Related papers: Node-wise Hardware Trojan Detection Based on Graph…

Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability…

Globalization in the semiconductor industry enables fabless design houses to reduce their costs, save time, and make use of newer technologies. However, the offshoring of Integrated Circuit (IC) fabrication has negative sides, including…

The trend toward Microgrids (MGs) is significantly increasing by employing Distributed Generators (DGs) which leads to new challenges, especially in the fault detection. This paper proposes an algorithm based on the Total Harmonic…

Graph Convolutional Network (GCN) are widely used in Graph Anomaly Detection (GAD) due to their natural compatibility with graph structures, resulting in significant performance improvements. However, most researchers approach GAD as a…

Deep learning methods for graphs achieve remarkable performance across a variety of domains. However, recent findings indicate that small, unnoticeable perturbations of graph structure can catastrophically reduce performance of even the…

Recently, Haider et al. introduced the first rigorous hardware Trojan detection algorithm called HaTCh. The foundation of HaTCh is a formal framework of hardware Trojan design, which formally characterizes all the hardware Trojans based on…

Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their…

Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph learning and have shown significant improvements in many important graph problems.…

Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a…

Image manipulation detection is to identify the authenticity of each pixel in images. One typical approach to uncover manipulation traces is to model image correlations. The previous methods commonly adopt the grids, which are fixed-size…

Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability…

Graph neural networks (GNNs) have attracted significant attention for their outstanding performance in graph learning and node classification tasks. However, their vulnerability to adversarial attacks, particularly through susceptible…

Detection of malicious behavior in a large network is a challenging problem for machine learning in computer security, since it requires a model with high expressive power and scalable inference. Existing solutions struggle to achieve this…

The graph identification problem consists of discovering the interactions among nodes in a network given their state/feature trajectories. This problem is challenging because the behavior of a node is coupled to all the other nodes by the…

Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models. Recently, a number of deep testing methods in software engineering were proposed…

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data. In this work, we investigate Contrastive Learning (CL), a key component…

A cyber-attack is a malicious attempt by experienced hackers to breach the target information system. Usually, the cyber-attacks are characterized as hybrid TTPs (Tactics, Techniques, and Procedures) and long-term adversarial behaviors,…

The commercial off-the-shelf (COTS) component based ecosystem provides an attractive system design paradigm due to the drastic reduction in development time and cost compared to custom solutions. However, it brings in a growing concern of…

This paper reports a novel approach that uses transistor aging in an integrated circuit (IC) to detect hardware Trojans. When a transistor is aged, it results in delays along several paths of the IC. This increase in delay results in timing…

Cyber Threat hunting is a proactive search for known attack behaviors in the organizational information system. It is an important component to mitigate advanced persistent threats (APTs). However, the attack behaviors recorded in…