Electronics

Cyber adversaries are becoming more sophisticated, creating complex security challenges as digital services expand. The reliability of the firewall is of the utmost importance in the context of network security since it serves as the first line of protection. Penetration testing is an approach used to evaluate the reliability of a firewall and improve security by uncovering exploitable flaws. Frequently, penetration testing solutions are developed using machine learning, and it is of the utmost importance to explain the obtained results during the penetration testing. The emergence of explainable AI (XAI) addresses transparency in ML models, which is essential for informed cybersecurity decisions. Additionally, effective penetration testing reports are crucial for organizations, helping them comprehend and address vulnerabilities with tailored mitigation strategies. This study contributes to firewall security by developing an explainable penetration testing method, which includes two machine learning classification models: a binary model for detecting attacks and a multiclass model for identifying attack types with an explainability feature. This research introduces a novel explainability method that emphasizes significant features related to attack types based on multiclass predictions and proposes an approach using the extended System Security Assurance Ontology (SSAO) to clarify vulnerabilities and suggest alternative mitigation strategies. After evaluating numerous ML algorithms for the CIC-IDS2017 dataset, the Fine Tree model was considered to have the greatest performance. For the binary model, it achieved a validation accuracy of 99.7%, while for the multiclass model, it achieved a validation accuracy of 99.6%. Both models were used to test the firewall for vulnerabilities. Firewall penetration testing using the binary model achieves an accuracy of 82.1%, while the multiclass model achieves an accuracy of 78.7%.

The recent development of 3D generative AI encompassing generation and editing technologies has been increasingly investigated to advance immersive applications. To enrich visual aesthetics, 3D stylization techniques focus on transferring artistic effects from reference style images to 3D scenes. However, existing 3D stylization techniques primarily focus on global style transfer, which can result in unwanted modifications to background regions and a lack of localized control. To address these limitations, we propose LocalGaussStyle, a novel approach for localized style transfer on scenes represented by 3D Gaussian splatting. The proposed pipeline consists of two phases: object localization and localized stylization. First, 2D instance segmentation masks are projected into a 3D scene to precisely localize target objects. Next, a boundary-aware optimization is designed to perform style transfer and mitigate style leakage caused by the spatial overlap of Gaussians. In addition, geometry-decoupled adaptive densification (GDAD) is employed to enhance the geometric resolution of Gaussians within the target object, thereby improving the representation capacity. The LocalGaussStyle facilitates high-fidelity style transfer that preserves the geometry and appearance of the non-target regions. In terms of style fidelity and background preservation, the effectiveness and efficiency of the proposed method are demonstrated through extensive experiments conducted on various scenes and reference style images.

The escalating economic losses in agriculture due to deer intrusion, estimated to be in the hundreds of millions of dollars annually in the U.S., highlight the inadequacy of traditional mitigation strategies such as hunting, fencing, use of repellents, and scare tactics. This underscores a critical need for intelligent, autonomous solutions capable of real-time deer detection and deterrence. But the progress in this field is impeded by a significant gap in the literature, mainly the lack of a domain-specific, practical dataset and limited studies on the viability of deer detection systems on edge devices. To address this gap, this study presents a comprehensive evaluation of state-of-the-art deep learning models for deer detection in challenging real-world scenarios. We introduce a curated, publicly available dataset of 3095 annotated images with bounding box annotation of deer. Then, we provide an extensive comparative analysis of 12 model variants across four recent YOLO architectures (v8 to v11). Finally, we evaluated their performance on two representative edge computing platforms, the CPU-based Raspberry Pi 5 and the GPU-accelerated NVIDIA Jetson AGX Xavier, to assess feasibility for real-world field deployment. To ensure a standardized comparison, we established a framework-agnostic deployment pipeline using universal Open Neural Network Exchange (ONNX) runtimes. Results show that the real-time detection performance is not feasible on Raspberry Pi using universal runtimes, suggesting that while framework-agnostic runtimes facilitate portability, low-power CPU deployment requires hardware-specific optimization to achieve real-time thresholds. Conversely, NVIDIA Jetson provides greater than 30 frames per second (FPS) with ‘s’ and ‘n’ series models. This study also reveals that smaller, architecturally advanced models such as YOLOv11n, YOLOv8s, and YOLOv9s offer the optimal balance of high accuracy (Average Precision (AP) > 0.85) and computational efficiency (Inference Time < 34 milliseconds).