News

New Dataset Alert! (BCCC-IoT-IDS-Zwave-2025)

BCCC-IoT-IDS-Zwave-2025 We released a large-scale, multi-source IoT security dataset developed over five months (20 TB data, including more than 1 BILLION records) using a comprehensive smart-home testbed comprising more than 110 devices, including sensors, actuators, smart plugs, locks, meters, and controllers. According to the paper, the dataset includes 88 distinct attack scenarios spanning network-layer, device-layer, and service-layer threats, making it […]

New Article Alert!

A deep learning-based vulnerability detection in blockchain smart contracts using masked attention and control flow graph analysis SCs are self-executing programs on the blockchain, used for transactions without intermediaries, particularly in cryptocurrencies like Ethereum. However, they are vulnerable to security flaws that can lead to significant financial losses, as demonstrated by the DAO hack 2016. […]

New Article Alert!

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 […]

Blockchain Security: DeFi Transaction Analyzer and Feature Extractor ( DeFiTranLyzer-V1.0 )

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 […]

The second CyberSecurity Cartoon Award (CSCA) – Hosted by Universidad Politécnica de Madrid (Spain)

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 […]

New Dataset Alert! (BCCC-DeFiFraudTrans-2025)

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

New Article Alert!

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 […]

The Third Annual Collaborative Workshop – BCCC & NICT Japan (September 5)

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 […]

New Article Alert!

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 […]

New Article Alert!

SCsVulSegLyzer: Detecting and Extracting Vulnerable Segments from Smart Contracts Using Weakly-Supervised Learning Smart contracts (SCs) are widely used in finance but remain attractive targets for hackers due to vulnerabilities, the immaturity of Solidity, and blockchain’s immutability. We present SCsVulSegLytix, a Transformer-based model that detects and extracts vulnerable segments directly from Solidity code using only contract-level […]