I am a cybersecurity researcher with a PhD in Computer Science, specializing in blockchain security, smart contract vulnerability analysis, machine learning for cybersecurity, and secure distributed systems.

My research focuses on developing practical methods, datasets, and open-source frameworks for improving the security and reliability of blockchain-based systems, with particular emphasis on Ethereum smart contracts, vulnerability detection, security dataset construction, empirical evaluation, and AI-driven security analytics.

• 🔐 Cybersecurity
• ⛓️ Blockchain Security
• 📜 Smart Contract Security
• 🛡️ Vulnerability Detection & Analysis
• 🤖 Machine Learning for Cybersecurity
• 📊 Security Dataset Construction & Benchmarking
• 🧪 Empirical Security Evaluation
• 🌐 Secure Distributed Systems
• 🔑 Authentication & Identity Security

A blockchain digging framework for constructing vulnerability-tagged smart contract datasets.

DIVE provides an open-source pipeline for collecting blockchain data, extracting heterogeneous smart contract features, integrating vulnerability labels, preprocessing data, and constructing datasets for security and machine learning research.

A vulnerable Ethereum smart contract labeling and evaluation framework.

MultiTagging supports vulnerability-tag extraction, normalization, evaluation, multi-tool voting, and analysis of smart contract vulnerability detection tools.

An empirical study of tree-based ensemble learning for phishing transaction detection on Ethereum.

The project investigates machine learning approaches for detecting phishing transactions using Ethereum transaction data, with emphasis on classification performance, class imbalance, feature selection, and computational efficiency.

DIVE: A Multi-Label Smart Contract Vulnerability Dataset
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
Scientific Data, 13, Article 664
DOI: 10.1038/s41597-026-07025-5

Enhanced Phishing Transactions Detection on Ethereum Network with Tree-based Ensembles: An Empirical Study
Shikah J. Alsunaidi, Hamoud Aljamaan
Blockchain: Research and Applications
DOI: 10.1016/j.bcra.2026.100506

Leveraging Machine Learning Models to Improve Smart Contract Security: A Survey of Vulnerabilities and Detection Methods
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
ACM Computing Surveys
DOI: 10.1145/3772367

MultiTagging: A Vulnerable Smart Contract Labeling and Evaluation Framework
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
Electronics, 13(23), 4616
DOI: 10.3390/electronics13234616

My recent work centers on three connected directions:

Developing methods and frameworks for vulnerability labeling, benchmarking, detection, and security evaluation.

Constructing high-quality, reproducible datasets that support fair evaluation and machine learning research.

Applying machine learning and data-driven methods to blockchain security, phishing detection, vulnerability analysis, and security analytics.

• PhD in Computer Science
• Research focus on cybersecurity, blockchain, smart contracts, and AI-driven security
• Experience in academic instruction and cybersecurity training
• Former Cisco Networking Academy Instructor
• Active in research collaboration, peer review, and academic service

Advancing reproducible, data-driven cybersecurity research for secure and resilient digital systems.