PeerJ Computer Science最新文献_第6页

SH-SDS: a new static-dynamic strategy for substation host security detection.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-22 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2512

Yang Diao, Hui Chen, Wei Liu, Abdur Rasool

{"title":"SH-SDS: a new static-dynamic strategy for substation host security detection.","authors":"Yang Diao, Hui Chen, Wei Liu, Abdur Rasool","doi":"10.7717/peerj-cs.2512","DOIUrl":"10.7717/peerj-cs.2512","url":null,"abstract":"A substation is integral to the functioning of a power grid, enabling the efficient and safe transmission and distribution of electrical energy to meet the demands of consumers. The digital transformation of critical infrastructures, particularly in the electric power sector, such as the emergence of intelligent substations, is a double-edged sword. While it brings about efficiency improvements and consumer-centric advancements, it raises concerns about the heightened vulnerability to cyberattacks. This article proposes a new static-dynamic strategy for host security detection by implementing a system prototype and evaluating its detection accuracy. To reduce the subjectivity in manually selecting features, we combine classified protection for cybersecurity-related standards and construct the requirement generation algorithm to construct a network security detection standard library for the substation host. Based on this, we develop strategy generation algorithm to match the list of host detection projects to obtain the security detection strategy of the target host. Moreover, we output and analyze the detection logs to obtain a security detection report. The prototype is efficient and effective through practical use, and it serves as a practical tool in substation host security detection. The experiments suggest that the mechanism proposed in our study can operate at a high speed and demonstrates satisfactory performance in terms of detection.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2512"},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient phrase search with reliable verification over encrypted cloud-IoT data.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-22 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2235

Wanshan Xu, Ze Zhu, Muhammad Irfan Khalid

{"title":"Efficient phrase search with reliable verification over encrypted cloud-IoT data.","authors":"Wanshan Xu, Ze Zhu, Muhammad Irfan Khalid","doi":"10.7717/peerj-cs.2235","DOIUrl":"https://doi.org/10.7717/peerj-cs.2235","url":null,"abstract":"Phrase search encryption enables users to retrieve encrypted data containing a sequence of consecutive keywords without decrypting, which plays an important role in cloud Internet of Things (IoT) systems. However, due to the sequential relationship between keywords in the phrase, phrase search and verification are more difficult than multi-keyword search. Furthermore, verification evidence is generated by the server in existing schemes, and cloud servers are generally considered untrustworthy, so the verification is unreliable. To address this, we propose an efficient phrase search scheme that supports reliable verification of search results, where blockchain is introduced to generate verification evidence and perform verification of the results. The immutable nature of blockchain guarantees the credibility of evidence and verification. During the verification, we use a multiset hash function to generate aggregated evidence, reducing storage and blockchain transaction costs. In addition, we design a novel composite index and discrimination algorithm based on homomorphic encryption, with which we can quickly identify phrases and improve search efficiency. Finally, we conducted security analysis and detailed experiments on our scheme, which proved that the scheme is secure and efficient.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2235"},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive review of gait analysis using deep learning approaches in criminal investigation.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-22 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2456

Sai Thu Ya Aung, Worapan Kusakunniran

{"title":"A comprehensive review of gait analysis using deep learning approaches in criminal investigation.","authors":"Sai Thu Ya Aung, Worapan Kusakunniran","doi":"10.7717/peerj-cs.2456","DOIUrl":"10.7717/peerj-cs.2456","url":null,"abstract":"Despite the growing worries expressed by privacy supporters about the extensive adoption of gait biometrics, research in this field has been moving forward swiftly. Deep learning, a powerful technology that enables computers to learn from data, has found its way into criminal investigations involving gait. In this survey, the literature of gait analysis concerning criminal investigation is discussed with a comprehensive overview of developments in gait analysis with deep neural networks. Firstly, terminologies and factors regarding human gait with scenarios related to crime are discussed. Subsequently, the areas and domains corresponding to criminal investigation that can be tackled by gait analysis are discussed. Also, deep learning methods for gait analysis and how they can be applied in criminal investigations are presented. Then, gait analysis techniques and approaches using deep learning methods including currently available datasets are mentioned. Moreover, crime-related video datasets are presented with literature on deep learning-based anomaly detection with gait human poses. Finally, challenges regarding gait analysis in criminal investigations are presented with open research issues.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2456"},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From attributes to communities: a novel approach in social network generation.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-22 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2483

Muhtar Çağkan Uludağlı, Kaya Oğuz

{"title":"From attributes to communities: a novel approach in social network generation.","authors":"Muhtar Çağkan Uludağlı, Kaya Oğuz","doi":"10.7717/peerj-cs.2483","DOIUrl":"10.7717/peerj-cs.2483","url":null,"abstract":"Generating networks with attributes would be useful in computer game development by enabling dynamic social interactions, adaptive storylines, realistic economic systems, ecosystem modelling, urban development, strategic planning, and adaptive learning systems. To this end, we propose the Attribute-based Realistic Community and Associate NEtwork (ARCANE) algorithm to generate node-attributed networks with functional communities. We have designed a numerical node attribute-edge relationship computation system to handle the edge generation phase of our network generator, which is a different method from our predecessors. We combine this system with the proximity between nodes to create more life-like communities. Our method is compared against other node-attributed social network generators in the area with using both different evaluation metrics and a real-world dataset. The model properties evaluation identified ARCANE as the leading generator, with another generator ranking in a tie for first place. As a more favorable outcome for our approach, the community detection evaluation indicated that ARCANE exhibited superior performance compared to other competing generators within this domain. This thorough evaluation of the resulting graphs show that the proposed method can be an alternate approach to social network generators with node attributes and communities.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2483"},"PeriodicalIF":3.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A distributed algorithm for solving large-scale p-median problems using expectation maximization.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-21 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2446

Harsha Gwalani, Joseph Helsing, Sultanah M Alshammari, Chetan Tiwari, Armin R Mikler

{"title":"A distributed algorithm for solving large-scale p-median problems using expectation maximization.","authors":"Harsha Gwalani, Joseph Helsing, Sultanah M Alshammari, Chetan Tiwari, Armin R Mikler","doi":"10.7717/peerj-cs.2446","DOIUrl":"10.7717/peerj-cs.2446","url":null,"abstract":"The p-median problem selects p source locations to serve n destinations such that the average distance between the destinations and corresponding sources is minimized. It is a well-studied NP-hard combinatorial optimization problem with many existing heuristic solutions, however, existing algorithms are not scalable for large-scale problems. The fast interchange (FI) heuristic which yields results close to the optimal solution with respect to the objective function value becomes suboptimal with respect to time requirements for large-scale problems. We present a novel distributed divide and conquer algorithm, EM-FI, to solve large-scale p-median problems quickly even with limited computing resources. The algorithm identifies the existing spatial clusters of the destination locations using expectation maximization (EM) and solves them as independent p-median problems using integer programming or FI concurrently. The proposed algorithm showed an order of magnitude improvement in time without the loss of quality in terms of the objective function value on synthetic and real datasets.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2446"},"PeriodicalIF":3.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A transformer-based framework for enterprise sales forecasting.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-21 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2503

Yupeng Sun, Tian Li

{"title":"A transformer-based framework for enterprise sales forecasting.","authors":"Yupeng Sun, Tian Li","doi":"10.7717/peerj-cs.2503","DOIUrl":"10.7717/peerj-cs.2503","url":null,"abstract":"Sales forecasting plays an important role in business operations as it impacts decisions on inventory management, allocation of resources, and financial planning. Accurate sales predictions are essential for optimizing cash flow management, adapting marketing and sales strategies, and facilitating strategic planning. This study presents a computational framework for predicting business sales using transformers, which are considered one of the most powerful deep learning architectures. The design of our model is specifically tailored to accommodate tabular data with low dimensions. The experimental results demonstrated that our proposed method surpasses conventional machine learning models, achieving reduced mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), as well as higher R2 values of nearly 0.95. The results confirmed that the model is applicable not only to this research but also to similar studies that use low-dimensional tabular data. The improved accuracy and stability of our model demonstrate its potential as a useful tool for enhancing sales prediction, therefore facilitating more informed decision-making and strategic planning in corporate operations.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2503"},"PeriodicalIF":3.5,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the effectiveness of online courses in international trade using deep learning.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-21 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2509

Zhaozhe Zhang, Javier De Andrés

引用次数: 0

Stylometry for real-world expert coders: a zero-shot approach.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-20 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2429

Andrea Gurioli, Maurizio Gabbrielli, Stefano Zacchiroli

{"title":"Stylometry for real-world expert coders: a zero-shot approach.","authors":"Andrea Gurioli, Maurizio Gabbrielli, Stefano Zacchiroli","doi":"10.7717/peerj-cs.2429","DOIUrl":"10.7717/peerj-cs.2429","url":null,"abstract":"Code stylometry is the application of stylometry techniques to determine the authorship of software source code snippets. It is used in the industry to address use cases like plagiarism detection, code audits, and code review assignments. Most works in the code stylometry literature use machine learning techniques and (1) rely on datasets coming from in vitro coding competition for training, and (2) only attempt to recognize authors present in the training dataset (in-distribution authors). In this work we give a fresh look at code stylometry and challenge both these assumptions: (1) we recognize expert authors who contribute to real-world open-source projects, and (2) we show how to accurately recognize authors not present in the training set (out-distribution authors). We assemble a novel open dataset of code snippets for code stylometry tasks consisting of 114,400 code snippets, authored by 104 authors having contributed 1,100 snippets each. We develop a K-nearest neighbors algorithm (k-NN) classifier for the code stylometry task and train it on the dataset. Our system achieves a top accuracy of 69% among five randomly selected in-distribution authors, thus improving state of the art by more than 20%. We also show that when moving from in-distribution to out-distribution authors, the classification performances of the k-NN classifier remain the same, achieving a top accuracy of 71% among five randomly-selected out-distribution authors.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2429"},"PeriodicalIF":3.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-20 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2515

Ramnath M, Yesubai Rubavathi C

{"title":"Enhancing AppAuthentix recommender systems using advanced machine learning techniques to identify genuine and counterfeit android applications.","authors":"Ramnath M, Yesubai Rubavathi C","doi":"10.7717/peerj-cs.2515","DOIUrl":"10.7717/peerj-cs.2515","url":null,"abstract":"Smartphone app expansion needs strict security measures to avoid fraud and danger. This study overcomes this issue by identifying apps differently. This new solution uses convolutional neural network (CNN), natural language processing (NLP), and the strong AppAuthentix Recommender algorithm to secure app stores and boost customer confidence in the digital marketplace. Since the app ecosystem has grown, counterfeit and harmful applications have risen, threatening consumers and app merchants. These risks need advanced technology that can distinguish malware from legitimate apps. A complex prediction model using CNNs for image analysis, NLP for text feature extraction, and the novel AppAuthentix Recommender algorithm to properly identify legitimate and counterfeit mobile applications is the goal of this research. The whole strategy secures app stores and authenticates apps. The urgent need to safeguard app markets and users against unauthorized and hazardous programs sparked this study. Our cutting-edge solutions make mobile app consumers' digital lives safer and app marketplaces more trustworthy. CNN, NLP, and AppAuthentix Recommender yielded amazing results in this investigation. Mobile app authenticity may be estimated with 98.25% accuracy. This technology greatly improves app store security and enables mobile app verification. In conclusion, our work offers a novel way to app identification at a time of rapid mobile app development. CNN, NLP, and AppAuthentix Recommender have dramatically enhanced app store security. These new solutions may boost mobile app security and consumer confidence.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2515"},"PeriodicalIF":3.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probability-boosting technique for combinatorial optimization.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-20 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2499

Sanpawat Kantabutra

引用次数: 0