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.

<p>A common approach to leverage software vulnerabilities in the contemporary operating system has been the Return-Oriented Programming(ROP) attack. Although protection mechanisms are involved in the OS, an attacker may execute arbitrary code with the support of ROP. A decade ago, Return Oriented programming was designed to solve the buffer overflow exploit security mechanisms such as ASLR, DEP (or W?X) by reusing the machine code in the form of gadgets that are stitched together to render a full assault on Turing. And it will take more complex efforts to conduct a Turing complete attack, and very little data is possible to perform it with raw input. Therefore, in this project, we are systematizing the interpretation of the new findings that can be used to carry out a full ROP attack with the help of pwntools python library.</p>