A new defense mechanism against smishing attacks using gray wolf optimizer

Dissertant

al-Samirrai, Marwan Hamid Shakir

Thesis advisor

al-Fayyumi, Muhammad Ahmad

University

Isra University

Faculty

Faculty of Information Technology

Department

Department Software Engineering

University Country

Jordan

Degree

Master

Degree Date

2020

English Abstract

Recently, the phishing attack is one of the critical threats against Organizations, internet users, service providers, cloud computing, and many other fields in daily life.

In the phishing attack, the intruder attempts to defraud the users and leak or steal the credential information, including personal information such as bank account, passwords, etc., by sending a fooled email or SMS to redirect the user to an untrusted website.

Various methods have been proposed in terms of filtering and detect different types of phishing attacks; however, the researchers and security information experts are still studying to find a solution to assure the internet security from phishing and other attacks.

Viewing SMS phishing messages are mostly short text and become a relatively low number associated with legitimate messages, new features for quick writing, and oversampling technique for imbalanced data utilized to SMS phishing detection.

In this research, a novel framework of the SMS phishing detection presented.

The proposed method combines feature extraction, oversampling, optimization algorithm for feature selection and classification.

For the feature extraction and classification, the Support vector machine is implemented.

In addition, the Adaptive Synthetic Sampling Approach method used to be an oversampling method.

Then, the Binary Gray Wolf Optimizer Algorithm (BGWO) is applied to analyze the extracted features and select the optimal sequence of all the features.

Experimental results show that the BGWO approach enhances the accuracy of SMS phishing detection system.

The proposed method in this thesis achieves the best accuracy with 99.25% by using only an average of 87.4 of features.

The results demonstrate that the proposed method has a promising performance in detecting the SMS phishing messages.

Main Topic

Information Technology and Computer Science

No. of Pages

65

Table of Contents

Table of contents.

Abstract.

Chapter One : Introduction.

Chapter Two : Attacks on SMS messages.

Chapter Three : Related work.

Chapter Four : Methodology.

Chapter Five : Experiments and results.

Chapter Six : Conclusion and future work.

References.

American Psychological Association (APA)

al-Samirrai, Marwan Hamid Shakir. (2020). A new defense mechanism against smishing attacks using gray wolf optimizer. (Master's theses Theses and Dissertations Master). Isra University, Jordan
https://search.emarefa.net/detail/BIM-988692

Modern Language Association (MLA)

al-Samirrai, Marwan Hamid Shakir. A new defense mechanism against smishing attacks using gray wolf optimizer. (Master's theses Theses and Dissertations Master). Isra University. (2020).
https://search.emarefa.net/detail/BIM-988692

American Medical Association (AMA)

al-Samirrai, Marwan Hamid Shakir. (2020). A new defense mechanism against smishing attacks using gray wolf optimizer. (Master's theses Theses and Dissertations Master). Isra University, Jordan
https://search.emarefa.net/detail/BIM-988692

Language

English

Data Type

Arab Theses

Record ID

BIM-988692