machine learning malware detection thesis

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Malware analysis and detection using machine learning algorithms.

1. Introduction

2. literature review, 3. research problem, 4. methodology, 4.1. dataset, 4.2. pre-processing, 4.3. features extraction, 4.4. features selection, 5. results and discussion, logistic regression, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Akhtar, M.S.; Feng, T. Malware Analysis and Detection Using Machine Learning Algorithms. Symmetry 2022 , 14 , 2304. https://doi.org/10.3390/sym14112304

Akhtar MS, Feng T. Malware Analysis and Detection Using Machine Learning Algorithms. Symmetry . 2022; 14(11):2304. https://doi.org/10.3390/sym14112304

Akhtar, Muhammad Shoaib, and Tao Feng. 2022. "Malware Analysis and Detection Using Machine Learning Algorithms" Symmetry 14, no. 11: 2304. https://doi.org/10.3390/sym14112304

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Title: Improving the Understanding of Malware using Machine Learning

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Title: malware detection using machine learning and deep learning.

Abstract: Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware. The velocity, volume, and the complexity of malware are posing new challenges to the anti-malware community. Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection. We have used opcode frequency as a feature vector and applied unsupervised learning in addition to supervised learning for malware classification. The focus of this tutorial is to present our work on detecting malware with 1) various machine learning algorithms and 2) deep learning models. Our results show that the Random Forest outperforms Deep Neural Network with opcode frequency as a feature. Also in feature reduction, Deep Auto-Encoders are overkill for the dataset, and elementary function like Variance Threshold perform better than others. In addition to the proposed methodologies, we will also discuss the additional issues and the unique challenges in the domain, open research problems, limitations, and future directions.

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Android malware detection using static analysis, machine learning and deep learning

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• Ahmad, Fawad
• Strathclyde Thesis Copyright
• University of Strathclyde
• Doctoral (Postgraduate)
• Doctor of Philosophy (PhD)
• Department of Computer and Information Sciences
• Android has been a dominant mobile operating system since 2012 as shown in Figure 1. This popularity coupled with a ubiquitous usage of smartphone in all aspects of our lives, e.g. online banking, social networking, and online shopping etc. have made Android a lucrative target for malware developers.To combat the threat of malware stealing our private information, researchers have suggested various techniques for detecting Android malware. Broadly speaking, three primary techniques have been used for malware detection. Static Analysis, performed without running the application, has been used to generate signatures of malware, that can be used to differentiate between malware and benign applications. Another technique, Dynamic Analysis, has been used to create a behaviour profile of malware and benign applications by executing them in a controlled environment and monitoring their behaviour to detect malware. Hybrid Analysis has been used to utilise signatures generated from static analysis and behaviour profile created from dynamic analysis for detecting Android malware. In recent years, complementary techniques such as Machine Learning and Deep Learning have been used to extract features from the three primary analysis techniques and feed them to several algorithms for classification purposes. Deep Learning is a subfield of Machine Learning that relates to structuring algorithms in layers to mimic human neural network. The artificial neural network is used to solve complex problems using different algorithms.In this dissertation, firstly, a systematic review is presented to amalgamate current approaches for detecting Android malware, and custom-built malware detection technologies. As a result of the literature evaluation, a taxonomy is suggested for Android malware detection. Furthermore, trends in the usage of the major analytical techniques and complementary techniques are shown. Research gaps in the Android malware detection area are identified for future research direction.Secondly, Droid Fence, a custom-built web-based framework, for managing experiments is developed. Droid Fence automates the extraction of the required features from malware and benign applications directory by conducting static analysis via a frontend. Next, Droid Fence completes the automated process by storing the extracted features against each application record in a relational database, feeding them to the required machine learning and deep learning algorithms, storing the result into the database, and finally displaying the outcome of each experiment.Thirdly, developed an approach that amalgamates a set of permissions, services, and six other features (usage of https, database, dynamic code, native code, reflection, and cryptography) to generate a matrix that is used for detecting malware effectively. To the best of our knowledge, this is a novel approach that combines these features to detect malware. Droid Fence is evaluated on a dataset of 13191 applications consisting of 5787 malware and 7404 benign applications. Our results show that Droid Fence is very effective when it utilises a Sequential (Deep Learning) algorithm to detect malware, achieving accuracy, F1-measure, precision, and recall scores of 0.971, 0.967, 0.977, and 0.956 respectively. Our experiments, conducted using Droid Fence, demonstrates that deep learning Sequential algorithm scored consistently highly when compared against eight machine learning algorithms. However, the difference between the accuracy scores achieved by the Sequential (97.1%) and Random Forest Classifier (95.8%) is minimal in comparison with the remaining algorithms used in our experiments. We used a stratified k fold cross-validation method, and the result was compared for four metrics: accuracy, F1 score, precision, and recall.Finally, a conclusion and future research direction are suggested for both Android malware detection area and improvement in Droid Fence.
• Terzis, Sotirios, 1973-
• Roper, Marc, 1961-
• Doctoral thesis
• 10.48730/tr4w-1p93
• Orion Practice Management Systems Limited

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Malware detection using machine learning

• Masters Thesis
• Donepudi, Naveen
• Lee, Wonjun
• Wiegley, Jeffrey
• McIlhenny, Robert
• Wang, Taehyung
• Computer Science
• California State University, Northridge
• medium sized dataset
• recurrent neural network (RNN)
• one-sided perceptron
• convolutional neural network
• Machine learning
• Malware detection
• Dissertations, Academic -- CSUN -- Computer Science.
• random forest
• decision tree
• http://hdl.handle.net/10211.3/224558
• by Naveen Donepudi

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Machine Learning Methods for Malware Detection and Classification

Chumachenko, kateryna (2017).

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Bachelor Thesis for XAMK - Machine Learning Methods for Malware Detection and Classification

katerynaCh/Malware-Classification-with-ML

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Machine learning methods for malware detection and classification.

This project is my final work for the Bachelor of Engineering degree in South-Eastern Finland University of APplied Sciences. The idea was to build the machine learning based classification of malware on top of the Cuckoo Sandbox, test how it can detect unknown malware (to simulate polymorphic or zero-day behavior) and evaluate the accuracy compared to the current signature-based methods.

Specifically, k-Nearest-Neighbors, Decision Trees, Support Vector Machines, Naive Bayes and Random Forest classifiers were evaluated. The dataset used for this study consistsed of the 1156 malware files of 9 families of different types and 984 benign files of various formats. The familes included Dridex, Locky, CTB-Locker, TeslaCrypt, Vawtrak, Zeus, Darkcomet, Cybergate, Xtreme.

If you find the work useful, kindly cite is as:

The amount of requests to share the data used for analysis has been high, but unfortunately it is not possible for me to do this at this point due to restrictions related to its sensitivity.

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Detecting Phishing URLs Using Machine Learning: A Review

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• First Online: 20 August 2024
• Cite this conference paper

• Kritika Kapse 14 ,
• Meenu Chawla 14 ,
• Namita Tiwari 14 &
• Richa Goenka 14  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1001))

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• International Conference on Deep Learning, Artificial Intelligence and Robotics

The Internet’s explosive expansion has led many people to switch from conventional banking to online banking. Unfortunately, this transition has resulted in cybercrimes such as domain fraud, ransomware, hacking, social engineering, and phishing. Attackers easily use phishing websites to steal delicate personal data because of the Internet’s inherent anonymity, such as passwords and user identities. Deterring these phishing scams is critical for protecting online organisations and individual users. Therefore, the purpose of this study is to investigate and evaluate the current state of the art in detecting phishing URLs using machine learning. Various methods, including feature extraction methods and classification algorithms, were studied. The review discusses limitations and effectiveness evaluation metrics while providing possible improvements. This comprehensive review aims to provide insight into URL-based phishing detection techniques through machine-learning approaches, which promise more robust ways of enhancing online security against fraudulent activities.

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Department of Computer Science and Engineering, MANIT, Bhopal, India

Kritika Kapse, Meenu Chawla, Namita Tiwari & Richa Goenka

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UNHCR, Geneva, Switzerland

David Pastor-Escuredo

Emlyon Business School, Écully, France

Imene Brigui

Department of Computer Science, Central University of Rajasthan, Tehsil Kishangarh, Rajasthan, India

Nishtha Kesswani

National Institute of Technology Mizoram, Mizoram, India

Sushanta Bordoloi

NERIST, North Eastern Regional Institute of Science and Technology, Nirjuli, India

Ashok Kumar Ray

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Kapse, K., Chawla, M., Tiwari, N., Goenka, R. (2024). Detecting Phishing URLs Using Machine Learning: A Review. In: Pastor-Escuredo, D., Brigui, I., Kesswani, N., Bordoloi, S., Ray, A.K. (eds) The Future of Artificial Intelligence and Robotics. ICDLAIR 2023. Lecture Notes in Networks and Systems, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-031-60935-0_24

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Published : 20 August 2024

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