Intrusion Detection System (IDS) is a tool, or software application, that monitors network or system activity and detects malicious activity occurring. The protected evolution of the network must incorporate new threats and related approaches to avoid these threats. The key role of the IDS is to secure resources against the attacks. Several approaches, methods and algorithms of the intrusion detection help to detect a plethora of attacks. The main objective of this paper is to provide a complete system to detect intruding attacks using the Machine Learning technique which identifies the unknown attacks using the past information gained from the known attacks. The paper explains preprocessing techniques, model comparisons for training as well as testing, and evaluation technique.

Computer networks went through massive and rapid growth over the last few decades and with that growth have come severe congestion problems; basically, when the packet sending rate is increased than the receiving rate then congestion arises example given, it is often observed that internet gateways drop about 10% of the incoming packets due to overflow of local buffers and much of the time these problems are caused due to transport protocol implementations (not in the protocols themselves): The "obvious" ways to implement a window-based transport protocol can result in exactly the wrong behaviour in response to network congestion.