Detecting Attacks and Locating Malicious Devices Using Unmanned Air Vehicles and Machine Learning

• Main Author: C. Júnior, Evilasio
• Publication Date: 2022
• Other Authors: L. Costa, Wanderson, L. C. Portela, Ariel, S. Rocha, Leonardo, L. Gomes, Rafael, M. C. Andrade, Rossana
• Format: Article
• Language: eng
• Source: Journal of internet services and applications (Internet)
• Download full: https://journals-sol.sbc.org.br/index.php/jisa/article/view/2327
• topic: Location; Detection; Machine Learning; Unmanned Aerial Vehicles

Internet access in both private and public environments allows users to broadly access their data what makes possible the deployment of new services based on Internet of Things. This fact created Smart Environments (SEs) that are composed of a huge amount of heterogeneous devices, for example, personal devices (smartphones, notebooks, tablets, etc) and IoT devices (sensors, actuators, and others). However, these environments can facilitate the action of malicious agents interested in promoting Distributed Denial of Service (DDoS) attacks to the network, and, when they are public places, it is challenging to locate these attackers. In this way, it is necessary to deploy solutions that can detect DDoS in SEs and to determine the physical location of the attacker, which is essential to prevent future attacks. Within this context, this article presents an Intelligent System for detection of DDoS and physical location of devices in SEs, applying Machine Learning (ML) and trilateration techniques. The experiments performed, using real network traffic and simulation, suggest that the proposed system is capable of detecting attacks and finding malicious devices.