Design-for-Security (DfS) primitives such as Physical Unclonable Functions (PUFs), pseudorandom number generator (PRNGs), logic locking structures, and on-chip sensors are widely embedded in modern hardware to counter threats including cloning, piracy, and reverse engineering. However, these structures introduce identifiable structural signatures that can be exploited by adversaries with access to post-synthesis netlists to locate and disable or manipulate the underlying security mechanisms. Existing approaches for identifying such primitives are largely ad hoc, design-specific, and require significant manual intervention, limiting scalability and compatibility with automated analysis frameworks. This paper presents SPHINX, an open-source framework for automated identification and localization of DfS primitives directly from post-synthesis gate-level netlists without requiring RTL, simulation, or designer annotations. SPHINX employs a three-stage pipeline comprising constrained synthesis, graph-based feature extraction, and unsupervised clustering. The framework computes 53 per-cell structural features capturing topology, information-theoretic properties, and domain-specific signatures of DfS circuits. Across five DfS classes (Arbiter Physical Unclonable Function (APUF), Ring Oscillator Physical Unclonable Function (RO-PUF), Linear Feedback Shift Register (LFSR), PRNG, and Dynamically Obfuscated Scan Chains (DOSC)) and 35 benchmark configurations, SPHINX achieves a mean F1 score exceeding 0.95. These results demonstrate that structural analysis alone is sufficient to isolate security-critical circuitry, significantly reducing the search space for subsequent adversarial actions.

Due to the reliance on untrusted supply chain entities, tracking and authentication of Integrated Circuits (ICs) has become crucial to prevent the rapid proliferation of counterfeits. Physically Unclonable Functions (PUFs) can be used for such IC authentication since they generate unique identifiers for individual ICs. However, PUF-generated signatures are often noisy and traditional solutions like Error Correcting Codes (ECC) are expensive and vulnerable to attacks. Moreover, comprehensive PUF-based authentication at multiple locations of the supply chain at any given time suffers from large storage requirements, high query processing time, and security threats. This article proposes a Persistent Hierarchical Bloom Filter (PHBF) to enable fast, storage-efficient and noise-tolerant authentication to track ICs across the supply chain. The proposed framework is demonstrated using 4,000 PUF-generated signatures from several FPGAs and achieved the highest possible authentication accuracy under temperature-induced and synthetic noise of varied degrees without any ECC. Our comparative analysis of storage and query time requirements against four different solutions for detecting wide range counterfeit ICs shows the significant benefit of PHBF, providing up to \(10^{5}\) times faster query processing and 39 times lower storage requirement compared to blockchain.

Graph analytics has become instrumental in uncovering insights across various domains, specifically in social networks. It serves as a crucial tool for analyzing the relationship between users in different online platforms. In this research, we apply methods of social network analysis to examine the communication patterns among participants in an online forum recognized for far-right extremism. Our study demonstrates the actors’ relationships and activities through different aspects of applications over networks. In extensive analysis, we identify the influential actors and map their relationships throughout the course of 76 monthly networks. Moreover, we illustrate the evolution of networks over that period, and their connections with significant events. The findings of this analysis aim to understand the nature of interactions and networks, and to allow practitioners to take necessary precautions to mitigate far-right activities on various online platforms.

Predicting events, ranging from political unrest to disease outbreaks and criminal activities, stands as a pivotal task in proactively addressing emerging challenges. Despite the richness of textual data as a source for event detection, it is challenging to extract contextual information from documents due to their complex structure and the dynamic evolution of events. In response to this challenge, dynamic Graph Neural Networks (GNNs) have emerged as a promising tool for capturing the intricate patterns embedded within textual data graphs. Nevertheless, many models in this domain primarily rely on local node-level representations, overlooking the essential global graph-level context. However, both node-level and graph-level representations are critical for effective event prediction. Node-level representations provide insight into the local structure, while graph-level representations offer an understanding of the global structure and the evaluation of temporal graphs. To address these challenges, in this paper, we propose a Dynamic Graph Contrastive Learning (DyGCL) method for event prediction. Our model DyGCL first employs a local view encoder to effectively capture the local dynamic structure of input graphs as the evolving node representations. Then, it performs a global view encoder to perceive the hierarchical dynamic graph representation of the input graphs. Finally, the graph representations from both encoders, optimized via contrastive learning, are combined with an attention mechanism and utilized to predict future events. Our extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods for event prediction on six real-world datasets.

Modeling the interactions between drugs, targets, and diseases has significant implications for drug discovery, precision medicine and personalized treatments. Current computational approaches consider pairwise interaction, including drug-target or drug-disease interaction individually. On the other hand, within human metabolic systems, the interaction of drugs with protein targets in cells influences target activities. Moving beyond binary relationships and exploring tighter relationships together as triple is essential to understanding drugs' mechanism of action (MoAs). Moreover, considering the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, it is critical to model these complex interactions appropriately. To address these challenges, we develop a novel Heterogeneous Graph Triplet Attention Network (HeTan)by modeling the interconnectedness of all entities in a heterogeneous graph. HeTAN introduces a novel triplet message passing and triplet-wise attention mechanism within this heterogeneous graph structure. In contrast to focusing only on pairwise attention as the importance of an entity for the other, we define triplet attention to model the importance of pairs for the other in the drug-target-disease triplet prediction problem. We perform extensive experiments on real-world datasets and our results show that HeTAN outperforms several baselines, demonstrating its superior performance in uncovering novel drug-target-disease relationships.

This paper introduces a scalable SPICE-based tool infrastructure designed to optimize analog compute-in-memory (ACIM) architectures utilizing emerging non-volatile resistive memory (eNVM) technologies. The inherent efficiency of analog eNVM crossbar arrays in performing matrix-vector multiplications significantly enhances the power, performance, and area efficiency of edge AI devices and other applications. Our framework addresses the challenges of accurately simulating ACIM architectures, which are highly susceptible to variations in process, voltage, temperature, and analog noise. The framework uses SPICE for accurate analog and mixed-signal circuit simulation. It automates the generation of SPICE-level ACIM designs for deep neural networks. Additionally, it speeds up the simulation runtime by up to 35× for large DNN models while maintaining the same SPICE-level accuracy. Moreover, it ensures simulation convergence for large netlists, facilitating SPICE simulation of large-scale eNVM crossbars that were previously impractical. We demonstrated that our framework is capable of simulating inference using netlists for MLPs with over 800,000 parameters trained on the MNIST dataset within acceptable runtime, where contemporary SPICE simulators do not even converge. We validated the simulation results in terms of inference accuracy, which shows less than a 3.8% accuracy drop compared to software-based inference results. Lastly, we demonstrated the integration of our framework with an architectural simulator, facilitating comprehensive system-level simulation.

Network analysis is an emerging field with a wide spectrum of applications across many disciplines such as social networks, computer networks, and healthcare. However, the ever-increasing size of real-world networks is a major challenge for network analysis due to their high computational and space costs. In this paper, we utilize a node similarity-based graph compression method, SGC, and investigate the effect of various node similarity measures on graph compression. SGC compresses the input graph to a smaller graph without losing any/much information about its global structure and the local proximity of its vertices. We apply our compression method to the network embedding problem to study its effectiveness and efficiency. Our experimental results on four real-world networks show that each similarity measure has a different effect on graph compression and embedding, where some yield an improvement up to 70% network embedding time without decreasing classification accuracy as evaluated on single and multi-label classification tasks.