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INSTANT MESSAGING SPAM DETECTION IN LONG TERM EVOLUTION NETWORKS

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INSTANT MESSAGING SPAM DETECTION IN LONG TERM EVOLUTION NETWORKS

Das, Swagata (2013) INSTANT MESSAGING SPAM DETECTION IN LONG TERM EVOLUTION NETWORKS. Masters thesis, Concordia University.

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Abstract

The lack of efficient spam detection modules for packet data communication is resulting to increased threat exposure for the telecommunication network users and the service providers. In this thesis, we propose a novel approach to classify spam at the server side by intercepting packet-data communication among instant messaging applications. Spam detection is performed using machine learning techniques on packet headers and contents (if unencrypted) in two different phases: offline training and online classification. The contribution of this study is threefold. First, it identifies the scope of deploying a spam detection module in a state-of-the-art telecommunication architecture. Secondly, it compares the usefulness of various existing machine learning algorithms in order to intercept and classify data packets in near real-time communication of the instant messengers. Finally, it evaluates the accuracy and classification time of spam detection using our approach in a simulated environment of continuous packet data communication. Our research results are mainly generated by executing instances of a peer-to-peer instant messaging application prototype within a simulated Long Term Evolution (LTE) telecommunication network environment. This prototype is modeled and executed using OPNET network modeling and simulation tools. The research produces considerable knowledge on addressing unsolicited packet monitoring in instant messaging and similar applications.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Das, Swagata
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:15 August 2013
Thesis Supervisor(s):Debbabi, Mourad and Pourzandi, Makan
Keywords:Classification, LTE, Machine Learning, Mobile Network, SPIM
ID Code:977830
Deposited By: SWAGATA DAS
Deposited On:25 Nov 2013 19:51
Last Modified:18 Jan 2018 17:45

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