Mohammed Abuhamad

Assistant Professor of Computer Science

I am an assistant professor of Computer Science at Loyola University Chicago. I received a Ph.D. degree in Computer Science from the University of Central Florida (UCF) in 2020. I also received a Ph.D. degree in Electrical and Computer Engineering from INHA University, (Incheon, Republic of Korea) in 2020. I received a Master degree in Information Technology (Artificial Intelligence) from the National University of Malaysia, (Bangi, Malaysia) in 2013.

During my doctoral study, I worked at the Security Analytics Research Lab , with my advisor Prof. Mohaisen. At INHA, I worked at the Information Security Research Lab, with my advisor Prof. Nyang. I am interested in AI/Deep-Learning-based Information Security, especially Software and Mobile/IoT Security. I am also interested in Machine Learning-based Applications and Adversarial Machine Learning. I have published several peer-reviewed research papers in top-tier conferences and journals such as ACM CCS, PoPETS, IEEE ICDCS, and IEEE IoT-J.

Recent News

Accepted Paper (2020)

Our survey paper on continuous authentication using behavioral biometrics was accepted in IEEE Internet of Things Journal (IoTJ).

Accepted Paper (2020)

Our paper on studying DDoS attacks' progression and predicting the spatio-temporal behavior of the attacks was accepted in WISA.

Moved to Loyola

I started my new position as an Assistant Professor of Computer Science at Loyola University Chicago.

Accepted Paper (2020)

Our paper on identifying multiple programmers from source code was accepted in the Privacy Enhancing Technologies Symposium (PETS).

Accepted Paper (2020)

Our paper on the robustness of CFG-based malware detection models against adversarial examples was accepted in IEEE ICDCS.

Accepted Paper (2020)

Our paper introducing AUToSen for continuous authentication using mobile sensors was accepted in IEEE Internet of Things Journal (IoTJ).

Research.

Information Security and Privacy

Adversarial Machine Learning

Machine Learning-based Applications

Publications.

Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey

IEEE Internet of Things Journal, 2020

The survey provides an overview of the state-of-the-art approaches for continuous authentication using behavioral biometrics captured by smartphones’ embedded sensors, including insights and open challenges.

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Multi-χ: Identifying Multiple Authors from Source Code Files

Privacy Enhancing Technologies Symposium (PETS) , 2020

Multi-χ leverages a deep learning-based approach for multi-author identification in source code, is lightweight, uses a compact representation for efficiency, and does not require any code parsing, syntax tree extraction, nor feature selection.

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AUToSen: Deep Learning-based Implicit Continuous Authentication Using Smartphone Sensors

IEEE Internet of Things Journal, 2020

AUToSen is an active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user.

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Insights into Attacks' Progression: Prediction of Spatio-Temporal Behavior of DDoS Attacks

The 21st World Conference on Information Security Applications (WISA), 2020

We construct an ensemble of state-of-the-art deep learning models to predict behavioral patterns of DDoS attacks.

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Soteria: Detecting Adversarial Examples in Control Flow Graph-based Malware Classifiers

The 40th IEEE International Conference on Distributed Computing Systems (ICDCS), 2020

In this paper, we systematically tackle the problem of adversarial examples detection in the control flow graph (CFG) based classifiers for malware detection using Soteria.

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A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification

In this paper, we examine the performance of the state-of-the-art methods against adversarial IoT software that are crafted using the graph embedding and augmentation techniques.

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Understanding the Proxy Ecosystem: A Comparative Analysis of Residential and Open Proxies on the Internet

IEEE Access, 2020

In this work, we explore the ecosystem of proxies by understanding their affinities and distributions comparatively.

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Subgraph-based Adversarial Examples Against Graph-based IoT Malware Detection Systems

International Conference on Computational Data and Social Networks (CSoNet), 2019

This paper investigates the robustness of CFG-based deep learning malware detection systems against adversarial attacks.

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W-Net: A CNN-based Architecture for White Blood Cells Image Classification

AAAI 2019 Symposium on AI for Social Good, 2019

This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset, obtained from The Catholic University of Korea.

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Code authorship identification using convolutional neural networks

Future Generation Computer Systems, 2019

This work proposes a CNN-based code authorship identification system. Our proposed system exploits term frequency-inverse document frequency, word embedding modeling, and feature learning techniques for code representation.

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Data Categorization and Model Weighting Approach for Language Model Adaptation in Statistical Machine Translation

International Journal of Advanced Computer Science and Applications, 2019

This research introduces a language model adaptation approach that combines both data selection and weighting criteria.

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Large-scale and language-oblivious code authorship identification

ACM SIGSAC Conference on Computer and Communications Security (CCS), 2018

This work proposes a Deep Learning-based Code Authorship Identiication System (DL-CAIS) for code authorship attribution that facilitates large-scale, language-oblivious, and obfuscation-resilient code authorship identiication.

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Event-driven business intelligence approach for real-time integration of technical and fundamental analysis in forex market

Journal of Computer Science, 2013

This study proposes an event-driven business intelligence approach to respond immediately to any change in the market status by generating trading signals based on different analyses.

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Chemical Rings Handwritten Recognition Based On Neural Network

Ubiquitous Computing and Communication Journal, 2008

This paper is focused on pattern recognition for Heterocyclic chemical handwritten recognition using Neural Networks.

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Contact.

mabuhamad@luc.edu
  • 308 Doyle Center, Lake Shore Campus
  • Department of Computer Science
  • Loyola University Chicago
  • 1052 W Loyola Ave. Chicago, IL 60626