Security

The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.

With the widespread use of the Portable Document Format (PDF), it’s increasingly becoming a target for malware, highlighting the need for effective detection solutions. In recent years, machine learning-based methods for PDF malware detection have grown in popularity. However, the effectiveness of ML models is closely related to the quality of the training datasets. In this research, we investigated two widely used PDF malware datasets: Contagio and CIC. We found biases and representativeness issues that could affect the reliability and applicability of models built on them.

With the continuous expansion of grid nodes, traditional centralized methods exhibit certain limitations in the amount of communication data and computational cost for state estimation. In the past few decades, distributed state estimation has been fully developed in Multi-area Power Systems (SG). According to different regions or electrical equipment, the SG is divided into several regional nodes, and each node can estimate the entire state of the SG through local sensor measurement information and neighbor state estimation information.

Open computerized  numerical control (CNC) systems, which are crucial pieces of machinery in discrete manufacturing, are under constant security threat. Trusted computing is considered to be an effective way to protect them. However, the machining process of an open CNC system cannot be protected effectively against control-flow hijacking.