Tanvir Hossain, Mahmudul Hasan, Kareem El-Araby, Lorenzo R. Godinez, Swarup Bhunia, Md Tauhidur Rahman, Tamzidul Hoque
Cybersecurity education has predominantly focused on software and network security, under the assumption that the underlying hardware is trustworthy. Threats introduced through the global semiconductor supply chain, together with vulnerabilities arising during design and integration, challenge the assumption that hardware can serve as an unquestioned root of trust. Sensor-enabled systems and machine-learning accelerators further expand the hardware attack surface. The undergraduate curriculum of electrical and computer engineering and computer science programs has not evolved accordingly. Core courses cover the design and the functionality of hardware while the security aspects remain overlooked, and dedicated hardware security (HS) courses are elective, scarce, and often restricted to graduate students. At the University of Florida, fewer than 7% of the undergraduate electrical and computer engineering students register for the introductory HS course each year. We present an integrated and economical approach that disseminates fundamental HS concepts within existing core courses through contextual learning. Nine hands-on modules were developed such that each security concept exercises an associated design concept of the core course, as a component of that course and not as a course of their own. Doing the security work is how the student exercises the design topic, so the modules are built to strengthen the host course and not only to add to it. Three were assessed across three offerings of the embedded systems course through one course survey and two conducted by an external evaluator, with 25 respondents. We report what those instruments measured and the limitations of the approach.
Muhammad Ifte khairul Islam, Tanvir Hossain, Esra Akbas
Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph. Although augmented view generation is the central step in GCL, existing models rely on stochastic augmentation of the input graph, such as edge perturbation and node masking, which lack structural awareness and frequently destroy critical topology or introduce semantically inconsistent views. While recent adaptive methods improve upon random perturbations, they primarily operate on localized node or edge neighborhoods and fail to capture higher-order, macro-level structural organization crucial for graph classification. To address this limitation, we propose Structure-aware Compression for Graph Contrastive Learning (SAC-GCL), a novel framework that employs structural decomposition and graph compression as a principled view generation strategy. Moving beyond random augmentations, SAC-GCL leverages structural decomposition techniques to extract cohesive subgraphs and condense them into supernodes, constructing coarsened graph views that preserve local density and capture higher-level structural patterns while reducing noise and redundancy. We leverage multiple cohesive subgraph models to derive complementary views of the graph, each capturing distinct structural properties. To capture multi-resolution information, a hierarchical view encoder processes these compressed views by initializing super-node features with shared-weight cohesive subgraph embeddings. Our results from extensive experiments on six benchmark datasets demonstrate that structure-aware compression provides a powerful alternative to random perturbations, yielding more robust and discriminative graph representations for graph classification tasks.