Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation Testbed for SDN-Enabled IoT Devices
Kiruthikan Sritharan, Rojeemeeharan Elagumeeharan, Senthuran Nakkeeran, Agaar Mohamed, Binura Ganegoda, Kanishka Yapa · 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) · 2022
One of the most important innovations in contemporary technology since the 1980s is the Internet of Things [1]. One of the most popular current technologies today is the Internet of Things (IoT), which enables various devices to communicate with one another, exchange data, and be controlled by other devices that have been granted authentication. As these sophisticated technologies were created and developed, cyber threats against them also grew significantly. Since devices may be controlled if a network or a specific device is penetrated by an attacker, there are particularly significant hazards to the IoT environment. The downside is that because IoT devices are connected to local networks, if an attacker gained access to a weak device, the entire local network will be compromised, exposing important data and disrupting operations. One of the treatments for DDoS attacks on IoT devices is the topic of our research. To be more precise, our research suggests an Automated IoT Vulnerability Testbed for DDoS Attacks in an SDN environment, constructed using the Python programming language and numerous machine learning techniques. The network traffic that is coming to the IoT devices will be analyzed as part of our suggested solution, which will help to thwart successful DDoS attacks.