VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction

Binary program vulnerability detection is critical for software security, yet existing deep learning approaches often rely on source code analysis, limiting their ability to detect unknown vulnerabilities. To address this, we propose VulCatch, a binary-level vulnerability detection framework. VulCatch introduces a Synergy Decompilation Module (SDM) and Kolmogorov-Arnold Networks (KAN) to transform raw binary code into pseudocode using CodeT5, preserving high-level semantics for deep analysis with tools like Ghidra and IDA. KAN further enhances feature transformation, enabling the detection of complex vulnerabilities. VulCatch employs word2vec, Inception Blocks, BiLSTM Attention, and Residual connections to achieve high detection accuracy (98.88%) and precision (97.92%), while minimizing false positives (1.56%) and false negatives (2.71%) across seven CVE datasets.

mAPm: multi-scale Attention Pyramid module for Enhanced scale-variation in RLD detection

Detecting objects across various scales remains a significant challenge in computer vision, particularly in tasks such as Rice Leaf Disease (RLD) detection, where objects exhibit considerable scale variations. Traditional object detection methods often struggle to address these variations, resulting in missed detections or reduced accuracy. In this study, we propose the multi-scale Attention Pyramid module (mAPm), a novel approach that integrates dilated convolutions into the Feature Pyramid Network (FPN) to enhance multi-scale information ex-traction. Additionally, we incorporate a global Multi-Head Self-Attention (MHSA) mechanism and a deconvolutional layer to refine the up-sampling process. We evaluate mAPm on YOLOv7 using the MRLD and COCO datasets. Compared to vanilla FPN, BiFPN, NAS-FPN, PANET, and ACFPN, mAPm achieved a significant improvement in Average Precision (AP), with a +2.61% increase on the MRLD dataset compared to the baseline FPN method in YOLOv7. This demonstrates its effectiveness in handling scale variations. Furthermore, the versatility of mAPm allows its integration into various FPN-based object detection models, showcasing its potential to advance object detection techniques.