Research Interests

Information Security

Software Security, Mobile and Pervasive Computing Security, Privacy

Data Science

NLP, Data Mining, Information Retrieval, Knowledge Technology

Machine Learning

Deep Learning and Machine Learning Applications for Security and Privacy

Research Projects

Software Authorship Identification

Successful software authorship de-anonymization has both software forensics applications and privacy implications. Software authorship identification is useful for software forensics and security analysts, especially for identifying malicious code (such as malware) programmers. Moreover, authorship identification of software can help in detecting plagiarism, authorship disputes, copyright infringement, and code integrity investigations. In this project, we conduct a large-scale software authorship analysis.

Continuous User Authentication

Smartphones have become crucial for our daily life activities and are increasingly loaded with our personal information to perform several sensitive tasks, including mobile banking, communication, and storing private photos and files. Therefore, there is a high demand for applying usable continuous authentication techniques that prevent unauthorized access. We proposed a deep learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user. We addressed various aspects regarding the accuracy, efficiency, and usability of using our approach.

Malware Analysis

Understanding and studying malware through analysis using various approaches, such as Control Flow Graph (CFG)-based features and then applying deep learning detection, are widely explored. We investigated the robustness of such models against adversarial attacks. Understanding the landscape of possible adversarial attacks, we propose a fine-grained hierarchical learning approach for malware detection and classification.

Adversarial Machine Learning

Investigating the security proprieties of machine learning methods, recent studies have shown various categories of adversarial attacks such as: model leakage, data membership inference, model's confidence reduction, evasion, and poisoning attacks. These attacks are studied given a level of adversarial knowledge and accessibility assumptions. We believe it is important to investigate and understand the implications of such attacks on sensitive applications in the field of information security and privacy.


  • Hisham Alasmary, Ahmed Abusnaina, Rhongho Jang, Mohammed Abuhamad, Afsah Anwar, Daehun Nyang, and David Mohaisen, "Soteria: Detecting Adversarial Examples in Control Flow Graph-based Malware Classifiers" The 40th IEEE International Conference on Distributed Computing Systems (ICDCS), 2020. PDF
  • Mohammed Abuhamad, Tamer Abuhmed, DaeHun Nyang, and David Mohaisen, "Multi-χ: Identifying Multiple Authors from Source Code Files" Privacy Enhancing Technologies Symposium (PETS), 2020. PDF
  • Mohammed Abuhamad, Tamer Abuhmed, David Mohaisen, and DaeHun Nyang, "AUToSen: Deep Learning-based Implicit Continuous Authentication Using Smartphone Sensors" the IEEE Internet of Things Journal (IoTJ), 2020. PDF
  • Mohammed Abuhamad, Ahmed Abusnaina, DaeHun Nyang, and David Mohaisen, "Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Survey" Link PDF
  • Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary, Afsah Anwar, Rhongho Jang, Saeed Salem, DaeHun Nyang, David Mohaisen, "A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification" Link PDF
  • Ahmed Abusnaina, Hisham Alasmary, Mohammed Abuhamad, Saeed Salem, DaeHun Nyang, and Aziz Mohaisen, "Subgraph-based Adversarial Examples Against Graph-based IoT Malware Detection Systems" International Conference on Computational Data and Social Networks (CSoNet), 2019 Link
  • Changhun Jung, Mohammed Abuhamad, Jumabek Alikhanov, Kyungja Han, Aziz Mohaisen, and Daehun Nyang, "W-Net: A CNN-based Architecture for White Blood Cells Image Classification" AAAI 2019 Symposium on AI for Social Good Link
  • Mohammed Abuhamad, Ji-su Rhim, Tamer AbuHmed, Sana Ullah, Sanggil Kang, and DaeHun Nyang, "Code authorship identification using convolutional neural networks" Future Generation Computer Systems, Vol. 95, pp. 104-115, June 2019 Link
  • Mohammed Abuhamad, and Masnizah Mohd, "Data Categorization and Model Weighting Approach for Language Model Adaptation in Statistical Machine Translation" International Journal of Advanced Computer Science and Applications vol. 10 (1), 2019 Link
  • Mohammed Abuhamad, Tamer AbuHmed, Aziz Mohaisen, and DaeHun Nyang, "POSTER: DL-CAIS: Deep Learning-based Code Authorship Identification System" Proceedings of Network and Distributed System Security Symposium (NDSS), 2019
  • Mohammed Abuhamad, Tamer AbuHmed, Aziz Mohaisen, and DaeHun Nyang, "Large-scale and language-oblivious code authorship identification" ACM SIGSAC Conference on Computer and Communications Security (CCS), 2018 PDF
  • Mohammed Abuhamad, Masnizah Mohd, and Juhana Salim, "Event-driven business intelligence approach for real-time integration of technical and fundamental analysis in forex market" Journal of Computer Science Vol. 9 (4) pp. 488-499, 2013 PDF
  • Nabil Hewahi, Mohamed N. Nounou, Mohamed S. Nassar, Mohamed AbuHamad, and Husam AbuHamad, "Chemical Rings Handwritten Recognition Based On Neural Network" Ubiquitous Computing and Communication Journal Vol. 3 (3), 2008