DoS Attack Detection Using Machine Learning and Neural Network
Shreekh Wankhede, Deepak D. Kshirsagar · 2018
The current digital world is using the internet almost everywhere. The usage of internet has been increasing, however, threats are also increasing in numbers. One such threat is DoS attack which uses reasonable service requests to gain excessive computing and network resources and results in an inability to access them by legitimate users. The DoS attack can happen at different layers of OSI model such as network, transport and application layers. The aim of this paper is to detect DoS attack effectively using Machine learning (ML) and Neural Network (NN) algorithms. The detection is specifically focused on application layer DoS attack detection rather than at transport and network DoS attack detection. The latest DoS attack dataset CIC IDS 2017 dataset is used in the experiment. The experimentation has divided the dataset into different splits and the best split is found for each algorithm i.e. RF and MLP. Results of RF and MLP are compared and it is shown that RF provides better results than MLP.