One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. These flaws are highly likely ex-ploited and lead to system compromise, data leakage, or denial of ser-vice. C and C++ open source code are now available in order to create a large-scale, classical machine-learning and quantum machine-learning system for function-level vulnerability identification. We assembled a siz-able dataset of millions of open-source functions that point to poten-tial exploits. We created an efficient and scalable vulnerability detection method based on a deep neural network model Long Short Term Memory (LSTM), and quantum machine learning model Long Short Term Memory (QLSTM), that can learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the de-pendency. Therefore, We keep the semantic and syntactic information using state of the art word embedding algorithms such as Glove and fastText. The embedded vectors are subsequently fed into the classical and quantum convolutional neural networks to classify the possible vulnerabilities. To measure the performance, we used evaluation metrics such as F1 score, precision, re-call, accuracy, and total execution time. We made a comparison between the results derived from the classical LSTM and quantum LSTM using basic feature representation as well as semantic and syntactic represen-tation. We found that the QLSTM with semantic and syntactic features detects significantly accurate vulnerability and runs faster than its classical counterpart.
Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI
department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（放射医学与核医学科，宣武医院，首都医科大学）；Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics（北京磁共振成像与脑信息学重点实验室）；Beijing United Imaging Research Institute of Intelligent Imaging（北京智能成像联合研究院）
Enhancing Vulnerability Reports with Automated and Augmented Description Summarization
Department of Computer Science at the University of Central Florida（佛罗里达中央大学计算机科学系）；Department of Computer Science at Northeastern Illinois University（北伊利诺伊大学计算机科学系）；Department of Computer Science at Qatar University（卡塔尔大学计算机科学系）；Ewha Womans University（成均馆大学）
DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning
CREST - The Centre for Research on Engineering Software Technologies, The University of Adelaide（CREST - 工程软件技术研究中心，阿德莱德大学）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
Adaptive Soft Error Protection for Neural Network Processing
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所国家处理器实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Jinan Inspur Data Technology Co., Ltd.（济南Inspur数据技术有限公司）
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning
University of Würzburg（维尔茨堡大学）；University of California, San-Diego（加利福尼亚大学圣地亚哥分校）；Technical University of Darmstadt（达姆施塔特工业大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Control Engineering（北京控制工程研究所）
Evaluating LLaMA 3.2 for Software Vulnerability Detection
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering of the Polytechnic of Porto（波尔图理工学院工程学院）；INESC TEC/Polytechnic of Porto - School of Engineering（INESC TEC/波尔图理工学院工程学院）
