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A Comparison of Model Confidence Metrics on Visual Manufacturing Quality Data 1

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international conference on Computer Vision and machine Intelligence, CVMI 2022

作者： Mascha, Philipp University of Augsburg Universitätsstraße 2 Augsburg86159 Germany

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

After ground-breaking achievements through the application of modern deep learning, there is a considerable push towards using machine learning systems for visual inspection tasks part of most industrial manufacturing processes. But whilst there exist a lot of successful proof-of-concept implementations, productive use proves problematic. Whilst missing interpretability is one concern, the constant presence of data drift is another. Changes in pre-materials or process and degradation of sensors or product redesigns impose constant change towards statically trained machine learning models. To handle these kind of changes, a measurement of system confidence is needed. Since pure model output probabilities often lack in this concern better solutions are required. In this work, we compare and contrast several pre-existing methods used to describe model confidence. In contrast to previous works, they are evaluated on a large set of real-world manufacturing data. It is shown that utilizing an approach based on auto-encoder reconstruction error proves to be most promising in all scenarios tested. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Task-oriented Approach to Guide Visually Impaired People During Smart Device Usage

Task-oriented Approach to Guide Visually Impaired People Dur...

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IEEE international conference on Big Data and Smart computing (BigComp)

作者： Akkapusit, Peerapon Ko, In-Young Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

ISBN: (纸本)9781728189246

Visually impaired persons often experience limited user accessibility regarding smart devices. Particularly, it is challenging for such persons to precisely touch a button or icon to operate smart devices. Existing studies have reported a lack of step-by-step guidance for visually impaired users to perform tasks on smart devices. Although prototypes and non-visual interface systems were implemented to aid blind people in using mobile phones, a step-by-step guidance system was not developed. Moreover, devices other than smartphones were not emphasized. To address these limitations, we propose a novel guidance system that employs an object detection method for recognizing the control panels of smart devices and offers appropriate guidelines based on the user's targeted task. A user study conducted with 20 participants indicated that users were more satisfied with our guidance system than with existing voice assistants.

关键词： visually impaired users task-oriented guidelines object detection machine learning smart devices

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Analysis of Boosting Mechanisms in Cloud-based Intrusion Detection Systems

Analysis of Boosting Mechanisms in Cloud-based Intrusion Det...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Mohd Zuhair Pronaya Bhattacharya Vivek Kumar Prasad Manav Barot Monil Modi CSE Department Institute of Technology Nirma University Ahmedabad India Department of CSE Amity School of Engineering and Technology Amity University Kolkata India

In modern cloud ecosystems, cloud users request service sets for software, platform, and infrastructures. Due to open channels (Internet), cloud networks become soft targets for malicious intruders. Recent literature suggests the use of Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) to prevent the exploitation of cloud servers, which eats up useful resources (software, platform, and infrastructures). Thus, secured IDS are a critical necessity of modern-day cloud servers against zero-day attacks. To improve the learning and error rate, boosting mechanisms are used with machine learning (ML) algorithms, which improves the predictive analysis. In the boosting process, we sequentially combine different weak classifiers to improve the performance and reduce the bias of ML algorithms. The paper explores the different boosting algorithms to support attack classification in modern IDS datasets through a comparative analysis in terms of precision, recall, and F1 score. A case study of Binary Modest AdaBoost (BMA) is presented, and the comparative analysis of error rates and running time of BMA is presented for five benchmarking IDS (KDD Cup’ 99, UNSW-B15, TRAbID, NSL-KDD, and the CICIDS2017) datasets.

关键词： Training Cloud computing machine learning algorithms Error analysis Intrusion detection Boosting Prediction algorithms

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Privacy Threats and Countermeasures in Federated learning for Internet of Things: A Systematic Review

Privacy Threats and Countermeasures in Federated Learning fo...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Adel ElZemity Budi Arief School of Computing University of Kent Canterbury United Kingdom

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Federated learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, ACM, and Science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures – specifically within the context of IoT – using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions.

关键词： Privacy Differential privacy Social computing Computational modeling Multi-party computation Robustness Blockchains Internet of Things Time factors Security

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TEA-Fed: Time-Efficient Asynchronous Federated learning for Edge computing 21

TEA-Fed: Time-Efficient Asynchronous Federated Learning for ...

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18th ACM international conference on computing Frontiers (CF)

作者： Zhou, Chendi Tian, Hao Zhang, Hong Zhang, Jin Dong, Mianxiong Jia, Juncheng Soochow Univ Suzhou Jiangsu Peoples R China Southern Univ Sci & Technol Shenzhen Guangdong Peoples R China Muroran Inst Technol Muroran Hokkaido Japan

ISBN: (纸本)9781450384049

Federated learning (FL) has attracted more and more attention recently. The integration of FL and edge computing makes the edge system more efficient and intelligent. FL usually uses the server to actively select certain edge devices to participate in the global model training. However, the selected edge devices may be stragglers, or even crash during training. Meanwhile, the unselected idle edge devices cannot be fully utilized for training. Therefore, besides the widely studied communication efficiency and data heterogeneity issues in FL, we also take the above time efficiency into consideration, and propose a time-efficient asynchronous federated learning protocol, TEA-Fed, to solve these problems. With TEA-Fed, idle edge devices actively apply for training tasks and participate in model training asynchronously once assigned tasks. Considering that there may be a huge number of edge devices in edge computing, we introduce control parameters to limit the number of devices participating in training the identical model at the same time. Meanwhile, we also introduce caching mechanism and weighted averaging with respect to model staleness in the model aggregation step to reduce the adverse effects of model staleness and further improve the accuracy of the global model. Finally, the experimental results show that the protocol can accelerate the convergence of model training, improve the accuracy, and has robustness to heterogeneous data.

关键词： Federated learning machine learning Edge computing Time efficiency

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Novel soft Sensor Model based on Spatio-Temporal Attention

Novel Soft Sensor Model based on Spatio-Temporal Attention

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international Joint conference on Neural Networks (IJCNN)

作者： Hu, Xuan Geng, Zhiqiang Han, Yongming Huang, Wei Chen, Kai Xie, Feng Guizhou Prov Key Lab Publ Big Data Guiyang 550025 Peoples R China Beijing Univ Chem Technol Coll Informat Sci Technol Beijing Peoples R China

ISBN: (纸本)9780738133669

Industrial process data is usually time series data collected by sensors with high non-linearity, dynamics and high noise. Many existing soft sensor models usually focus on the temporal features of industrial process data, while ignoring the spatial interaction between auxiliary variables. Therefore, a novel spatio-temporal attention neural network (STAN) is proposed for dynamic soft sensor modeling of industrial processes. The temporal attention module and the spatial attention module extract the temporal and spatial interaction features of sequence data, respectively. Then the spatio-temporal fusion module adaptively controls the fusion of the temporal and spatial interactive features to obtain the spatio-temporal interactive features. Finally, the spatio-temporal interaction features are input into the fully connected layer and the highway layer of the STAN to output the final prediction result. The STAN is applied in the dynamic soft sensor modeling of polypropylene melt index. Compared with backpropagation neural network (BPNN), extreme learning machine (ELM), long short-term memory (LSTM) and convolutional LSTM (CNN+LSTM), the STAN achieves the state-of-the-art results.

关键词： Spatio-temporal feature soft sensor Polypropylene Spatio-temporal gate Neural network

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Optimizing Instance Selection Strategies in Interactive machine learning: An Application to Fraud Detection 1

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20th international conference on Hybrid Intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Carneiro, Davide Guimarães, Miguel Sousa, Miguel CIICESI Escola Superior de Tecnologia e Gestão Instituto Politécnico do Porto Felgueiras Portugal

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

machine learning systems are generally thought of as fully automatic. However, in recent years, interactive systems in which Human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained machine learning models which improve over time. In this paper we show that the order by which instances are evaluated by the auditors, and their feedback incorporated, influences the evolution of the performance of the system over time. The goal of this paper is to study of different instance selection strategies for Human evaluation and feedback can improve the learning speed. This information can then be used by the system to determine, at each moment, which instances would improve the system the most, so that these can be suggested to the users for validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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A Network Malicious Traffic Detection Method Based on Semi-Supervised Deep learning

A Network Malicious Traffic Detection Method Based on Semi-S...

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2021 IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2021

作者： Lu, Wanzhi Ding, Yaojun Gansu University of Political Science and Law School of Cyberspace Security Lanzhou730070 China

ISBN: (纸本)9781665429184

As an effective means of network security protection, the key technology of network malicious traffic detection was to accurately identify various attacks in the network. Although many supervised learning and unsupervised learning in the field of machine learning have been used to improve the efficiency of malicious traffic detection, it is still a problem for the existing malicious traffic detection algorithms to achieve a good performance. Aiming at the problem of low efficiency of network traffic detection caused by the continuous change of network attack types in the real network environment, this paper proposes a network malicious traffic detection method based on semi-supervised deep learning, which uses ladder network to integrate supervised learning and unsupervised learning. Firstly, random forest algorithm is used to rank the importance of features and select the best top 20 features;Secondly, the feature data is preprocessed and input into the ladder network, and the parameters of the training model are optimized;Finally, CICIDS2017 and KDD CUP99 are used to evaluate the accuracy, recall and F1 value on the trained ladder network. The experimental results show that, Under the condition of the same total number of samples, The malicious traffic detection method based on the semi-supervised ladder network can detect network malicious traffic better than other semi-supervised learning models with only 50% of the labeled samples. © 2021 IEEE.

关键词： Network security

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Analysis and Application of Multispectral Data for Water Segmentation Using machine learning

Analysis and Application of Multispectral Data for Water ...

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international conference on Computer Vision and machine Intelligence, CVMI 2022

作者： Gupta, Shubham Uma, D. Hebbar, R. PES University KA Bangaluru560085 India Regional Remote Sensing Centre–South ISRO KA Bengaluru560037 India

ISBN: (纸本)9789811978661

Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolution data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave-infrared bands (B11 and B12) are the most superior for segmenting water bodies. B11 achieves an overall accuracy of 71 % while B12 achieves 69 % across all algorithms on the test site. We also find that the Support Vector machine (SVM) algorithm is the most favorable for single-band water segmentation. The SVM achieves an overall accuracy of 69 % across the tested bands over the given test site. Finally, to demonstrate the effectiveness of choosing the right amount of data, we use only B11 reflectance data to train an artificial neural network, BandNet. Even with a basic architecture, BandNet is proportionate to known architectures for semantic and water segmentation, achieving a 92.47 mIOU on the test site. BandNet requires only a fraction of the time and resources to train and run inference, making it suitable to be deployed on web applications to run and monitor water bodies in localized regions. Our codebase is available at https://***/IamShubhamGupto/BandNet. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Strengthening IDS against Evasion Attacks with GAN-based Adversarial Samples in SDN-enabled network 15

Strengthening IDS against Evasion Attacks with GAN-based Adv...

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15th RIVF international conference on computing and Communication Technologies (RIVF)

作者： Cao Phan Xuan Qui Dang Hong Quang Phan The Duy Do Thi Thu Hien Van-Hau Pham Univ Informat Technol Informat Secur Lab Hochiminh City Vietnam Vietnam Natl Univ Ho Chi Minh City Hochiminh City Vietnam

ISBN: (纸本)9781665404358

With the spread of the number of smart devices in the context of Smart City, software Defined Networking (SDN) is considered as a vital principle to manage a large-scale heterogeneous network within centralized controller. To deal with cyberattacks against such networks, intrusion detection system (IDS) is built to recognize and alert to the system administrator for further appropriate response. Currently, machine learning-based IDS (ML-IDS) has been explored and is still being developed. However, these systems give a high rate of false alert and are easily deceived by sophisticated attacks such as variants of attacks containing perturbation. Therefore, it is necessary to continuously evaluate and improve these systems by simulating mutation of real-world network attack. Relied on the Generative Discriminative Networks (GANs), we introduce DIGFuPAS, a framework that generates data flow of cyberattacks capable of bypassing ML-IDS. It can generate malicious data streams that mutate from real attack traffic making the IDS undetectable. The generated traffic flow is used to retrain ML-IDS, for improving the robustness of IDS in detecting sophisticated attacks. The experiments are performed and evaluated through 2 criteria: Detection rate (DR) and F1 Score (F1) on the public dataset, named CICIDS2017. DIGFuPAS can be used for continuously pentesting and evaluating IDS's capability once integrated as an automated sustainability test pipeline for SDN-enabled networks.

关键词： IDS machine learning IDS Generative Adversarial Networks Adversarial Attacks

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