An Ensemble Learning Based Multi-Level Network Intrusion Detection System for Wi-Fi Dominant Networks

Francisco D Vaca, Purdue University

Abstract

Today, networks contribute significantly to everyone’s life. The enormous usefulness of networks for various services and data storage motivates adversaries to launch attacks on them. Network Intrusion Detection Systems (NIDSs) are used as security measure inside the organizational networks to identify any intrusions and generate alerts for them. The idea of deploying an NIDS is quite known and has been studied and adopted in both academia and industry. However, most of the NIDS literature have emphasized to detect the attacks that originate externally in a wired network infrastructure. In addition, Wi-Fi and wired networks are treated the same for the NIDSs. The open infrastructure in Wi-Fi network makes it different from the wired network. Several internal attacks that could happen in a Wi-Fi network are not possible in a wired network. The NIDSs developed using traditional approaches may fail to identify these internal attacks. The thesis work attempts to develop a Multi-Level Network Intrusion Detection System (ML-NIDS) for Wi-Fi dominant networks that can detect internal attacks specific to Wi-Fi networks as well as the generic network attacks that are independent of network infrastructure. In Wi-Fi dominant networks, Wi-Fi devices (stations) are prevalent at the edge of campus and enterprise networks and integrated with the fixed wired infrastructure at the access. The implementation is proposed for Wi-Fi dominant networks; nevertheless, it aims to work for the wired network as well. We develop the ML-NIDS using an ensemble learning method that combines several weak learners to create a strong learner.

Degree

M.Sc.

Advisors

Niyaz, Purdue University.

Subject Area

Design|Statistics|Communication|Artificial intelligence|Computer science|Economics|Electrical engineering|Information Technology|Management|Mass communications|Web Studies

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