A Review on Machine Learning Techniques for DDoS Attack Detection in IoT
Apoorva Gupta, Ojasvi Tyagi, Vanshika Uniyal, Shiva Singhal, Vivekanand Jha · 2022
In today’s era, the demand for global connectivity has made researchers establish such a wireless network that provides around the clock connectivity for information exchange and maximum coverage. Here, the coverage involves both large and small-scale organizations. This coverage has given a chance to malicious practitioners to steal sensitive information by performing network attacks. Amongst this large pool of network attacks, this paper identifies that, the distributed denial of service (DDoS) attack is such an attack that goes undetected. In recent years, various researchers have come up with different solutions to detect this attack. So, this paper reviews the existing solutions proposed by the research community to detect such attacks using modern technology such as deep learning and supervised machine learning techniques. Further, this paper performs the comparative analysis of the existing techniques in terms of accuracy, where deep learning models were found to be effective in detecting such attacks.