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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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A Privacy Leakage Detection Method for Personalized Course Recommendation Based on Pi-Calculus 24

A Privacy Leakage Detection Method for Personalized Course R...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wu, Zixin Zhou, Xiaoyun Zhu, Yi Wang, Chenyu Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Jiangsu Engineering Technology Research Center of Ict in Education Jiangsu Xuzhou China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

Privacy protection is one of the main approaches to improve the security and reliability of personalized course recommendation systems. However, the current privacy protection methods for personalized course recommendation mainly focus on data protection, but ignore the potential privacy leakage that may be caused by the system behavior. In addition, privacy protection is not fully considered in the initial phases of system development and design. To address this issue, this paper presents a methodology for detecting privacy leakage in personalized course recommendation systems using the process algebra Pi-Calculus. Firstly, the basic semantics and evolution rules of Pi-Calculuss as well as the definition of mutual simulation techniques are introduced. Then the personalized course recommendation system is mapped to a process algebra Pi-Calculus, a formal description and analysis of the interaction behavior of the components of the personalized course recommendation system is given, and finally a framework for privacy leakage detection is introduced and applied to a case study to detect whether there is privacy leakage behavior in the recommendation system by the inter-simulation technique. Experiments demonstrate that the method can effectively detect potential privacy leakage problems in the early stages of system development. © 2024 IEEE.

关键词： Differential privacy

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Deep Learning-Based Operators for Evolutionary Algorithms

arXiv

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arXiv 2024年

作者： Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of deep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERT mutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual's fitness. We show the efficacy of both operators through experimentation. © 2024, CC BY.

关键词： Genetic programming

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On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sensor based Classification of Table-Tennis Strokes 1

On Understanding Sports-HAR: Hierarchical, Mobile, Multi-Sen...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Ezzeldin, Mustafa Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Helwan University Faculty of Computers AI Software Engineering Cairo Egypt Helwan University Faculty of Computers and AI Computer Science Cairo Egypt Helwan University Faculty of Computers and AI Information Systems Cairo Egypt Helwan Univeristy HCI-LAB Faculty of Computers and AI Computer Science Cairo Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Table Tennis is a renowned competitive and recreational sport. An Olympic sport since 1988, table tennis in-cludes several movements, shots (i.e., strokes), and positions. Consequently, many factors can affect the strokes' accuracy and strength. This paper aims twofold: to classify the type of shot and to enhance the performance of the classification by improving the recognition accuracy and reducing the computational time. To track the movement of the body parts and extract table tennis stroke information, sensors (both Accelerometers and Gyroscopes) were utilized. The extracted data are processed and passed to a P-Dollar classifier to recognize six different table tennis strokes. This work utilizes two classification approaches to evaluate the performance, both linear and hierarchical. The presented results demonstrate that the hierarchical classification yields a better accuracy of 88%, achieved in 4 seconds, while traditional linear classification achieved 83% within 6 seconds. © 2023 IEEE.

关键词： Gyroscopes

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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Image segmentation with spatiotemporal relationship-guided graph convolution network

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Computing 2025年第7期107卷 1-26页

作者： You, Hongfeng Yu, Long Lin, Jianchu Chen, Xiaobing Wang, Wenhao Yu, Kun Wang, Jiacheng Huaiyin Institute of Technology Huaian China Key Laboratory of Software Engineering Technology Xinjiang University Urumuqi China Jiangsu Key Lab of Image and Video Understanding for Social Security and Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

The size of the receptive field limits traditional deep convolutional networks and needs help to fully capture the detailed semantic information of targets in complex remote sensing images. At the same time, the attributes of boundary pixels are ambiguous due to many external factors, such as illumination and imaging technology. To solve these problems, we propose a spatiotemporal relationship-guided graph convolutional network for image segmentation. We aim to explore the global and contextual semantics of targets in images and judge the attribute information of boundary pixels. On the one hand, a particular contextual attention module is embedded in the feature extractor to improve the feature extractor’s global and local detail representation. On the other hand, the graph convolution layer, enhanced by the recursive network, aggregates the high-level and low-level features of nodes to strengthen the interactivity between semantic information at different levels. At the same time, long-term dependencies are established between boundary pixels and target center pixels to constrain boundary pixels and improve their attribute decision-making ability. It is worth noting that we designed a weighted loss function to supervise, adjust, and optimize different modules separately. Finally, experimental results on the indian driving dataset, uestc all-day scenery datasets and deepglobe land cover classification challenge datasets show that the proposed framework is robust and has good segmentation performance, with F1 of 89.03%, 98.38% and 85.46%, respectively.

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Emotional Synchronization for Audio-Driven Talking-Head Generation

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Yan Luo Zhichao Zuo Richang Hong Yi Yang Meng Wang School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable.

关键词： Training Codes Accuracy Digital humans Synchronization Data mining Faces Testing

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A TMA-Seq2seq Network for Multi-Factor Time Series Sea Surface Temperature Prediction

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computers, Materials & Continua 2022年第10期73卷 51-67页

作者： Qi He Wenlong Li Zengzhou Hao Guohua Liu Dongmei Huang Wei Song Huifang Xu Fayez Alqahtani Jeong-Uk Kim Department of Information Technology Shanghai Ocean UniversityShanghai201306China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural ResourcesHangzhou310012China College of Computer Dong Hua UniversityShanghai200051China College of Information Technology Shanghai Jian Qiao UniversityShanghai201306China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Department of Electrical Engineering Sangmyung UniversitySouth KoreaKorea

Sea surface temperature (SST) is closely related to global climatechange, ocean ecosystem, and ocean disaster. Accurate prediction of SST isan urgent and challenging task. With a vast amount of ocean monitoring dataare continually collected, data-driven methods for SST time-series predictionshow promising results. However, they are limited by neglecting complexinteractions between SST and other ocean environmental factors, such as airtemperature and wind speed. This paper uses multi-factor time series SSTdata to propose a sequence-to-sequence network with two-module attention(TMA-Seq2seq) for long-term time series SST prediction. Specifically, TMASeq2seq is an LSTM-based encoder-decoder architecture facilitated by factorand temporal-attention modules and the input of multi-factor time series. Ittakes six-factor time series as the input, namely air temperature, air pressure,wind speed, wind direction, SST, and SST anomaly (SSTA). A factor attentionmodule is first designed to adaptively learn the effect of different factors onSST, followed by an encoder to extract factor-attention weighted features asfeature representations. And then, a temporal attention module is designedto adaptively select the hidden states of the encoder across all time steps tolearn more robust temporal relationships. The decoder follows the temporalattention module to decode the feature vector concatenated from the weightedfeatures and original input feature. Finally, we use a fully-connect layer tomap the feature into prediction results. With the two attention modules, ourmodel effectively improves the prediction accuracy of SST since it can notonly extract relevant factor features but also boost the long-term *** experiments on the datasets of China Coastal Sites (CCS) demonstrate that our proposed model outperforms other methods, reaching 98.29%in prediction accuracy (PACC) and 0.34 in root mean square error (RMSE).Moreover, SST prediction experiments in China’s East, South,

关键词： Sea surface temperature multi-factor attention long short-term memory

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Asymptotic behavior of least squares estimators for nonlinear autoregressive models

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Science China(information Sciences) 2023年第6期66卷 299-300页

作者： Zhaobo LIU Chanying LI College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

Dear editor,Estimations of nonlinear autoregressive(AR) models in the literature typically involve ergodic series. Based on this assumption,the asymptotic theory has been established accordingly(see [1–3]). However,t...

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