Affiliations
Albandari Alsumayt
Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Naya Nagy
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Shatha Alsharyofi
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Noor Al Ibrahim
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Renad Al-Rabie
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Resal Alahmadi
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Roaa Ali Alesse
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
Amal A. Alahmadi
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
The use of Unmanned Aerial Vehicles (UAVs) or drones has increased lately. This phenomenon is due to UAVs’ wide range of applications in fields such as agriculture, delivery, security and surveillance, and construction. In this context, the security and the continuity of UAV operations becomes a crucial issue. Spoofing, jamming, hijacking, and Denial of Service (DoS) attacks are just a few categories of attacks that threaten drones. The present paper is focused on the security of UAVs against DoS attacks. It illustrates the pros and cons of existing methods and resulting challenges. From here, we develop a novel method to detect DoS attacks in UAV environments. DoS attacks themselves have many sub-categories and can be executed using many techniques. Consequently, there is a need for robust protection and mitigation systems to shield UAVs from DoS attacks. One promising security solution is intrusion detection systems (IDSs). IDs paired with machine learning (ML) techniques provide the ability to greatly reduce the risk, as attacks can be detected before they happen. ML plays an important part in improving the performance of IDSs. The many existing ML models that detect DoS attacks on UAVs each carry their own strengths and limitations.
