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Predicting software vulnerability based on software metrics: a deep learning approach

Iran Journal of Computer science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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A Secure Container Placement Algorithm Based on Microservice Invocation Criticality 2

A Secure Container Placement Algorithm Based on Microservice...

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2nd IEEE International Conference on Control, Electronics and Computer technology, ICCECT 2024

作者： Li, Mingyang Hu, Hongchao Liu, Wenyan Zhengzhou University Cyber Science and Engineering Zhengzhou China Institute of Information Technology Information Engineering University Zhengzhou China Institute of Information Technology Information Engineering University Key Laboratory of Cyberspace Ministry of Education Zhengzhou China

ISBN: (纸本)9798350380958

The lightweight and efficient characteristics of container technology make it gradually become an important player in cloud computing. The characteristics of shared kernel and operating system make the risk of container co-resident attacks increasingly severe, while the complex calling relationship between microservices exacerbates the risk of co-resident attacks. The existing container placement algorithms only consider load balancing, the existing container placement algorithms either only consider load balancing, or do not fully consider all aspects of security risks that may be brought by container co-resident attacks. Aiming at the problems existing in the current research, this paper establishes a multi-application container cloud threat model to evaluate the risk of co-resident attacks within and between nodes, and comprehensively considers the risk of co-resident threats spreading with the service chain and the load balance of the system. On this basis, the Secure Container Placement Algorithm (SmCPA) is proposed based on the criticality of microservice invocations, which evaluates and ranks the criticality of microservices, and optimizes the container placement process step by step. Finally, the experimental results show that compared with the existing algorithms, SmCPA algorithm reduces the risk of co-resident attack by 17.38%, and improves the load balancing by 13.39%. © 2024 IEEE.

关键词： Cloud computing

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CBWF+: Collaboratively Enhanced Lightweight Circular-Boundary-Based WiFi Fingerprinting

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IEEE Internet of Things Journal 2025年第1期12卷 650-662页

作者： Tao, Ye Tan, Shaolin Yan, Rongen Liu, Nian Wang, Wei Lü, Jinhu Zhongguancun Laboratory Beijing100094 China The School of Information Science and Technology The Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing Forestry University Beijing100083 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Zhongguancun Laboratory Beijing100094 China

—Users upload their own WiFi localization request signals to match against the received signal strengths (RSSs) in an offline constructed fingerprint database, thereby obtaining their own geographic location estimates. However, the relationships between the measured signals of users are seldom explored for enhancing the localization accuracy. Furthermore, the tedious site surveys required for building an offline fingerprint database hinder the wider application of this technology. To address the above issues, this article proposes a collaboratively enhanced lightweight WiFi localization algorithm named CBWF+, capable of providing an accurate indoor position with low-overhead fingerprints and collaborative localization. Specifically, we first discretize the area of interest to produce virtual points (VPs), and leverage the concept of circular-boundary-based localization to select a few VPs that share similar signal characteristics with the request signals. Then, important users for collaborative localization are chosen by two new proposed indicators. Next, based on the selection results and the RSS relationships among users, the spatiotemporal characteristics of WiFi signals are used to further filter out VPs that contradict the relationship between RSS and physical location. Finally, the credible VPs are used to obtain a high-accuracy estimate, by a proposed weighting method. The results of the real-world experiments validate the effectiveness of our proposed CBWF+ algorithm, compared to other state-of-the-art approaches (e.g., NN, OCLoc, CBWF, etc.). Specifically, in a 40-m × 17-m real scenario with only 20 reference points (RPs) and 11 access point (APs), our algorithm achieves an average localization accuracy of 2.49 m. Our codes are available at: https://***/dadadaray/CBWF2.0. © 2024 IEEE.

关键词： Wi-Fi

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Kolmogorov-Arnold Networks Based Signal Detection for OTFS systems in LEO Satellite Communications 14th

Kolmogorov-Arnold Networks Based Signal Detection for OTFS ...

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14th EAI International Conference on Wireless and Satellite systems, WiSATS 2024

作者： Zhang, Yakai He, Jiayi Liu, Zhiyong School of Information Science and Engineering of HIT Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems of Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031861956

In numerous studies, orthogonal time-frequency space (OTFS) has been utilized in satellite-terrestrial communication systems with high mobility due to its strong adaptability to Doppler shifts and delays. Signal detection, a crucial technology influencing OTFS performance, is often hindered by high complexity or suboptimal detection capabilities. To address this, we propose a Kolmogorov-Arnold Networks (KANs)-based OTFS signal detection method for low Earth orbit satellite (LEO-Sat) communication systems. KANs employ learnable activation functions instead of traditional learnable linear weights between network nodes, enabling dynamic activation functions to enhance model accuracy. We integrate KANs, trained offline, into the LEO-Sat system to recover distorted signals at the receiver, facilitating effective signal detection. Compared to conventional detection algorithms, the proposed method demonstrates superior Bit Error Rate (BER) performance. Additionally, KANs are more parameter-efficient than DNNs, using significantly fewer parameters. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Bit error rate

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Aerial Target Grouping based on Sparse Subspace Clustering with the Mixed constraints of Distance and Velocity 9

Aerial Target Grouping based on Sparse Subspace Clustering w...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Zhang, Peng Mei, Fengjia Liu, Zonghao Xie, Ke Li, Yuanman Li, Yanshan Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Nanjing Research Institute of Electronic Engineering Science and Technology on Information Systems Engineering Laboratory Nanjing210023 China

ISBN: (纸本)9798350330076

Aerial target grouping, partitioning targets into some clusters according to the features of attribute and behavior, contributes to tactical recognition and command decision-making. For the scenario of high-dimensional data in aerial target grouping, sparse subspace clustering is expected to be one of the effective methods, where high-dimensional data are represented by low dimensional subspaces using sparsity. In order to achieve aerial target grouping by sparse subspace clustering efficiently and accurately, an improved sparse subspace clustering with the mixed constraints of distance and velocity (SSC_MC) is proposed. First, k nearest neighbours algorithm is used to filter neighbours, so as to improve the self-represention by the whole data set into only by their nearest neighbours;next, the mixed constraints of distance and velocity are introduced in sparse subspace clustering, which emphasizes the importance of these features in aerial target grouping;last, the experiments for aerial target grouping are implemented, showing the proposed algorithm SSC_MC can be used to aerial target grouping with high efficiency and accuracy. © 2023 IEEE.

关键词： Decision making

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Enhancing stability and safety:A novel multi-constraint model predictive control approach for forklift trajectory

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IET Cyber-systems and Robotics 2024年第4期6卷 32-47页

作者： Yizhen Sun Junyou Yang Donghui Zhao Moses Chukwuka Okonkwo Jianmin Zhang Shuoyu Wang Yang Liu School of Electrical Engineering Shenyang University of TechnologyShenyangChina Intelligent Robot Laboratory Shenyang Open UniversityShenyangChina School of Information Engineering Shenyang UniversityShenyangChina Department of Intelligent Mechanical Systems Engineering Kochi University of TechnologyKochiJapan

The advancements in intelligent manufacturing have made high-precision trajectory tracking technology crucial for improving the efficiency and safety of in-factory cargo *** study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control(ESS-MPC)*** approach includes a multi-constraint strategy for improved stability and *** kinematic model for a single front steeringwheel forklift vehicle is constructed with all known state quantities,including the steering angle,resulting in a more accurate model description and trajectory *** ensure vehicle safety,the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS-MPC *** optimisation constraints are also updated with the key kinematic and dynamic parameters of the *** ESSMPC method improved the position and pose accuracy and stability by 57.93%,37.83%,and 57.51%,respectively,as demonstrated through experimental validation using simulation and real-world *** study provides significant support for the development of autonomous navigation systems for industrial forklifts.

关键词： automatic guided vehicles mobile robots robot manipulator tracking control

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Development of A Virtual Aerial Display Considering Interaction with 3DCG Objects 22

Development of A Virtual Aerial Display Considering Interact...

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22nd International Symposium on Communications and information Technologies, ISCIT 2023

作者： Saito, Hideaki Imamura, Hiroki Graduate School of Science and Technology Department of Information Systems Engineering Tokyo Japan

ISBN: (纸本)9781665457316

In recent years, aerial displays that enable users to experience three-dimensional images are attracting attention. The reason why they are attracting attention is that they are expected to be used in various fields, such as for meetings at workplaces for a long-term using stereoscopic images, which have not been realized so far, and for applications in advertising. Therefore, the purpose of this research is to create a system that allows a single user to actually move their head to view 3D objects projected on a PC, and to develop a system that allows interaction while viewing stereoscopic images via an aerial display. © 2023 IEEE.

关键词： Antennas

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