Detection of Multi-Vector DDoS Attack - Brahma, Kunal Kumar; Sarmah, Satyajit; Kalita, Chandan; Ghosh, Rajdeep (International Journal of Computer Sciences and Engineering, 2019)
In this current technology driven society, internet has become a basic commodity for every individuals as well as organization. Due to the rapid increase of internet dependency of government offices, private company, or corporate sectors, security has become the main concern in all of these organizations. Attack over the network using stochastic approaches has created large chaos. The DDoS attack has created destruction and damages over the network since early 2000’s. DDoS is known for its ability to fade the identity of the source of attack because of multiple address and flooding mechanism. Preventing the attack from its original source is quite difficult. This floods the whole system making the system of the particular sector to be crippled and can be remedied by early detection of the attack. In this work we try to detect the different DDoS attack vectors and classify it. The nature and its mechanism are studied to identify the type of attack. We use scikit learn, a machine learning approach to detect different forms of attacks.