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software Defect Prediction Method Based on Stable Learning

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Computers, Materials & Continua 2024年第1期78卷 65-84页

作者： Xin Fan Jingen Mao Liangjue Lian Li Yu Wei Zheng Yun Ge College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210012China School of software Nanchang Hangkong UniversityNanchang330029China software Testing and Evaluation Center Nanchang Hangkong UniversityNanchang330029China

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and *** previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data ***,target projects often lack sufficient data,which affects the performance of the transfer learning *** addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning *** address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual *** method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code *** the model training process,target project data are not required as prior *** the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the *** approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction *** evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE *** experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction ***,SDP-SL can effectively enhance within-and cross-project defect predictions.

关键词： software defect prediction code visualization stable learning sample reweight residual network

IoV environment exploring coordination:A federated learning approach

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Digital Communications and Networks 2024年第1期10卷 135-141页

作者： Tong Ding Lei Liu Yi Zhu Lizhen Cui Zhongmin Yan School of software Shandong UniversityJinan250101China

Exploring open fields with coordinated unmanned vehicles is popular in academia and *** of the most impressive applicable approaches is the Internet of Vehicles(lov).The IoV connects vehicles,road infrastructures and communication facilities to provide solutions for exploration ***,the coordination of acquiring information from multi-vehicles may risk data *** this end,sharing high-quality experiences instead of raw data has become an urgent *** paper employs a Deep Reinforcement Learning(DRL)method to enable IoVs to generate training data with prioritized experience and states,which can support the IoV to explore the environment more ***,a Federated Learning(FL)experience sharing model is established to guarantee the vehicles'*** numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental *** experiments also suggest that the proposed method could support agents without full information to achieve the tasks.

关键词： Internet of vehicle Deep reinforcement learning Federated learning Data privacy

An Online Algorithm Based on Replication for Using Spot Instances in IaaS Clouds

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Journal of Computer Science & Technology 2024年第1期39卷 103-115页

作者：许志伟潘丽刘士军 School of software Shandong UniversityJinan 250101China

Infrastructure-as-a-Service(IaaS)cloud platforms offer resources with diverse buying *** can run an instance on the on-demand market which is stable but expensive or on the spot market with a significant ***,users have to carefully weigh the low cost of spot instances against their poor *** instances will be revoked when the revocation event ***,an important problem that an IaaS user faces now is how to use spot in-stances in a cost-effective and low-risk *** on the replication-based fault tolerance mechanism,we propose an on-line termination algorithm that optimizes the cost of using spot instances while ensuring operational *** prove that in most cases,the cost of our proposed online algorithm will not exceed twice the minimum cost of the optimal of-fline algorithm that knows the exact future a *** a large number of experiments,we verify that our algorithm in most cases has a competitive ratio of no more than 2,and in other cases it can also reach the guaranteed competitive ratio.

关键词： Infrastructure-as-a-Service(IaaS)cloud cost management competitive analysis online algorithm spot instance

Learnware:small models do big

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Science China(Information Sciences) 2024年第1期67卷 39-50页

作者： Zhi-Hua ZHOU Zhi-Hao TAN National Key Laboratory for Novel software Technology Nanjing University

There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills,the difficulty of continual learning,the risk of catastrophic forgetting,and the leaking of data privacy/*** research efforts have been focusing on one of those concerned issues separately,paying less attention to the fact that most issues are entangled in *** prevailing big model paradigm,which has achieved impressive results in natural language processing and computer vision applications,has not yet addressed those issues,whereas becoming a serious source of carbon *** article offers an overview of the learnware paradigm,which attempts to enable users not to need to build machine learning models from scratch,with the hope of reusing small models to do things even beyond their original purposes,where the key ingredient is the specification which enables a trained model to be adequately identified to reuse according to the requirement of future users who know nothing about the model in advance.

关键词： artificial intelligence machine learning learnware

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

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Digital Communications and Networks 2024年第2期10卷 439-449页

作者： Chaopeng Guo Zhaojin Zhong Zexin Zhang Jie Song software Campus Northeastern UniversityShenyang110000LiaoningChina

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing *** accurate energy prediction approach is critical to provide measurement and lead optimization ***,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training *** paper presents a novel energy prediction model,*** treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy *** has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and *** results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture *** code is available at https://***/NEUSoftGreenAI/***.

关键词： Internet of things Neural network energy prediction Graph neural networks Graph structure embedding Multi-head attention

A buffer overflow detection and defense method based on RiSC-V instruction set extension

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Cybersecurity 2024年第2期7卷 28-43页

作者： Chang Liu Yan-Jun Wu Jing-Zheng Wu Chen Zhao Intelligent software Research Center Institute of SoftwareChinese Academy of SciencesBejingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingChina

Buffer overflow poses a serious threat to the memory security of modern operating *** overwrites the con-tents of other memory areas by breaking through the buffer capacity limit,destroys the system execution environ-ment,and provides implementation space for various system attacks such as program control flow *** makes it a wide range of harms.A variety of security technologies have been proposed to deal with system security problems including buffer *** example,No eXecute(NX for short)is a memory management technology commonly used in Harvard *** can refuse the execution of code which residing in a specific memory,and can effectively suppress the abnormal impact of buffer overflow on control ***,in recent years,it has also been used in the field of system security,deriving a series of solutions based on NX technology,such as ExecShield,DEP,StackGuard,***,these security solutions often rely too much on the processor archi-tecture so that the protection coverage is insufficient and the accuracy is *** in the emerging system architecture field represented by RiSC-V,there is still a lack of effective solutions for buffer overflow *** the continuous rapid development of the system architecture,it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security ***,we propose BOP,A new system memory security design method based on RISC-V extended instructions,to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in *** to this method,NX technology can be combined with program control flow analysis,and Nx bit mechanism can be used to manage the executability of memory space,so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system

关键词： RISc-V Operating system security Buffer overflow Control flow hijacking Nx bit Xibop

An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3535-3563页

作者： Younghoon Ban Myeonghyun Kim Haehyun Cho School of software Soongsil UniversitySeoul06978Korea

Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware *** the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based *** antivirus systems,in particular,have been identified as vulnerable to such *** paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware *** attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack *** directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack *** a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based ***,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality *** thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware ***,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense *** several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation *** conclusion,our defense model alleviates the limitation of the mos

关键词： Malware classification machine learning adversarial examples evasion attack cybersecurity

IoTDQ: An Industrial IoT Data Analysis Library for Apache IoTDB

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Big Data Mining and Analytics 2024年第1期7卷 29-41页

作者： Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang School of software Tsinghua UniversityBeijing 100084China National Engineering Research Center for Big Data software(NERCBDS) Tsinghua UniversityBeijing 100084China

There is a growing demand for time series data analysis in industry *** loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O *** User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB *** satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache *** library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,*** enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability *** provides a framework for users to examine loT time series with data quality *** show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关键词： industrial big data data quality data mining and analytics

Key points and visible part fusion attention network for occluded pedestrian detection in traffic environments

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Optoelectronics Letters 2024年第7期20卷 430-436页

作者： LIU Peiyu MA Yixuan School of software Engineering Beijing Jiaotong University

Aiming at the problem of low detection accuracy of occluded pedestrian in traffic environments,this paper proposes a key points and visible part fusion network for occluded pedestrian *** proposed algorithm constructs two attention modules by introducing human key points and the bounding box of visible parts respectively,which suppresses the occluded parts in the channel features and spatial features of pedestrian features *** results on CityPersons and Caltech datasets demonstrate the effectiveness of the proposed *** missing rate(MR) is reduced to 40.78 on the Heavy subset of the CityPersons dataset and surpasses many outstanding methods.

关键词： A