An Algorithm to Prevent Distributed Denial-of-Service Attacks on the Internet of Things

The Internet, the most potent invention in human history, has historically spawned continuous technological innovation, one of which is the Internet of Things (IoT), which has become more favourable since it makes a smart life possible. The IoT has a footing in a wide range of modern electronics, including smartwatches, smartphones, smart televisions, and so forth. However, with any invention comes drawbacks. The IoT devices are vulnerable to all sorts of cyberattacks, particularly Distributed Denial-of-Service (DDoS) attacks, which become an overnight sensation and the preferred weapon of choice because of how they are characterized by their broadly conveyed nature and little assault estimate time from each source. Previously proposed solutions that are detailed and investigated in the literature review are not viable. Mainly because many researchers have proposed solutions that have high computational costs and require high-end performance devices, which is not an option because of limited resources such as the processor, memory, and the persistent connection within IoT devices. As a result, this paper proposes an algorithm that was designed using a Support Vector Machine (SVM), which is a supervised Machine Learning (ML) algorithm used in pattern recognition. Equally, for a robust solution, the Intrusion Detection Evaluation Dataset (CIC IDS) 2017 will be utilized to train the SVM algorithm. The dataset consists of labelled data of current DDoS attacks. In the future, we will present the simulation results.