Toward generating a large-scale IoT-Zwave intrusion detection dataset: Smart device profiling, intruders behavior, and traffic characterization This article introduces BCCC-IoT-IDS-Zwave-2025, the most extensive and diverse IoT smart home dataset to date, developed over five months using a large-scale testbed comprising more than 50 IoT devices and encompassing over 80 distinct attack scenarios. Unlike prior datasets […]
DeFi Transaction Analyzer and Feature Extractor ( DeFiTranLyzer-V1.0 ) DeFiTransLyzer, part of the Understanding Cybersecurity Series (UCS), is an open-source Python framework for analyzing Ethereum wallets and transactions in DeFi research.The Wallet Analyzer extracts statistical and behavioral features such as gas usage, transaction duration, and address diversity, while the Transaction Analyzer captures efficiency ratios, event logs, and […]
BCCC, York University, Toronto, ON, Canada The second CyberSecurity Cartoon Award (CSCA) – Hosted by Universidad Politécnica de Madrid (Spain) Today, October 6, We’re excited to announce the official winners of the second CyberSecurity Cartoon Award (CSCA 2025); a flagship initiative under the Understanding Cybersecurity Series (UCS) program, dedicated to promoting cybersecurity awareness among K-12 […]
DeFi Fraud Transactions (BCCC-DeFiFraudTrans-2025) We released the BCCC-DeFiFraudTrans-2025, a large-scale, Ethereum-based benchmark designed explicitly for profiling fraudulent and legitimate DeFi transactions. It contains 1,026,867 annotated transaction samples spanning from 2017 to 2024, drawn from 9,374 unique wallet addresses. The dataset integrates both wallet-level and transaction-level attributes, with 79 features extracted via the DeFiTransLyzer-V1.0 analyzer... Dataset: BCCC-DeFiFraudTrans-2025
BCCC-DeFiFraudTrans-2025 is a large-scale, Ethereum-based benchmark designed explicitly for profiling fraudulent and legitimate DeFi transactions. It contains 1,026,867 annotated transaction samples spanning from 2017 to 2024, drawn from 9,374 unique wallet addresses. The dataset integrates both wallet-level and transaction-level attributes, with 79 features extracted via the DeFiTransLyzer-V1.0 analyzer. These features are organized into categories, including gas usage, cumulative gas consumption, […]
Memory Analysis for Malware Detection: A Comprehensive Survey Using the OSCAR Methodology Malware has sharply escalated, with a 30% surge in global cyberattacks in 2024, highlighting the limitations of traditional detection methods against sophisticated threats. This survey addresses gaps in prior work by applying the OSCAR methodology to review memory acquisition techniques, forensic methods, and […]
BCCC, York University, Toronto, ON, Canada The Third Annual Collaborative Workshop – BCCC & NICT Japan (September 5) Today, September 5, we held the third annual collaborative workshop between the Behaviour-Centric Cybersecurity Centre (BCCC) and NICT Japan, with special thanks to Dr. Takeshi Takahashi, Dr. Tao Ban, Prof. Seiichi Ozawa, Dr. Muhammad Fakhrur Rozi, and […]
VADViT: Vision Transformer-Driven Memory Forensics for Malicious Process Detection and Explainable Threat Attribution Modern malware’s complexity challenges traditional detection and existing ML-based memory forensics, which often rely on outdated features and struggle with large-scale data. We propose VADViT, a vision transformer model that converts VAD memory regions into fused Markov, entropy, and intensity images for […]
The CIC-BCCC-NRC TabularIoTAttack-2024 dataset is a comprehensive collection of IoT network traffic data generated as part of an advanced effort to create a reliable source for training and testing AI-powered IoT cybersecurity models. This dataset is designed to address modern challenges in detecting and identifying IoT-specific cyberattacks, offering a rich and diverse set of labeled […]
The BCCC-VulSCs-2023 dataset is a substantial collection for Solidity Smart Contracts (SCs) analysis, comprising 36,670 samples, each enriched with 70 feature columns. These features include the raw source code of the smart contract, a hashed version of the source code for secure referencing, and a binary label that indicates a contract as secure (0) or […]