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RanViz: Ransomware Visualization and Classification Based on Time-Series Categorical Representation of API Calls

IEEE ACCESS 2025年 13卷 56237-56254页

作者： Mokoma, Vhuhwavho Singh, Avinash Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

Ransomware continues to pose a significant threat to individuals and organizations worldwide, causing disruptions, financial losses, and reputational damage. As ransomware attacks grow in sophistication, understanding their behaviour through effective analysis has become increasingly critical for mitigation and prevention. However, ransomware analysis presents several challenges. First, the sheer volume of application programming Interface (API) call data generated by ransomware during execution can overwhelm traditional analysis methods. Second, the temporal and categorical nature of this data makes identifying meaningful patterns complex. Third, the integration of machine learning (ML) models, which are essential for accurate classification, is hindered by the difficulty of modelling intricate API call behaviours. Without effective tools to address these issues, analysts risk missing critical behavioural indicators. To overcome these challenges, the proposed Ransomware Visualization (RanViz) system was developed to provide a comprehensive visual analytics and classification platform designed to enhance ransomware analysis. RanViz employs advanced visualization techniques to represent categorical API call time-series data, enabling analysts to intuitively understand ransomware behaviours that might otherwise remain obscured. The system incorporates ML models based on API call frequency, temporal interval, and sequence to classify unknown samples as either benign or ransomware. The models collectively achieve an accuracy of over 95% in detecting ransomware. By providing a unified platform that combines powerful visualization tools with high-performing ML models, RanViz simplifies ransomware analysis and offers a robust framework for accurate classification. This makes it an invaluable tool for digital forensics and cybersecurity professionals tasked with addressing the ever-evolving ransomware threat.

关键词： Ransomware Malware Data visualization application programming interfaces Encryption Visual analytics Organizations Computer security Complexity theory Ransomware analysis API calls machine learning ransomware time series visualization visualization

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What has scripting ever done for us? The CSD Python application programming interface (API)

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2024年第4期57卷 1235-1250页

作者： Sykes, Richard A. Johnson, Natalie T. Kingsbury, Christopher J. Harter, Jurgen Maloney, Andrew G. P. Sugden, Isaac J. Ward, Suzanna C. Bruno, Ian J. Adcock, Stewart A. Wood, Peter A. McCabe, Patrick Moldovan, Alexandru A. Atkinson, Francis Giangreco, Ilenia Cole, Jason C. Cambridge Crystallog Data Ctr 12 Union Rd Cambridge CB2 1EZ England Exscientia plc Schrodinger BldgOxford Sci Pk Oxford OX4 4GE England

Since its first release in 2016, the Cambridge Structural Database Python application programming interface (CSD Python API) has seen steady uptake within the community that the Cambridge Crystallographic Data Centre serves. This article reviews the history of scripting interfaces, demonstrating the need, and then briefly outlines the technical structure of the API. It describes the reach of the CSD Python API, provides a selected review of its impact and gives some illustrative examples of what scientists can do with it. The article concludes with speculation as to how such endeavours will evolve over the next decade.

关键词： software drug discovery drug development materials discovery application programming interfaces Cambridge Structural Database CSD Python API

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Artificial intelligence boundary resources: a relational view on leveraging "AI-as-a-Service"

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EUROPEAN JOURNAL OF INFORMATION SYSTEMS 2025年

作者： Hanelt, Andre Firk, Sebastian Zapadka, Patryk Oehmichen, Jana Univ Kassel Chair Digital Transformat Management Kassel Germany Univ Groningen Dept Accounting & Auditing Groningen Netherlands Johannes Gutenberg Univ Mainz Human Resources & Management Studies Mainz Germany

In response to the increasing relevance of artificial intelligence (AI) for competitive advantage, firms can utilize the capabilities of AI-savvy partners via boundary resources. Due to AI facets, such as autonomy, inscrutability, and learning, this strategy holds substantial promises for productivity and innovation, yet is not free of risks, given the strategic importance of AI and potential dependencies in this key area. As the antecedents and consequences of this strategy are unclear, we build a theoretical framework about these aspects based on the relational view of competitive advantage. We test our hypotheses on a longitudinal dataset and find that internal AI knowledge and the presence of a chief information officer in the top management team are associated with selecting AI boundary resources for process improvements. High external market pressure exerted by digital ventures and the AI sophistication of industry peers are positively associated with selecting AI boundary resources for product improvements. We further find that the use of AI boundary resources for process improvements is positively associated with operational efficiency and the use of AI boundary resources for product improvements with increasing sales. We derive implications for IS research on managing AI and boundary resources, as well as managerial practice.

关键词： Boundary resources application programming interfaces artificial intelligence CIO relational view

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LLM-Driven, Self-Improving Framework for Security Test Automation: Leveraging Karate DSL for Augmented API Resilience

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IEEE ACCESS 2025年 13卷 56861-56886页

作者： Pasca, Emil Marian Delinschi, Daniela Erdei, Rudolf Matei, Oliviu Tech Univ Cluj Napoca North Univ Ctr Baia Mare Dept Elect Elect & Comp Engn Baia Mare 430122 Romania HOLISUN SRL Dept Res & Dev Baia Mare 430397 Romania

Modern software architectures heavily rely on APIs, yet face significant security challenges, particularly with Broken Object Level Authorization (BOLA) vulnerabilities, which remain the most critical API security risk according to OWASP. This paper introduces Karate-BOLA-Guard, an innovative framework leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques to automate security-focused test case generation for APIs. Our approach integrates vector databases for context retrieval, multiple LLM models for test generation, and observability tools for process monitoring. Initial experiments were carried out on three deliberately vulnerable APIs (VAmPI, Crapi, and OWASP Juice Shop), with subsequent validation on fifteen additional production APIs spanning diverse domains including social media, version control systems, financial services, and transportation services. Our evaluation metrics show Llama 3 8B achieving consistent performance (Accuracy: 3.1-3.4, Interoperability: 3.7-4.3) with an average processing time of 143.76 seconds on GPU. Performance analysis revealed significant GPU acceleration benefits, with 20-25x improvement over CPU processing times. Smaller models demonstrated efficient processing, with Phi-3 Mini averaging 69.58 seconds and Mistral 72.14 seconds, while maintaining acceptable accuracy scores. Token utilization patterns showed Llama 3 8B using an average of 36,591 tokens per session, compared to Mistral's 25,225 and Phi-3 Mini's 31,007. Our framework's effectiveness varied across APIs, with notably strong performance in complex platforms (Instagram: A = 4.3, I = 4.4) while maintaining consistent functionality in simpler implementations (VAmPI: A = 3.6, I = 4.3). The iterative refinement process, evaluated through comprehensive metrics including Accuracy (A), Complexity (C), and Interoperability (I), represents a significant advancement in automated API security testing, offering an efficient, accurate, and adapt

关键词： Security Testing Retrieval augmented generation Test pattern generators application programming interfaces Accuracy Software testing Automation Systematics Computer architecture API security automation testing tools cybersecurity restful API software testing

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On Efficient Single-Source Personalized PageRank Computation in Online Social Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第6期37卷 3598-3612页

作者： Wei, Victor Junqiu Jiang, Di Zhang, Jason Chen Macau Univ Sci & Technol Taipa 999078 Macao Peoples R China WeBank Co Ltd AI Grp Shenzhen 518054 Peoples R China Hong Kong Polytech Univ Hung Hom Hong Kong Peoples R China

The Single-Source Personalized PageRank (SSPPR) problem is widely used in information retrieval and recommendation systems. Traditional algorithms assume full knowledge of the network, making them inapplicable to online social networks (OSNs), where the topology is unknown, and users can only explore the network step by step via APIs. The only feasible approach for SSPPR in OSNs is Monte Carlo (MC) simulation, but traditional MC methods rely on static sampling, which lacks flexibility, delays feedback, and overestimates the number of required random walks. To address these limitations, we propose PANDA (Single-Source Personalized PageRank on OSNs with Rademacher Average), a progressive sampling algorithm. PANDA iteratively samples random walks in batches, estimating accuracy dynamically using Rademacher Average from statistical learning theory. This data-dependent approach allows for early termination once the desired accuracy is met. Additionally, PANDA features a dynamic sampling schedule to optimize efficiency. Empirical studies show that PANDA significantly outperforms existing methods, achieving the same accuracy with far greater efficiency.

关键词： Social networking (online) Accuracy Heuristic algorithms Monte Carlo methods Network topology Machine learning algorithms application programming interfaces User experience Training Topology Online social networks personalized pageRank rademacher average random sampling

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GNEM: Comprehensive Similarity Learning With Ensemble Model for Code Search

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IEEE ACCESS 2024年 12卷 165005-165016页

作者： Hong, Juntong Choi, Eunjong Mizuno, Osamu Kyoto Inst Technol Grad Sch Sci & Technol Kyoto 6060951 Japan Kyoto Inst Technol Fac Informat & Human Sci Kyoto 6060951 Japan

Code search is a relevant research field of software engineering, with the objective of accurately retrieving the most relevant code for a given query. However, recent deep-learning-based code search models are limited in scalability and comprehensiveness for alignment learning since these models suffer from the out-of-vocabulary problem, and affinity matrix-based cross-modal attention may lead to incorrect alignments. In this paper, we propose a novel code search model, namely the Graph Network Ensemble Model (GNEM), to address the challenges by diverse learning alignments and enhancing the similarity representation. GNEM incorporates two graph networks to learn global and fine-grained alignments for inferring comprehensive similarity. To evaluate the performance of GNEM, we compared it with baseline models using two widely used datasets. The results demonstrate that GNEM achieves a Top@1 accuracy of 0.649 and 0.702, surpassing baseline models by approximately 18.6% and 11.7% in Top@1 accuracy, respectively. We also conducted ablation experiments to show the effectiveness of each component of GNEM. Finally, we visualize the attention weights between code and query to illustrate GNEM's behaviors while code searching. The results provide insights into GNEM's effective code search capabilities.

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Parallel programming in mobile devices with FancyJCL

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12891-12909页

作者： Afonso, Sergio Gomez-Cardenes, Oscar Exposito, Paula Blanco, Vicente Almeida, Francisco Univ La Laguna ULL Comp Sci & Syst Dept San Francisco de Paula s n San Cristobal la Laguna 38270 Spain

Mobile devices and handheld systems, such as the smartphones and tablets universally extended, are becoming increasingly powerful. Their basic hardware configuration is usually state-of-the-art heterogeneous architectures consisting of multi-core processors and some kind of accelerator such as GPUs or DSPs. Specific code adapted to the architecture is mandatory if high-performance computation is required and low-level libraries and parallelism are needed, which constitutes an important barrier for the usual developer in such devices. In this context, we propose the FancyJCL framework. It provides a high-level abstraction layer that hides implementation details and allows to develop parallel programs for mobile devices. The target platform for FancyJCL is mainly Android and Java developers due to their high market penetration. A very simple, seemingly sequential encoding results in parallel efficient OpenCL code. FancyJCL is itself based on the Fancier framework, which enables optimal memory management across memory spaces on unified memory systems. Benchmarks of FancyJCL code developed for a wide range of image processing algorithms show good performance with low development effort.

关键词： application programming interfaces Hardware acceleration Heterogeneous systems Image processing Mobile computing Parallel programming Performance analysis

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GridAPPS-D Distributed App Architecture and API for Modular and Distributed Grid Operations

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IEEE ACCESS 2024年 12卷 39862-39875页

作者： Sharma, Poorva Reiman, Andrew P. Anderson, Alexander A. Poudel, Shiva Allwardt, Craig H. Fisher, Andrew R. Slay, Tylor E. Mukherjee, Monish Dubey, Anamika Ogle, James P. Melton, Ronald B. Pacific Northwest Natl Lab Richland WA 99354 USA Washington State Univ Dept Elect Engn & Comp Sci Pullman WA 99163 USA

As power distribution systems evolve in complexity and scale, the coordination and control of distributed energy resources (DERs), intelligent devices, and agents become increasingly challenging. Distribution utilities invest in advanced distribution management systems (ADMSs) and distributed energy resource management systems (DERMSs) to enhance distribution systems' reliability, resiliency, and efficiency. Standardizing the application programming Interface (API) for ADMS applications is crucial to accelerate the integration of advanced distribution technologies. This paper introduces a distributed application architecture within the GridAPPS-D platform, aiming to address the limitations of centralized architectures in terms of scalability, maintainability, and flexibility. The proposed architecture draws inspiration from the Laminar Coordination Framework and is validated through extensive stakeholder engagement. Emphasizing extensibility, boundary deference, structural scalability, and securability, the layered framework is well-suited for large-scale distribution networks with diverse grid-edge devices and ownership structures. The contributions of this paper include a distributed layered architecture with defined distributed areas, a Common Information Model (CIM)-based standardized API for developing and deploying distributed applications (Distributed App API), and the design process and reference implementation of distributed services and applications. Based on laminar coordination, the software architecture combines centralized, distributed, and edge-control paradigms for effective distributed operations. The paper concludes with extending the centralized API in GridAPPS-D to distributed APIs for standards-based message exchange, emphasizing the need for scalable communication to coordinate diverse distributed agents. This work provides a foundation for advancing the field of distributed control in power distribution systems, supporting both centralized and

关键词： Computer architecture application programming interfaces Distributed power generation Software Scalability Distributed databases Software architecture Power distribution Reliability engineering application programming interface distributed applications distribution management systems software architecture

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API Recommendation for Novice Programmers: Build a Bridge of Query-Task Knowledge Gap

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IEEE TRANSACTIONS ON RELIABILITY 2024年第3期73卷 1473-1486页

作者： Wang, Yong Fang, Yingtao Gao, Cuiyun Chen, Linjun Anhui Polytech Univ Wuhu 241000 Peoples R China Harbin Inst Technol Shenzhen 518055 Peoples R China

During software development, programmers often rely on a wide range of application programming interfaces (APIs) to facilitate their tasks. However, APIs have been growing rapidly in recent years, making it difficult for developers to choose among the many APIs that suit their programming needs. To facilitate the development process, automatic API recommendation is becoming increasingly important. Although there have been many effective research methods, these methods have a high dependence on the accuracy of the user's description of his own task, and there is a knowledge difference between the user's query and the user's actual task, increasing the difficulty of accurate API recommendation. In this article, we propose REAPI, a method to bridge the knowledge gap between the user's query and the user's actual task to improve the recommendation accuracy. The REAPI approach involves reconstructing query by tapping into Stack Overflow data to glean user intentions. Refactoring the user's query to display implicit information can better capture the user's true intentions. Specifically, we generate three candidate reconstruction statements based on natural language queries and Stack Overflow data and incorporate user feedback to refine and select the final statement. To evaluate the effectiveness of REAPI, we conducted experiments at both the class-level and method-level. Our results show that REAPI outperforms state-of-the-art baselines across key evaluation metrics such as S@1, S@3, S@10, MRR, and MAP.

关键词： Task analysis application programming interfaces Vocabulary Codes Bridges Training Software application programming interface (API) recommendation potential information display statement reconstruction

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DataPoll: A Tool Facilitating Big Data Research in Social Sciences

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年第2期12卷 511-524页

作者： Charalampous, Antonis Djouvas, Constantinos Christodoulou, Christos Cyprus Univ Technol Dept Commun & Internet Studies CY-3036 Limassol Cyprus

The computational analysis of big data has revolutionized social science research, offering unprecedented insights into societal behaviors and trends through digital data from online sources. However, existing tools often face limitations such as technical complexity, single-source dependency, and a narrow range of analytical capabilities, hindering accessibility and effectiveness. This article introduces DataPoll, an end-to-end big data analysis platform designed to democratize computational social science research. DataPoll simplifies data collection, analysis, and visualization, making advanced analytics accessible to researchers of diverse expertise. It supports multisource data integration, innovative analytical features, and interactive dashboards for exploratory and comparative analyses. By fostering collaboration and enabling the integration of new data sources and analysis methods, DataPoll represents a significant advancement in the field. A comprehensive case study on the Ukrainian--Russian conflict demonstrates its capabilities, showcasing how DataPoll can yield actionable insights into complex social phenomena. This tool empowers researchers to harness the potential of big data for impactful and inclusive research.

关键词： Big Data Data models Social sciences Pipelines Data mining application programming interfaces Data visualization Crawlers Social networking (online) Machine learning Big data in social sciences big data systems computational methods computational social science online platforms social computing

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