Detecting Security threats in the Router using Computational Intelligence

TL;DR

This paper proposes a machine learning-based framework for detecting DoS attacks at routers by identifying relevant network features and developing accurate classifiers, enhancing network security measures.

Contribution

It introduces a novel knowledge discovery framework utilizing machine learning to detect DoS attacks at routers, focusing on feature selection and classifier development.

Findings

High detection accuracy achieved with kernel machine and soft computing methods

Identification of key network features relevant to DoS attack detection

Effective detection of both known and novel DoS attacks

Abstract

nformation security is an issue of global concern. As the Internet is delivering great convenience and benefits to the modern society, the rapidly increasing connectivity and accessibility to the Internet is also posing a serious threat to security and privacy, to individuals, organizations, and nations alike. Finding effective ways to detect, prevent, and respond to intrusions and hacker attacks of networked computers and information systems. This paper presents a knowledge discovery frame work to detect DoS attacks at the boundary controllers (routers). The idea is to use machine learning approach to discover network features that can depict the state of the network connection. Using important network data (DoS relevant features), we have developed kernel machine based and soft computing detection mechanisms that achieve high detection accuracies. We also present our work of identifying DoS pertinent features and evaluating the applicability of these features in detecting novel DoS attacks. Architecture for detecting DoS attacks at the router is presented. We demonstrate that highly efficient and accurate signature based classifiers can be constructed by using important network features and machine learning techniques to detect DoS attacks at the boundary controllers.