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Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

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IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Sariyer Istanbul34467 Turkey Wingie Enuygun Group Atasehir Istanbul34750 Turkey University of Bakircay Department of Computer Engineering Menemen Izmir35665 Turkey Istanbul Technical University Department of Data Science and Analytics Sariyer Istanbul34467 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Sariyer Istanbul34467 Turkey

This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI’s ChatGPT 4o and ChatGPT 4o-mini, Anthropic’s Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google’s Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in Application programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1–4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI’s ChatGPT and Anthropic’s Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

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Net-GPT: A LLM-Empowered Man-in-the-Middle Chatbot for Unmanned Aerial Vehicle 23

Net-GPT: A LLM-Empowered Man-in-the-Middle Chatbot for Unman...

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Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing

作者： Brett Piggott Siddhant Patil Guohuan Feng Ibrahim Odat Rajdeep Mukherjee Balakrishnan Dharmalingam Anyi Liu Department of Computer Science and Engineering Oakland University Rochester Michigan USA Department of Computer Data & Information Sciences University of Wisconsin-Madison Madison Wisconsin USA

ISBN: (纸本)9798400701238

In the dynamic realm of AI, integrating Large Language Models (LLMs) with security systems reshape cybersecurity. LLMs bolster defense against cyber threats but also introduce risks, aiding adversaries in generating malicious content, discovering vulnerabilities, and distorting perceptions. This paper presents Net-GPT, an LLM-empowered offensive chatbot that understands network protocols and launches Unmanned Aerial Vehicles (UAV)-based Man-in-the-middle (MITM) attacks against a hijack communication between UAV and Ground Control Stations (GCS). Facilitated by an edge server equipped with finely tuned LLMs, Net-GPT crafts mimicked network packets between UAV and GCS. Leveraging the adaptability of popular LLMs, Net-GPT produces context-aligned network packets. We fine-tune and assess Net-GPT's LLM-based efficacy, showing its impressive generative accuracy: 95.3% for Llama-2-13B and 94.1% for Llama-2-7B. Smaller LLMs, such as Distil-GPT-2, reach 77.9% predictive capability of Llama-2-7B but are 47× faster. Cost-efficiency tests highlight model quality's impact on accuracy while fine-tuning data quantity enhances predictability on specific metrics. It holds great potential to be used in edge-computing environments with amplified computing capability.

关键词： man-in-the-middle (MITM)

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Threat Modeling for Honeypot Deployment

Threat Modeling for Honeypot Deployment

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Information Technology International Seminar (ITIS)

作者： Kalpin Erlangga Silaen Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes.

关键词： Threat modeling Seminars Firewalls (computing) Filtering Prevention and mitigation Complexity theory Security Information technology Monitoring Cyberattack

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A Deep Learning Approach for Classification of Medicare Beneficiaries Based on Gender and being Affected with Cancer

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Procedia computer science 2023年 218卷 532-541页

作者： Manish K. Pandey Karthikeyan Subbiah Centre for Quantitative Economics and Data Science Birla Institute of Technology Mesra Ranchi-835215 Jharkhand India Department of Computer Science Institute of Science Banaras Hindu University Varanasi-221005 Uttar Pradesh India

With the advent of the Third computing platform of Social Mobility Analytics and Cloud (SMAC), data is getting generated in huge amounts. This huge amount of data is collected for domain-specific information to process them to get required domain-specific information as in real-time health analytics, financial frauds, real-time automated car driving, vital information of patients undergoing robotic surgery, handling cyber threats etc. This huge data, also known as Big data, is highly unstructured and imbalanced that is not possible for traditional techniques to handle and process. Advancements in computing power, speedy data storage and convergence of SMAC technologies have also contributed to the swift acceptance of the technology. This led to innovative analytical techniques that are data as well as computation intensive. One such technique is Deep Learning which originated from the artificial neural network and found its use in handling many real-life problems involving multidimensional features. The advantage of Feature Learning or Representational Learning makes Deep Learning a wonderful tool for big data analytics. The previous level of hierarchy transfers the feature learning to the next levels and thus complex features are learned through the learning of simpler features at different levels of abstraction. For efficient learning of these features, tuning of hyper-parameters is a mandatory step. The current work incorporates Grid Search for classification to find the best classifier for the classification of Medicare beneficiaries based on two scenarios. The first Scenario is beneficiaries who are affected by cancer and the Second Scenario is where Medicare beneficiaries are provided Gender wise (being a Female beneficiary). By experimenting using these algorithms at 10-fold cross-validation, the best results were achieved in the sensitivity of 99.17 %, Specificity of 97.68 % and accuracy of 98.8 % with Deep Learning Neural Network with Dropout for First Scen

关键词： Deep Learning DLNNWD DLNNWOD SMAC Random Forest SDG

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Enhancing Intrusion Detection Systems Using Ensemble Machine Learning Techniques

Enhancing Intrusion Detection Systems Using Ensemble Machine...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Isaac Adedoyin Oyebade Isaac Oluwafemi Elesemoyo Mercy Oka Ebute Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Akpan Caleb Solomon Agbotiname Lucky Imoize Kehinde Amudat Odulana Department of Computer Science Bowen University Iwo Nigeria Department of Computer Engineering Elizade University Ilara-Mokin Nigeria Department of Accounting Anderson School of Management University of New Mexico USA Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria School of Physics Engineering and Computer Science (Data Science and Analytics) University of Hertfordshire

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Intrusion detection systems are an essential part of the current cybersecurity environment, and IDS devices are intended to scan the activity of the network and systems to identify possible improper actions or violations. IDSs work in analyzing the traffic of networks and the traffic of networks and the behavior of systems, distinguishing suspicious information and signs of violation of legal access. This capability is critical for protecting information systems’ characteristics, such as integrity, confidentiality, and availability. Useful data was collected from Kaggle data archives, and several base models such as K-Nearest Neighbor, Random Forest, Logistic Regression, and Decision Tree classifier will be used. Their predictions are combined using a Voting Classifier. The models are trained and evaluated based on accuracy, precision, recall, and F1-Score. Results show that the Voting classifier achieved an accuracy of 99.78%, with a precision of 99.67% and recall of 99.92% for intrusion detection. The Logistic Regression and K-Nearest Neighbor models achieved accuracies of 95.37% and 99.52%, respectively, while having a precision of 94.93% and 99.51%, respectively. The Decision Tree and Random Forest models achieved accuracies of 99.76% and 99.86%, respectively, while having a precision of 99.75% and 99.83%, respectively. These findings suggest ensemble techniques significantly enhance intrusion detection, making networks safer.

关键词： Logistic regression Accuracy Sensitivity Intrusion detection Telecommunication traffic Nearest neighbor methods Classification algorithms Decision trees Object recognition Random forests

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep Learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep Learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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2D Animation - Q Version of the AIDS Prevention Short Video 10

2D Animation - Q Version of the AIDS Prevention Short Video

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10th International Conference on Orange Technology, ICOT 2022

作者： Wei, Chun-Cheng Hsieh, Hui-Ming Hung, Long-Chen Chen, Ru-Yi Huang, Jiang-Fang Lu, Xin-Chun Zhong, Yu-Shan School of Software and Big Data Changzhou College of Information Technology Changzhou China Taiwan Shoufu University Department of Computer Science and Multimedia Design Tainan Taiwan Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan

ISBN: (纸本)9781665454193

AIDS is a sexually transmitted disease. The medical term is called Acquired Immune Deficiency Syndrome. Medical methods such as cocktail therapy and anticancer drugs can kill AIDS. Although there are currently cured cases, prevention is still required to prevent the infection from expanding;prevention Ways, like the AIDS vaccine or preventive safe sex, can reduce the risk of infection. We hope that through the production of animated short films for viewing, we can help reduce the HIV infection rate. This short film is presented as a story similar to a micro-film by ourselves. And using the modern scene and the character design of the Q version as the material, the story content is made into 2D animation with Adobe Flash CS5. © 2022 IEEE.

关键词： Animation

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对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学对比分析

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Journal of Central South University 2023年第8期30卷 2616-2626页

作者： KHALID Abdulkhaliq M-alharbi HINA Gul MUHAMMAD Ramzan SEIFEDINE Kadry ABDULKAFI Mohammed-saeed Mechanical Engineering Department College of EngineeringUmm Al-Qura UniversityMakkah 24382Sandi Arabia Department of Computer Science Bahria UniversityIslamabad 44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题... 详细信息

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题由一组方程组成,借助MATLAB bvp4c包进行求解。为了使参数与相关剖面的影响可视化,给出了图形说明,并利用图表来评估关键参数变化是如何影响相关场及其相应的物理量。通过对盐1和盐2的Dufour参数的多次估计,可以推断出流体流速的波动。验证了流体动力流相对于流体磁流的优势。此外,还将特定情景下已发表的结果与当前研究结果进行了比较。

关键词：三重扩散纳米流体二次混合对流可变导热系数非线性热辐射

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Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

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computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

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Twitter Social Media Sentiment Analysis of Telecommunications Service Provider Using Long Short-Term Memory Method

Twitter Social Media Sentiment Analysis of Telecommunication...

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2021 International Conference on Artificial Intelligence and Big data Analytics, ICAIBDA 2021

作者： Nabbani, Fauzi Faruq Yulita, Intan Nurma Suryana, Ino Universitas Padjadjaran Department of Computer Science Bandung Indonesia Universitas Padjadjaran Research Center for AI and Big Data Department of Computer Science Bandung Indonesia

ISBN: (纸本)9781665408905

As one of the telecommunication companies in Indonesia, Provider X needs to pay attention to the services that their products provide for their users. This needs to be done so that their consumers don't switch to a different provider. To get the answers or information regarding the improvement of services, companies can process or analyze the users' sentiment toward their products through the Twitter social media. The purpose of this research is to analyze the sentiments of Provider X's tweet data of 5.375 on December 24, 2019 to February 24, 2020. The collected data is classified into two classes, negative and positive. This research uses the Long Short-Term Memory method. Hyperparameter testing is done to get the most optimal model. The test produced Word2vec hyperparameter including dimensions of 50, window size 5, epoch 50 and learning rate 0.025. In LSTM hyperparameter, optimal conditions are obtained when the number of LSTM layers is one, five LSTM units, the activation function is sigmoid, the optimizer is Nadam, the recurrent dropout size is 0.2 and the batch size is 32. The LSTM model is evaluated using K- fold cross validation and produces an average accuracy of 97.53%, f1score 97.39% and a loss of 10.36%. © 2021 IEEE.

关键词： Long short-term memory

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