Unveiling intruders’ behaviors: explainable AI-based profiling of malicious bot activities in IoT networks The rapid proliferation of Internet of Things (IoT) devices has improved connectivity but introduced new cybersecurity risks, particularly from botnets. Detecting and identifying malicious botnet activities is crucial for early attack mitigation, understanding attack patterns, and deploying effective countermeasures. However, state-of-the-art IoT […]
UWORCS 2026, Western University (April 10, 2026) Elevating Cybersecurity Vigilance: Fusing Knowledge Dissemination via the Understanding Cybersecurity Series (UCS) knowledge mobilization Program We continue advancing cybersecurity awareness through our Understanding Cybersecurity Series (UCS), designed for students, researchers, developers, and industry professionals. UCS aims to equip communities with the knowledge and tools needed to address evolving […]
MQTTFlowLyzer: interpretable TabNet-based flow-level MQTT intrusion detection for IoT MQTT is widely used in IoT systems but remains vulnerable due to its lightweight design. This paper proposes an interpretable deep learning-based intrusion detection framework that processes raw PCAP data through flow-based analysis. It introduces MQTTFlowLyzer for extracting protocol-aware features and presents the BCCC-IoT-MQTT-IDS-2025 dataset, which […]
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 […]
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 […]
The dataset was systematically developed by augmenting and refining the Aposemat-Bot-IoT-23 dataset to address limitations in class imbalance, labeling consistency, and feature representation. Unlike prior datasets that include limited or uneven distributions of malware families, this dataset focuses on high-quality botnet traffic and benign behavior, ensuring reliable and scalable modeling of IoT botnet activities. It […]
The dataset was systematically developed by integrating and augmenting multiple high-quality MQTT-based intrusion detection datasets, enabling a comprehensive and protocol-aware representation of IoT communication. Unlike prior datasets that predominantly focus on packet-level or TCP-based analysis with limited consideration of application-layer semantics, this dataset captures rich MQTT behavioral patterns by leveraging protocol-aware feature extraction and diverse […]
CAN-BiGRUBERT: Unveiling Automotive Vehicle Intruders by Profiling and Characterizing Anomalies in Controller Area Network In-vehicle Controller Area Networks (CAN) are vulnerable to various injection attacks that can compromise the safety of vehicle occupants and result in financial losses. While a substantial body of work on CAN intrusion detection exists, it lacks multiclass attack classification models. […]
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 BCCC-IoT-IDS-ZWave-2025 Dataset is 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, […]