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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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Joint QoS Prediction for Web Services based on Deep Fusion of Features

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IEEE Transactions on Network and Service Management 2023年 1-1页

作者： Ding, Linghang Liu, Jianxun Kang, Guosheng Xiao, Yong Cao, Buqing Hunan Provincial Key Lab. for Services Computing and Novel Software Technology and School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

In order to solve the problem of insufficient accuracy of Web service QoS prediction, a joint QoS prediction method for Web services based on the deep fusion of features was proposed by considering the hidden environmental preference information in QoS and the common characteristics of multi-class QoS. QoS data was modeled as user-service bipartite graph at first, then, multi-component graph convolution neural network was used for feature extraction and mapping, and weighted fusion method was used for the same dimensional mapping of multi-class of QoS features. Subsequently, the attention factor decomposition machine was used to extract the first-order features, second-order interactive features and high-order interactive features of the mapped feature vector. Finally, the results of each part were combined to achieve the joint QoS prediction. The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE). IEEE

关键词： Feature extraction

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Research on Face Anti-Spoofing Algorithm Based on Image Fusion

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Computers, Materials & Continua 2021年第9期68卷 3861-3876页

作者： Pingping Yu Jiayu Wang Ning Cao Heiner Dintera School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China German-Russian Institute of Advanced Technologies Karan420126Russia

Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent ***,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D *** order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light *** normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal ***,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing *** order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is ***,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency ***,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency ***,the DTCWT inverse transform is used to obtain the fused image containing the facial texture *** recognition is carried out on the fused image to realize identity *** results show that this algorithm can effectively resist attacks from photos,videos or *** with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.

关键词： Anti-spoofing infrared thermal images image fusion heart rate detection

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An Intelligent Remote Consultation System for Oral Prevention in Children: A Case Study in Mongolia 15

An Intelligent Remote Consultation System for Oral Preventio...

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15th International Conference on software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Ren, Lijuan Bendjeddou, Randa Cheima Kahn, Giacomo Seklouli, Aicha Sekhari Myagmar, Bat-Erdene Garidkhuu, Ariuntuul School of Software Engineering Chengdu University of Information Technology Chengdu China Lyon France School of Dentistry Mongolian National University of Medical Sciences Department of Dental Technology and Dental Hygiene Ulaanbaatar Mongolia School of Dentistry Mongolian National University of Medical Sciences Department of Prosthodontics Ulaanbaatar Mongolia

ISBN: (纸本)9798350316551

Children's dental caries is a common oral health issue, causing pain and discomfort. Therefore, oral health plays a crucial role in children's growth and development. However, regular dental check-ups and consultations are burdensome and expensive for parents, especially for Mongolian children with uneven access to healthcare. To address this issue, we developed an intelligent remote consultation system for children's dental prevention, which utilizes advanced technologies like risk assessment and image recognition for automated oral risk analysis and cavity localization. Subsequently, we conducted experiments and analyses based on data collected from the initial phase in Mongolia, showing the potential of the system to improve children's oral health. However, the system's precision is restricted by the small amount of data. Expanding data collection will be the focus of future efforts to improve system performance and further improve this creative solution. © 2023 IEEE.

关键词： Risk assessment

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Crafting Tomorrow's Trends with Automated Precision, Efficiency, and Faultless Excellence in Apparel Industry for Medium Scale Businesses

Crafting Tomorrow's Trends with Automated Precision, Efficie...

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Advancements in Computing (ICAC), International Conference on

作者： H M Hiruni Herath J M M G Jayaweera J M Y Jayasinghe K M T D Randeni Nelum Chathuranga Amarasena Samadhi Rathnayake S.N Chandanathilaka Department of Information Technology Sri Lankan Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lankan Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. This research explores the complexities of garment production, addressing critical challenges such as machine breakdowns, maintenance, and workforce management. By employing advanced technologies such as image recognition, loT devices, and machine learning, this study aims to transform pre-production decision-making, optimize sewing machine performance, implement predictive maintenance strategies, and refine workforce optimization. These initiatives, driven by domain expertise and cutting-edge methodologies, not only aim to bridge existing gaps in the industry but also pave the way for significant advancements, ensuring a future characterized by precision, efficiency, and operational excellence.

关键词： Industries Performance evaluation Transforms Production Manuals Machine learning Market research Maintenance Optimization Predictive maintenance

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Multi-modal Representation Learning for Social Post Location Inference

arXiv

引用

arXiv 2023年

作者： Dai, RuiTing Luo, Jiayi Luo, Xucheng Mo, Lisi Ma, Wanlun Zhou, Fan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Swinburne University of Technology Australia

Inferring geographic locations via social posts is essential for many practical location-based applications such as product marketing, point-of-interest recommendation, and infector tracking for COVID-19. Unlike image-based location retrieval or social-post text embedding-based location inference, the combined effect of multi-modal information (i.e., post images, text, and hashtags) for social post positioning receives less attention. In this work, we collect real datasets of social posts with images, texts, and hashtags from Instagram and propose a novel Multi-modal Representation Learning Framework (MRLF) capable of fusing different modalities of social posts for location inference. MRLF integrates a multi-head attention mechanism to enhance location-salient information extraction while significantly improving location inference compared with single domain-based methods. To overcome the noisy user-generated textual content, we introduce a novel attention-based character-aware module that considers the relative dependencies between characters of social post texts and hashtags for flexible multimodel information fusion. The experimental results show that MRLF can make accurate location predictions and open a new door to understanding the multi-modal data of social posts for online inference tasks. © 2023, CC BY-NC-ND.

关键词： Location

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Temporal-Assisted Dynamic Beampattern Optimization in Integrated Sensing and Communication Systems

arXiv

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arXiv 2024年

作者： Zhou, Shengcai Xiang, Luping Yang, Kun University of Electronic Science and Technology of China Quzhou324003 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China

In this paper, an integrated sensing and communication (ISAC) system is investigated. Initially, we introduce a design criterion wherein sensing data acquired from the preceding time slot is employed for instantaneous optimal beamforming in the succeeding time slot, aiming to enhance the communication rate. Subsequently, the development of optimal beamforming is addressed, and a high-caliber suboptimal resolution is derived utilizing successive convex approximation (SCA) techniques combined with the iterative rank minimization (IRM) methodology. Our evaluations, grounded on numerical analyses, reveal that the communication rate of the introduced beamforming strategy surpasses that of conventional omnidirectional sensing and pilot based approaches. Copyright © 2024, The Authors. All rights reserved.

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An Ensemble Model for software Defect Prediction 2

An Ensemble Model for Software Defect Prediction

引用

2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Ali, Amad Rizwan Ur Rehman, Attique Nawaz, Ali Ali, Tahir Muhammad Abbas, Muhammad UserMaven Inc United States University of Sialkot Department of Software Engineering Sialkot Pakistan College of Electrical and Mechanical Engineering National University of Science and Technology Rawapindi Pakistan Gulf University for Science and Technology Department of Computer Science College of Science and Arts Kuwait

ISBN: (纸本)9781665498197

software testing is one of the important ways to ensure the quality of software. It is found that testing cost more than 50% of overall project cost. Effective and efficient software testing utilizes the minimum resources of software. Therefore, it is important to construct the procedure which is not only able to perform the efficient testing but also minimizes the utilization of project resources. The goal of software testing is to find maximum defects in the software system. As world is continuously moving toward data driven approach for making important decision. Therefore, in this research paper we performed the machine learning analysis on the publicly available datasets and tried to achieve the maximum accuracy. The major focus of the paper is to apply different machine learning techniques on the datasets and find out which technique produce efficient result. Particularly, we proposed an ensemble learning models and perform comparative analysis among KNN, Decision tree, SVM and Naïve Bayes on different datasets and it is demonstrated that performance of Ensemble method is more than other methods in term of accuracy, precision, recall and F1-score. The classification accuracy of ensemble model trained on CM1 is 98.56%, classification accuracy of ensemble model trained on KM2 is 98.18% similarly, the classification accuracy of ensemble learning model trained on PC1 is 99.27%. This reveals that ensemble learning is more efficient method for making the defect prediction as compared other techniques. © 2022 IEEE.

关键词： software testing

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Towards Automated Support for the Co-Evolution of Meta-Models and Grammars

arXiv

引用

arXiv 2023年

作者： Zhang, Weixing Department of Computer Science & Engineering Division of Interaction Design & Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

Blended modeling is an emerging paradigm involving seamless interaction between multiple notations for the same underlying modeling language. We focus on a model-driven engineering (MDE) approach based on meta-models to develop textual languages to improve the blended modeling capabilities of modeling tools. In this thesis, we propose an approach that can support the co-evolution of meta-models and grammars as language engineers develop textual languages in a meta-model-based MDE setting. Firstly, we comprehensively report on the challenges and limitations of modeling tools that support blended modeling, as well as opportunities to improve them. Second, we demonstrate how language engineers can extend Xtext’s generator capabilities according to their needs. Third, we propose a semi-automatic method to transform a language with a generated grammar into a Python-style language. Finally, we provide a solution (i.e., GrammarOptimizer) that can support rapid prototyping of languages in different styles and the co-evolution of meta-models and grammars of evolving languages. © 2023, CC BY-NC-ND.

关键词： Modeling languages

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A Fine-Grained Emotion Prediction Method Based on Large Language Models

A Fine-Grained Emotion Prediction Method Based on Large Lang...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Fang Li Yubin Qu Chao Feng Xianzhen Dou Long Li Jiangsu College of Engineering and Technology Nantong Jiangsu China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi China Army Engineering University of PLA Nanjing Jiangsu China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Every day, much multimodal data emerges on social media. Previous studies have focused on the problem of fine-grained sentiment analysis in multimodal data. However, many multimodal data must be annotated for specific emotions to train fine-grained emotion prediction models. To efficiently train fine-grained emotion risk prediction models for multimodal data, we propose a few-shot learning-based method for multimodal fine-grained sentiment analysis. This model captures sentiment polarity in text using a pre-trained large language model, annotates a small amount of data, and generates a training dataset. Then, utilizing an intermediate layer fusion strategy, it combines representation vectors of text, images, videos, and audio to ultimately create a fine-grained emotion prediction model based on multimodal data. Exper-imental work on a real multimodal dataset, compared with random fine-grained sentiment analysis methods, shows that our proposed method has significant advantages in handling multimodal fine-grained sentiment analysis tasks and practical value.

关键词： Training Sentiment analysis Social networking (online) Large language models software quality Predictive models Data models Vectors software reliability Videos

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