Defense Notices

Guava leaf diseases significantly affect crop yield and quality, making timely detection essential for effective disease management. This project presents an end-to-end software system for automated guava leaf disease detection using deep learning and transfer learning techniques. Multiple pretrained convolutional neural network (CNN) architectures, including ResNet, AlexNet, VGG, SqueezeNet, DenseNet, Inception-v3, and EfficientNet, were adapted through feature extraction and trained on a guava leaf image dataset.

The system allows users to either capture an image using a camera or upload an existing leaf image through a software interface. The input image is processed and classified by the trained deep learning model, and the predicted disease class is displayed to the user. The dataset was divided into training, validation, and test sets to ensure robust performance evaluation, and final test accuracy was used to measure generalization on unseen data.

Experimental results demonstrate that transfer learning enables accurate and efficient guava leaf disease classification. Among the evaluated models, the best-performing architecture achieved an accuracy between 97% to 99%. Overall, the developed software provides a practical and user-friendly solution for real-world agricultural disease diagnosis.

The rapid growth of Internet of Things (IoT) technology has brought unprecedented convenience to everyday life, enabling users to deploy automation rules and develop IoT apps tailored to their specific needs. However, modern IoT ecosystems consist of numerous devices, applications, and platforms that interact continuously. As a result, users are increasingly exposed to complex and subtle security and privacy risks that are difficult to fully comprehend. Even interactions among seemingly harmless apps can introduce unforeseen security and privacy threats. In addition, violations of memory integrity can undermine the security guarantees on which IoT apps rely.

The first approach investigates hidden cross-app privacy leakage risks in IoT apps. These risks arise from cross-app interaction chains formed among multiple seemingly benign IoT apps. Our analysis reveals that interactions between apps can expose sensitive information such as user identity, location, tracking data, and activity patterns. We quantify these privacy leaks by assigning probability scores to evaluate risk levels based on inferences. In addition, we provide a fine-grained categorization of privacy threats to generate detailed alerts, enabling users to better understand and address specific privacy risks.

The second approach addresses cross-app interaction threats in IoT automation systems by leveraging a logic-based analysis model grounded in event relations. We formalize event relationships, detect event interferences, and classify rule conflicts, then generate risk scores and conflict rankings to enable comprehensive conflict detection and risk assessment. To mitigate the identified interaction threats, an optimization-based approach is employed to reduce risks while preserving system functionality. This approach ensures comprehensive coverage of cross-app interaction threats and provides a robust solution for detecting and resolving rule conflicts in IoT environments.

To support the development and rigorous evaluation of these security analyses, we further developed a large-scale, manually verified, and comprehensive dataset of real-world IoT apps. This clean and diverse benchmark dataset supports the development and validation of IoT security and privacy solutions. All proposed approaches are evaluated using this dataset of real-world apps, collectively offering valuable insights and practical tools for enhancing IoT security and privacy against cross-app threats. Furthermore, we examine the integrity of the execution environment that supports IoT apps. We show that, even under non-privileged execution, carefully crafted memory access patterns can induce bit flips in physical memory, allowing attackers to corrupt data and compromise system integrity without requiring elevated privileges.

In resource‑constrained contested environments (RCCEs), communications are routinely censored, surveilled, or disrupted by nation‑state adversaries, leaving at‑risk populations—including protesters, dissidents, disaster‑affected communities, and military units—without secure connectivity. This dissertation introduces MeshBLanket, a Bluetooth Mesh‑based framework designed for low‑power, low‑throughput messaging with minimal electromagnetic spectrum exposure. Built on commercial off‑the‑shelf hardware, MeshBLanket extends the Bluetooth Mesh specification with automated provisioning and network‑wide key refresh to enhance scalability and resilience.

We evaluated MeshBLanket through field experimentation (range, throughput, battery life, and security enhancements) and qualitative interviews with ten senior U.S. Army communications experts. Thematic analysis revealed priorities of availability, EMS footprint reduction, and simplicity of use, alongside adoption challenges and institutional skepticism. Results demonstrate that MeshBLanket maintains secure messaging under load, supports autonomous key refresh, and offers operational relevance at the forward edge of battlefields.

Beyond military contexts, parallels with protest environments highlight MeshBLanket’s broader applicability for civilian populations facing censorship and surveillance. By unifying technical experimentation with expert perspectives, this work contributes a proof‑of‑concept communications architecture that advances secure, resilient, and user‑centric connectivity in environments where traditional infrastructure is compromised or weaponized.