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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

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Differential Emulatable PUF Design against machine learning Attacks for Hardware Security

Differential Emulatable PUF Design against Machine Learning ...

引用

2023 international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Duan, Shengyu Cao, Chenhong Shanghai University School of Computer Engineering and Science Shanghai China

ISBN: (纸本)9798350322972

Physical unclonable functions (PUFs) are emerged to provide lightweight hardware security primitives, by generating device-specific responses as physical fingerprints. Emulatable PUFs use emulatable models to generate secrete reference information, eliminating the need of databases to store such information. Emulatable PUFs further improves the security by providing execution-simulation time gap. Meanwhile, machine learning (ML) attacks have become one of the greatest concerns to produce software counterfeits for PUFs. This paper investigates a typical emulatable PUF, differential emulatable PUF (DEPUF). We demonstrate a multilayer perceptron-based model, to show DEPUF is potentially susceptible to ML attacks. We investigate how structural configurations of DEPUFs can affect their resilience against ML attacks. The results indicate the resilience can be most significantly improved by increasing the width of a DEPUF. © 2023 IEEE.

关键词： Hardware security

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Lightwight Image Super-Resolution: The Shift Ladder Transformer Approach 2

Lightwight Image Super-Resolution: The Shift Ladder Transfor...

引用

2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Zhang, Jian Wu, Yujie Ma, Jingbin Ling, Siyao Li, Zhaoyan School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350331417

In lightweight Super Resolution (SR) tasks, balancing performance and efficiency is a challenge for CNN-based models. The Transformer architecture has been introduced into super-resolution tasks due to its global self-attention mechanism. However, existing lightweight image super-resolution networks based on Transformers typically employ localized attention to sparsify their attention, thereby limiting their receptive field and uniformity of pixel weight distribution. To address these challenges, this paper introduces the Shift Ladder Transformer for Super Resolution (slTSR), a lightweight model that melds lightweight convolutional layers with a distinct residual Shift Ladder Transformer block. With the integration of the Shift Ladder Transformer Layer (SLTL) strategy, slTSR effectively amalgamates information from diverse spatial windows, enhancing information exchange. Experiments on diverse datasets consistently highlight the exceptional performance and efficiency of slTSR. © 2023 IEEE.

关键词： Lightweight image super-resolution Localized attention Shift Ladder Transformer

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Connecting Cloud Computing and machine learning through Functional Situation-awareness: A User-centric Smart Monitoring Application

Connecting Cloud Computing and Machine Learning through Func...

引用

1st IEEE international conference on software Services engineering (IEEE SSE)

作者： Millan, Miguel Liu, Ruochen Ming, Hua Oakland Univ Dept Comp Sci & Engn Rochester MI 48307 USA

ISBN: (纸本)9798350340754

For many parents, the time between their children leaving home and the ensuing arrival at school is a grey area with uncertainty. The need of being aware of the highly critical safetyness situations there- fore arises. Situation-aware computing serves as a promising technique, both theoretically and pragmatically, to bring smart monitoring to the field. It seamlessly integrates the benefits offered by machine learning techniques and cloud computing technologies with an emphasis on user centric situations. In this paper we present a real world smart monitoring application: its underlying model, its human computer interactive design and design principles, its support from the theory and practice of situation-awareness computing, as well as its implementation details and viability test. This application also serves the purpose of elaborating important attributes of safety-aware situation computing, with a strong linkage to safety critical human computer interaction. The goal of this application is to accurately recognize and identify the faces of children, under relevant environmental contexts, when they enter or exit a bus, and then promptly alert their parents. Two main components, one being a distributed bus subsystem and the other being a central cloud-based subsystem, are built into this application highlighting lightweight to effectively serve the users. The novelty of our approach lies in the fact that we deeply embrace functional programming paradigm to tackle the specification of the two subsystems at the design level with rigorous computational semantics, which in turn contributes to the quality implementation of the lower level details.

关键词： Situation Situation-awareness Human Computer Interaction Intelligent Monitoring machine learning Cloud Computing

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A Collaborative Approach to Detect DDoS Attacks in OpenStack-based Cloud using Entropy and machine learning 14

A Collaborative Approach to Detect DDoS Attacks in OpenStack...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Patil, Ritesh Kandakur, Gururaj Vardhamane, Rishab Kotyal, Shivagond Narayan, D.G. Kachavimath, Amit Kle Technological University School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350335095

Cloud computing involves optimizing the efficiency of data storage and processing through the use of networking, various software, and internet services. In this context, network security is of utmost importance to prevent cybercriminals from disrupting resources and impeding effective utilization. Among the primary security concerns in cloud computing are Distributed Denial of Service (DDoS) attacks, which can have severe consequences for business continuity. To address this, it is crucial to develop a robust DDoS detection system that can identify suspicious activities within the cloud environment. In this study, we propose a collaborative approach that combines entropy and machine learning techniques to detect DDoS attacks specifically in an OpenStack-based cloud environment. Initially, we employ an entropy-based technique to identify vulnerable hosts, followed by a machine learning-based technique to identify attacker hosts. To train and evaluate our detection system, we utilize real-time cloud data obtained from an OpenStack-based cloud testbed. Results reveal that using logistic regression improves the accuracy of the classifier. Furthermore, the collaborative approach significantly reduces the detection time. © 2023 IEEE.

关键词： Entropy

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Data Generalization Processing and Fusion machine Translation System Based on Virtual Reality Technology 2

Data Generalization Processing and Fusion Machine Translatio...

引用

2nd international conference on Electronic Information Technology, EIT 2023

作者： Du, Xiaowen College of Foreign Languages Xinjiang University Xinjiang Urumqi830046 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

In the development of digital technology, machine language is one of the important branches. We need to establish a set of perfect, efficient and applicable data generalization processing and fusion methods applicable to practical application scenarios to improve the efficiency of model information transmission. The data processing part in this system mainly includes the model analysis, modeling and simulation. In machine translation, the model should first be transformed into a generalization ability feature function with certain significance. In order to make English translation more accurate and interesting, we can use virtual reality technology to design English translation learning methods and translation software. This paper mainly uses the methods of quantitative analysis and data comparison to make statistics and comparison on the response and transmission quantity of the system in different applications, and obtain the relevant performance of the system. The experimental data shows that the number of data transmission based on RDF is always more than 5 than that of SWIFT. © 2023 SPIE.

关键词： learning systems

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Deep Reinforcement Control Scheme for Transient and Steady-State Stability in Power Systems

Deep Reinforcement Control Scheme for Transient and Steady-S...

引用

2023 IEEE international conference on Energy Technologies for Future Grids, ETFG 2023

作者： Dewantoro, Gunawan Swain, Akshya Hafiz, Faizal Patel, Nitish The University of Auckland Department of Electrical Computer & Software Engineering Auckland New Zealand Université Côte d'Azur Skema Business School Sophia Antipolis France

ISBN: (纸本)9781665471640

The transient and the steady-state stability has long been a complex issue in interconnected power system operation due to intermittent time response of various components. The present study, therefore, proposes a model-free deep reinforcement learning controller to ensure both types of stability of Single machine Infinite Bus (SMIB) system. Initially, a Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are employed to develop a learning controller to enhance stability of SMIB. Next, the prescribed control objectives of the system are guaranteed by maximizing the long-term reward. The output of the controller is used to adjust the value of a series capacitive compensator in response to given disturbances. The effectiveness of the proposed controller is demonstrated considering the presence of a bolted fault and small signal disturbances. A comparative investigation with a Bang-Bang controller (BBC) shows that the proposed deep reinforcement learning controller outperforms BBC in improving the stability of the SMIB when subjected to disturbances. © 2023 IEEE.

关键词： deep reinforcement learning power systems single machine infinite bus steady-state stability transient stability

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'Sign Language Talk' Using machine learning 7

'Sign Language Talk' Using Machine Learning

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mahajan, Pushkar Rajani, P.K. Mali, Vaishnavi Khope, Darshan Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350304268

People with special needs frequently experience hearing loss, which impacts many parts of their lives and prevents them from interacting socially and communicating with others efficiently. The community of people who are speech-impaired use sign languages as their native tongues and have complete access to communication. Hand gestures are used in sign language to convey the intended meaning of people with specific communication needs to the outside world. This project proposes 'Sign Language Talk,' a machine learning-based sign language-to-speech system that will assist people in improving their communication skills. It helps in the conversion of real-time sign language image input to text and speech which allows communication between speech-impaired people and normal people. The speaker output helps to understand this sign language to visually impaired people too. The project is implemented on Convolutional Neural Network (CNN) using Python tools, which takes input through the camera and compares it with a pre-loaded Indian Sign Language (ISL) dataset and generates text output on the screen as well as speech output through speakers. The parameters such as accuracy, precision, and recall were taken into consideration for evaluating the project. During the training of the model, Accuracy was obtained to be 100%. The Accuracy outcome with the project in runtime was observed to be 87%. The software can also be used for educational purposes in practicing sign language in schools of speech-impaired students. © 2023 IEEE.

关键词： Convolutional Neural Network Indian Sign language machine learning Sign language Talk Speech impaired

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Automatic extraction of sequence diagram semantic information 5

Automatic extraction of sequence diagram semantic informatio...

引用

5th international conference on machine learning, Big Data and Business Intelligence, MLBDBI 2023

作者： Wang, Kai Liu, Wei Mu, Yongan Gao, Sheng Wuhan Institute of Technology Department of Computer Science and Engineering Dcse Wuhan China

ISBN: (纸本)9798350359930

In the realm of software reuse, particular attention has been devoted to the reuse of software design. The UML (Unified Modeling Language) sequence diagram, serving as a dynamic representation, adeptly reflects the evolution and progression of a system. During the software verification phase, it proves instrumental in examining the correctness and consistency of the system. Consequently, the automatic identification and extraction of semantic elements within UML sequence diagrams have become imperative. To address this issue, a Sequence Diagram Extraction (SDE) method based on object detection and text extraction is proposed. This method employs grayscale processing coupled with morphological operations to preprocess distorted images, thereby reducing noise from the original images. The You Only Look Once-version 5 (YOLOv5) algorithm, a deep learning model, is applied to detect each role, object, control focus, and message on the image. Precise localization, segmentation, and cropping of each region are performed. The results are input into the EasyOCR(A text recognition model that has been trained many times) algorithm to execute accurate optical character recognition for every extracted block region. Finally, the detection outcomes undergo semantic merging to obtain comprehensive semantic information for the entire sequence diagram. The effectiveness of the proposed approach is validated through the application of the method to fifty different sequence diagrams, affirming its viability. © 2023 IEEE.

关键词： software design

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A Weakly-Supervised Glass Bottle Defect Detection System Based on Multi-View Analysis 20

A Weakly-Supervised Glass Bottle Defect Detection System Bas...

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20th international Joint conference on Computer Science and software engineering, JCSSE 2023

作者： Phalawan, Lutfee Phisanbut, Nalina Piamsa-Nga, Punpiti Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350300505

Defect inspection is an essential process in glass bottle production. machine vision has shown great potential as an alternative to human inspection. In this paper, we present two open challenges in developing effective machine vision system, i.e., the constraint on an availability of training datasets with all relevant ground truth labels due to high annotation cost;and the difficulty in detecting defects in the texture region which may only be observable at certain angles. A novel weakly-supervised glass bottle defect detection system is proposed. The model features a two-stage learning process to recover missing labels, and train classifiers. A n IoT-based image acquisition system was designed to provide multi-view images of the bottle using a single camera. The proposed defect detection system is evaluated on glass bottle images acquired by our designed apparatus. The experimental results validate the effectiveness of the proposed system on a dataset with only single-positive labels with up to 96% accuracy. © 2023 IEEE.

关键词： Glass

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Fake News Detection Using machine learning Techniques 21

Fake News Detection Using Machine Learning Techniques

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Sultana, Achhiya Islam, Mahmudul Hasan, Mahady Ahmed, Farruk Independent University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345889

A lot of information is spread by people in the social media to update their status and share crucial news with others. But the majority of these platforms don't promptly validate the individuals or their posts and people aren't able to identify the fake news manually. Therefore, there is a need for an automated system capable of detecting fake news. This research has proposed to build a model using four machine learning algorithms. The dataset employed in the experiment is a composite of two datasets containing almost equal amounts of true and fake news articles on politics. The preprocessing stages begin with cleaning the data by removing punctuation, tokenization, special characters, white spaces, redundant word elimination, numerals, and English letters followed by stemming and stop with data discretization. Then, we analyzed the collected data and 80% of the data has been used to train each model initially. After that, the four manifested classification algorithms are applied. For identifying fake news from news articles, meth-ods like Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Classifier were used. The trained classifiers' accuracy has been evaluated using the remaining 20% of the data. The results show that the decision tree model produces the best accuracy of 99.60% and gradient boosting of 99.55%. Besides, the random forest shows 99.10% along with the logistic regression 98.99%. Moreover, we have explored the best model to achieve the highest precision, recall, F1-score based on the confusion matrix's outcome. © 2023 IEEE.

关键词： Decision trees

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