Malware Memory Snapshot and process-level behavioral Log Dataset (BCCC-MalMem-SnapLog-2025)

The dataset was systematically developed to capture memory-level behavioral dynamics of malware and benign processes through interval-based snapshot analysis. Unlike prior datasets that predominantly rely on static binaries or network-level observations, this dataset focuses on runtime memory behavior and process persistence, enabling a deeper understanding of how malicious activities evolve over time. It integrates diverse malware families and benign software, ensuring realistic and unbiased modeling of system-level threats in dynamic execution environments.

Captured and labeled 2 Data sources: Memory snapshot data and process-level behavioral logs
Testbed: Controlled execution environment with interval-based memory dumping across multiple time windows
Attack Profile: Eight malware categories, including Backdoor, Hoax, HackTool, Trojan, Worm, Virus, Rootkit, and Exploit, alongside benign software samples
Data size: 40 TB of memory snapshots and associated behavioral records across multiple execution intervals
Data records: 2000 malware samples and 250 benign samples with varying persistence patterns across snapshots
Data capturing: Interval-based memory snapshot collection capturing transient and persistent process behaviors
Extracted Features: Memory and process-level features capturing temporal persistence, behavioral transitions, and execution patterns. . .

Dataset: BCCC-MalMem-SnapLog-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 the most extensive Z-Wave–focused dataset to date. The dataset integrates five data sources: IP-based traffic, Z-Wave protocol communication data, device activity logs, MQTT traffic, and MQTT message logs, providing a holistic view of benign and malicious behavior. It contains multi-class records covering 81 IP network traffic classesÌý²¹²Ô»åÌý24 Z-Wave classes, supporting advanced behavioral profiling and classification tasks. The complete dataset, including raw packets, CSV records, metadata, and labeling files, is publicly available and represents a significant resource for intrusion detection, device fingerprinting, and IoT threat analysis. . .

Dataset: BCCC-IoT-IDS-ZWave-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

BCCC-SCsVul-2025

We released the BCCC-SCsVuls-2024 dataset, which is a comprehensive resource for analyzing and detecting vulnerabilities in Solidity-based smart contracts, featuring 111,897 meticulously labeled samples across 11 vulnerabilities such as Re-entrancy (17,698), IntegerUO (16,740), DenialOfService (12,394), and Secure contracts (26,914). . .

Dataset: BCCC-SCsVul-2025

BCCC-cPacket-Cloud-DDoS-2024

We released the 'BCCC-cPacket-Cloud-DDoS-2024' dataset collaboratively with cPacket from the States to address challenges identified in previous datasets through a cloud infrastructure. The dataset contains over eight benign user activities and 17 DDoS attack scenarios. The dataset is fully labeled (with a total of 26 labels) with over 300 features extracted from the network and transport layers of the traffic flows using NTLFlowLyzer. .

Dataset: BCCC-cPacket-Cloud-DDoS-2024

BCCC-VulSCs-2023

We released the 'BCCC-VulSCs-2023' dataset to analyze the secure and vulnerable Smart Contracts. 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. 

Dataset: BCCC-VulSCs-2023

BCCC-CIRA-CIC-DoHBrw-2020

We released the 'BCCC-CIRA-CIC-DoHBrw-2020' dataset to address the imbalance in the 'CIRA-CIC-DoBre-2020' dataset. Unlike the 'CIRA-CIC-DoHBrw-2020' dataset, which is skewed with about 90% malicious and only 10% benign Domain over HTTPS (DoH) network traffic, the 'BCCC-CIRA-CIC-DoHBrw-2020' dataset offers a more balanced composition including 249,836 instances in each category.

Dataset: BCCC-CIRA-CIC-DoHBrw-2020

BCCC-SFU-SQLInj-2023

We released our SQL Injection Attack dataste namely BCCC-SFU-SQLInj-2023, and published related paper entitled "An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation".

Dataset: BCCC-SFU-SQLInj-2023