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A New Framework for Software Vulnerability Detection Based on an Advanced Computing

Computers, Materials & Continua 2024年第6期79卷 3699-3723页

作者： Bui Van Cong Cho Do Xuan Department of Information Technology University of Economics and Technical IndustriesHanoi100000Vietnam Faculty of Information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest *** paper proposes a new framework called DrCSE to improve software vulnerability *** uses an intelligent computation technique based on the combination of two methods:rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software *** do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive *** which,the method(i)extracts the source code’s features based on the vertices and edges of the *** method of rebalancing data has the function of supporting the training process by balancing the experimental ***,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers *** experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum *** a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE *** performs better than other approaches,with a 5%boost in Precision and a 5%boost in ***,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

关键词： Source code vulnerability source code vulnerability detection code property graph feature profile contrastive learning data rebalancing

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Malicious Synchrophasor Detection Based on Highly Imbalanced Historical Operational data

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CSEE Journal of Power and Energy Systems 2019年第1期5卷 11-20页

作者： Jingyu Wang Zhengwei Sun Bin Bao Dongyuan Shi State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China Northeast Branch of State Grid Corporation of China Shenyang 110180China

By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems,cyber attackers can mislead the control center into taking wrong *** the viewpoint of machine learning,normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent ***,a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor ***,malicious synchrophasors occur infrequently in *** is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for *** most existing detectors,if they are directly trained with highly imbalanced datasets,their performances may severely *** this paper,a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing,Bagging-based ensemble learning,and the widely recognized eXtreme Gradient Boosting(XGBoost)*** show that although fewer malicious instances are provided,the proposed detector is still capable of detecting malicious synchrophasors.

关键词： data rebalancing ensemble learning malicious synchrophasor detection XGBoost

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