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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

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Reverse Engineered MiniFS File System 24

Reverse Engineered MiniFS File System

引用

19th international conference on Availability, Reliability, and Security (ARES)

作者： Belimov, Dmitrii Vinogradov, Evgenii Technol Innovat Inst Abu Dhabi U Arab Emirates

ISBN: (纸本)9798400717185

In an era where digital connectivity is increasingly foundational to daily life, the security of Wi-Fi Access Points (APs) is a critical concern. this paper addresses the vulnerabilities inherent in Wi-Fi APs, with a particular focus on those using proprietary file systems like MiniFS found in TP-Link's AC1900 WiFi router. through reverse engineering, we unravel the structure and operation of MiniFS, marking a significant advancement in our understanding of this previously opaque file system. Our investigation reveals not only the architecture of MiniFS but also identifies several private keys and underscores a concerning lack of cryptographic protection. these findings point to broader security vulnerabilities, emphasizing the risks of security-by-obscurity practices in an interconnected environment. Our contributions are twofold: firstly, based, on the file system structure, we develop a methodology for the extraction and analysis of MiniFS, facilitating the identification and mitigation of potential vulnerabilities. Secondly, our work lays the groundwork for further research into WiFi APs' security, particularly those running on similar proprietary systems. By highlighting the critical need for transparency and community engagement in firmware analysis, this study contributes to the development of more secure network devices, thus enhancing the overall security posture of digital infrastructures.

关键词： Reverse engineering Binary Analysis Data Extraction File System

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Deep Learning-Based Data Drift Detection of Image Rotation in Surveillance Videos 19

Deep Learning-Based Data Drift Detection of Image Rotation i...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Kim, Yongseong Kim, Bumyoon Jeon, Byeungwoo Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Surveillance systems are actively being in use for public safety. Using CCTV video data, such tasks as object tracking, and face recognition can be performed. However, if surveillance equipment rotates due to collisions with objects or natural events like typhoons, rotated image fed into the network can degrade the performance of the network model, making it necessary to detect data drift in a timely manner. In this paper, we propose a neural network-based model for detecting data drift due to image rotation. When two images are given to the network as input, the model detects whether there is image rotation drift or not. the experiment result of the model using the UCSD Ped1 and Ped2 datasets achieved significantly high accuracy. © 2025 IEEE.

关键词： Data Drift Detection Deep Learning image rotation estimation

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Deep Learning Strategies for Identifying Machine-Generated Text 19

Deep Learning Strategies for Identifying Machine-Generated T...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yadagiri, Annepaka Pakray, Partha Computer Science & Engineering National Institute of Technology Silchar India

ISBN: (纸本)9798331507817

Generative AIs like LLMs are now accessible to the general public. For example, students can utilize these tools to create essays or complete theses. However, how is a teacher supposed to determine if a text was composed by the student or an AI? Using deep learning techniques, we investigate novel and classic approaches for detecting text created by artificial intelligence. We also study the more complex instance when the AI is asked to write the text in a way that a human would not recognize as AI-generated, as we discovered that categorization is more challenging in this scenario. For our studies, we used llm-detect-ai-generated text from the Kaggle competition dataset, which included texts written by students and texts produced using different LLMs. Our top systems achieve an accuracy of 0.98% and F1 scores of more than 0.98% in classifying simple and complex texts produced by humans and AI-Genereted. the systems combine features such as TF-IDF vectorization, word2Vec, and word embedding properties. Our findings demonstrate that these additional characteristics significantly enhance the performance of several classifiers. Compared to deep learning models, our best-performing model for detecting AI-generated text outperforms even the fine-tuned ROBERTA-Open-AI classifier, achieving an accuracy of 0.98%. this underscores the efficacy of our proposed approach in distinguishing between human and AI-generated content. © 2025 IEEE.

关键词： Adversarial machine learning

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Detecting Smart Home Automation Application Interferences with Domain knowledge 38

Detecting Smart Home Automation Application Interferences wi...

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38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Wang, Tao Chen, Wei Liu, Liwei Wu, Guoquan Wei, Jun Huang, Tao Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China Joint Lab Cyberspace Secur China Southern Power Guangzhou Peoples R China

ISBN: (纸本)9798350329964

Trigger-action programming (TAP) is a widely used development paradigm that simplifies the Internet of things (IoT) automation. However, the exceptional interactions between automation applications may result in interferences, such as conflicts and infinite loops, which cause undesirable consequences and even security and safety risks. While several techniques have been proposed to address this problem, they are often restricted in handling explicit and simple conflicts without considering contextual influences. In addition, they suffer from performance issues when applying to large-scale applications. To address these challenges, we design an effective and practical tool KnowDetector with comprehensive domain knowledge to detect application interferences. To detect application interferences, KnowDetector constructs an automation graph with 1) events, conditions, and actions from automation applications, 2) vertices representing physical environment channels, and 3) edges derived from potential semantic relations between the vertices. In order to make the graph extensively capture the interactions between automation applications, we propose a knowledge model named KnowIoT that accurately characterizes IoT devices with command-level IoT services and the intricate relations between these services and the contextual environment. We abstract the interference detection into a graph pattern-matching problem and summarize ten application interference patterns of four types. Finally, KnowDetector can efficiently detect application interferences by searching for sub-graphs matching the patterns within the automation graph. We evaluated KnowDetector on three real-world datasets. the results demonstrated that it outperformed the other state-of-the-art tools with the highest precision, recall, and F-measure. In addition, KnowDetector is scalable to detect application interferences within a large number of applications with a minimal time overhead.

关键词： smart home platform TAP automation application interference Internet of things

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A Review of knowledge Graph Embedding Methods of TransE, TransH and TransR for Missing Links 8

A Review of Knowledge Graph Embedding Methods of TransE, Tra...

引用

8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Asmara, Salwana Mohamad Sahabudin, Noor Azida Nadiah Ismail, Nor Syahidatul Ahmad Sabri, Ily Amalina Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350310931

knowledge representation and reasoning require knowledge graph embedding as it is crucial in the area. It involves mapping entities and relationships from a knowledge graph into vectors of lower dimensions that are continuous in nature. this encoding enables machine learning algorithms to effectively reason and make predictions on graph-structured data. this review article offers an overview and critical analysis specifically about the methods of knowledge graph embedding which are TransE, TransH, and TransR. the key concepts, methodologies, strengths, and limitations of these methods, along with examining their applications and experiments conducted by existing researchers have been studied. the motivation to conduct this study is to review the well-known and most applied knowledge embedding methods and compare the features of those methods so that a comprehensive resource for researchers and practitioners interested in delving into knowledge graph embedding techniques is delivered. © 2023 IEEE.

关键词： knowledge graph

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Automated Test Case Generation for Prime Path Coverage with Jaya Algorithm 14

Automated Test Case Generation for Prime Path Coverage with ...

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14th international conference on Information and knowledge Technology, IKT 2023

作者： Yadegari, Saba Keyvanpour, Mohammad-Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350349412

Automatic test case generation is critical in software testing because it can significantly reduce testing time and cost while improving the software's overall quality. One of the critical objectives of test case generation is to improve the coverage of the program, with a focus on achieving a high prime path coverage. In this paper, we suggest a self-Adaptive Jaya algorithm for generating test cases that achieve a high coverage of prime paths in software. We conducted a comparative study against a well-established, state-of-the-Art approach utilizing eleven Java benchmark programs to evaluate its performance. Our results show that the suggested method achieves a higher average coverage of prime paths than the existing approach. © 2023 IEEE.

关键词： software testing

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Insights into the Potential of Fuzzy Systems for Medical AI Interpretability 16

Insights into the Potential of Fuzzy Systems for Medical AI ...

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Ouifak, Hafsaa Idri, Ali Faculty of Medical Sciences Mohammed VI Polytechnic University Ben Guerir Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat Morocco

ISBN: (纸本)9789897587160

Machine Learning (ML) solutions have demonstrated significant improvements across various domains. However, the complete integration of ML solutions into critical fields such as medicine is facing one main challenge: interpretability. this study conducts a systematic mapping to investigate primary research focused on the application of fuzzy logic (FL) in enhancing the interpretability of ML black-box models in medical contexts. the mapping covers the period from 1994 to January 2024, resulting in 67 relevant publications from multiple digital libraries. the findings indicate that 60% of selected studies proposed new FL-based interpretability techniques, while 40% of them evaluated existing techniques. Breast cancer emerged as the most frequently studied disease using FL interpretability methods. Additionally, TSK neuro-fuzzy systems were identified as the most employed systems for enhancing interpretability. Future research should aim to address existing limitations, including the challenge of maintaining interpretability in ensemble methods. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Adversarial machine learning

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software Vulnerability Prediction knowledge Transferring Between Programming Languages 18

Software Vulnerability Prediction Knowledge Transferring Bet...

引用

18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Hanifi, Khadija Fouladi, Ramin F. Unsalver, Basak Gencer Karadag, Goksu Ericsson Secur Res Istanbul Turkiye Vodafone Istanbul Turkiye

ISBN: (纸本)9789897586477

Developing automated and smart software vulnerability detection models has been receiving great attention from both research and development communities. One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, we address this issue by proposing a transfer learning technique to leverage available datasets and generate a model to detect common vulnerabilities in different programming languages. We use C source code samples to train a Convolutional Neural Network (CNN) model, then, we use Java source code samples to adopt and evaluate the learned model. We use code samples from two benchmark datasets: NIST software Assurance Reference Dataset (SARD) and Draper VDISC dataset. the results show that proposed model detects vulnerabilities in both C and Java codes with average recall of 72%. Additionally, we employ explainable AI to investigate how much each feature contributes to the knowledge transfer mechanisms between C and Java in the proposed model.

关键词： software Security Vulnerability Prediction Source Code Machine Learning Transfer Learning

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases 38

Domain Adaptive Code Completion via Language Models and Deco...

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38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Tang, Ze Ge, Jidong Liu, Shangqing Zhu, Tingwei Xu, Tongtong Huang, Liguo Luo, Bin Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanyang Technol Univ Singapore Singapore Huawei Software Engn Applicat Technol Shenzhen Peoples R China Southern Methodist Univ Dept Comp Sci Dallas TX USA

ISBN: (纸本)9798350329964

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. they are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose kNM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. the extensive experiments on the completion of intra-project and intra-scenario have confirmed that kNM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that kNM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

关键词： domain adaptive code completion retrieval-augment language model

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EpiMDE: A Model-Driven engineering Platform for Epidemiological Modeling 24

EpiMDE: A Model-Driven Engineering Platform for Epidemiologi...

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27th international conference on Model Driven engineering Languages and Systems (MODELS)

作者： Curzi-Laliberte, Bruno Fokaefs, Marios Famelis, Michalis Hamdaqa, Mohammad Polytech Montreal Montreal PQ Canada York Univ Toronto ON Canada Univ Montreal Montreal PQ Canada

ISBN: (纸本)9798400705045

Modeling is a critical step in studying epidemics. It allows us to better understand and predict the progression of a disease, design interventions such as vaccination, and assess their impact. Current epidemics are modeled using compartmental and mathematical models. While these are enough to achieve the primary goal of modeling, they suffer from shortcomings with respect to communicating and sharing the models, comparison and validation, and reproducibility. In this work, we propose the use of model-driven software engineering principles, to better represent disease models and facilitate the model management operations. We present an extensible metamodel for epidemics and an integrated development environment to allow epidemiologists to create and manage their models and simulations. We present the use of our platform on a COVID-19 model, where we show that the resulting model is more concise yet structurally and functionally equivalent to the original.

关键词： epidemiological modeling integrated development environment reproducibility extensibility metamodeling

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