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A Behavioural Network Traffic Novelty Detection for the Internet of Things Infrastructures 1

11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Hamad, Salma Abdalla Sheng, Quan Z. Tran, Dai Hoang Zhang, Wei Emma Nepal, Surya Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia School of Computer Science The University of Adelaide Adelaide Australia Data61 CSIRO Sydney Australia

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

The Internet of Things (IoT) applied solutions are changing the way the world perceives technology. IoT devices are now being used in a wide range of applications to transfer or share relevant information, hence reducing human interventions. With such widespread IoT solutions, security becomes a significant concern. Many of the IoT devices are vulnerable due to several reasons, including in-secure implementations, poor life cycle management, and inappropriate configurations, leading to an increase in the risk of these devices getting exposed and attacked. However, the current security approaches for detecting compromised IoT devices are inefficient, especially for zero-day attacks. Since no one knows how a new attack would look like, it will be useful to monitor and detect anomalies using accurate detection techniques. This work probes the possibility of detecting IoT network traffic anomalies using novelty detection techniques;thus, it can detect compromised IoT devices. One of this work’s main contributions is developing an IoT anomaly detection system named Behavioural Novelty Detection for IoT Infrastructure (BND-IoT). BND-IoT trains a neural network with novel selected behavioural features extracted from benign traffic only and then uses the novelty techniques to detect any unusual traffic patterns. We show that the presented approach effectively detects anomalies within IoT devices’ network traffic with a robust average F1-score of 96.7% and a low false rejection rate of 7%. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow 2

Dynamic Lane Management with IoT for Real-time Lane Configur...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Pasha, Mohammed Rehaman Latha, S. Usha, R. Marin, V. Bini Ishwarya, M.V. Muthulekshmi, M. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Department of Department of Biotechnology Tamil Nadu Salem India S. A. Engineering College Department of Mathematics Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Ramapuram Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology's potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control. © 2024 IEEE.

关键词： Internet of things

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Exploration of Efficient Neural Network for Path Optimization Method 39

Exploration of Efficient Neural Network for Path Optimizatio...

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Namekawa, Yusuke Kashiwa, Kouji Matsuda, Hidefumi Ohnishi, Akira Takase, Hayato Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan

We present our attempts to control the sign problem by the path optimization method with an emphasis on the efficiency of the neural network. We found a gauge invariant neural network is successful in the 2-dimensional U(1) gauge theory with a complex coupling. We also investigate the possibility of improvement in the learning process. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Quantum theory

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Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

arXiv

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arXiv 2023年

作者： Qazanfari, Hamed Alyan Nezhadi, Mohammad M. Khoshdaregi, Zohreh Nozari Image Processing and Data Mining Lab Shahrood University of Technology Shahrood Iran Faculty of Science University of Science and Technology of Mazandaran Behshahr Iran Department of Computer Engineering University of Bojnord Iran

Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar images based on content features. Challenges faced by CBIR systems, including the semantic gap and scalability, are discussed, along with potential solutions. It elaborates on the semantic gap, which arises from the disparity between low-level features and high-level semantic concepts, and explores approaches to bridge this gap. One notable solution is the integration of relevance feedback (RF), empowering users to provide feedback on retrieved images and refine search results iteratively. The survey encompasses long-term and short-term learning approaches that leverage RF for enhanced CBIR accuracy and relevance. These methods focus on weight optimization and the utilization of active learning algorithms to select samples for training classifiers. Furthermore, the paper investigates machine learning techniques and the utilization of deep learning and convolutional neural networks to enhance CBIR performance. This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques. It guides researchers and practitioners in comprehending existing methodologies, challenges, and potential solutions while fostering knowledge dissemination and identifying research gaps. By addressing future research directions, it sets the stage for advancements in CBIR that will enhance retrieval accuracy, usability, and effectiveness in various application domains. © 2023, CC BY.

关键词： Object detection

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PM2.5 Forecasting Model based on Linear and Non-linear Hybrid Algorithm

PM2.5 Forecasting Model based on Linear and Non-linear Hybri...

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International Conference on Knowledge and Smart Technology (KST)

作者： Anupong Banjongkan Nittaya Kerdprasop Anusara Hirunyawanakul Kittisak Kerdprasop School of Computer Engineering Suranaree University of Technology Thailand Data Science and Computation Faculty of Science Energy and Environment King Mongkut’s University of Technology North Bangkok Rayong Campus Thailand

Air pollution is one of the harmful problems that the world has focused on and needs to be solved urgently because air pollution has a direct impact on humans leading to premature death caused by various diseases such as asthma inflammatory respiratory disease, lung cancer, and so on. The air pollutants, especially tiny particulate matter (PM), are currently receiving attention because they are a major problem in many large and populated cities around the world. This paper proposed a time-series model for forecasting PM 2.5 in advance through a machine learning process with a linear and non-linear hybrid algorithm. A hybrid algorithm that brings together the capabilities of autoregressive integrated moving average (ARIMA) and the adaptive-neuro fuzzy inference system (ANFIS) is used to find the linear and nonlinear correlation of the PM 2.5 time-series data. The proposed model is called ARIMA-FIS which uses the gradient descent (GD) method in the learning process. The dataset used in this research is the daily recorded of PM 2.5 values in Rayong province, which is the industrial city in Thailand. The results showed that the ARIMA-FIS model had the best performance in forecasting PM 2.5 in advance with the least error at 3.46 of mean absolute error (MAE) and 5.11 of root mean square error (RMSE). The proposed model gave the percentage of RMSE almost 3% better than the other standard time-series models.

关键词： Adaptation models Machine learning algorithms Urban areas Machine learning Predictive models Air pollution Inference algorithms

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Min Li Jinghui He Zuobin Ying Gang Li Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings.

关键词： Accuracy Computational modeling Memory management Production Feature extraction Real-time systems Product design Quality assessment Reliability Anomaly detection

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Estimation of River Water Surface Elevation Using UAV Photogrammetry and Machine Learning

arXiv

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arXiv 2023年

作者： Szostak, Radoslaw Pietroń, Marcin Wachniew, Przemyslaw Zimnoch, Miroslaw Ćwiąkala, Pawel AGH University of Science and Technology Faculty of Physics and Applied Computer Science ul. Reymonta 19 building D-10 Kraków Poland AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications al. Mickiewicza 30 building D-17 Krakow Poland AGH University of Science and Technology Faculty of Geo-Data Science Geodesy and Environmental Engineering al. Mickiewicza 30 building C-4 Krakow Poland

Unmanned aerial vehicle (UAV) photogrammetry allows for the creation of orthophotos and digital surface models (DSMs) of a terrain. However, DSMs of water bodies mapped with this technique reveal water surface distortions, preventing the use of photogrammetric data for accurate determination of water surface elevation (WSE). Firstly, we propose a new solution in which a deep learning (DL) convolutional neural network (CNN) is used as a WSE estimator from photogrammetric DSMs and orthophotos. Second, we improved the previously known "water-edge" method by filtering the outliers using a forward-backwards exponential weighted moving average (FBEWMA). An additional improvement in these two methods was achieved by performing a linear regression of the WSE values against chainage. Furthermore, the solutions estimate the uncertainty of the predictions, which allows the evaluation of the model credibility when the ground-truth values are unknown. To our knowledge, this is the first approach in which DL was used for this task. A brand new machine learning data set has been created for the purpose of this work. It was collected on a small lowland river in winter and summer conditions. It consists of 322 samples, each corresponding to a 10 by 10 meter area of the river channel and adjacent land. Each data set sample contains orthophoto and DSM arrays as input, along with a single ground-truth WSE value as output. The data set was supplemented with data collected by other researchers that compared the state-of-the-art methods for determining WSE using an UAV. This makes these additional data an excellent benchmark to compare the method developed with existing ones. The results of the DL solution were verified using the reliable k-fold cross-validation method, in which samples from a single survey campaign were excluded from training and used as a validation subset. This provided an in-depth examination of the model's ability to perform on previously unseen data. The WSE RMSE va

关键词： Antennas

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FGSNet: A Finer-Grained Siamese Network for Industrial Few-Shot Anomaly Detection

FGSNet: A Finer-Grained Siamese Network for Industrial Few-S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： DeLong Han Luo Xu MingLe Zhou Gang Li Min Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image anomaly detection usually relies on a large set of training samples and abnormal samples are relatively rare and challenging to obtain in daily industrial scenarios. In the scenario of Few-Shot Anomaly Detection (FSAD), how to better utilize the fine-grained features of the few images is a key issue. To solve these problems, a Finer-Grained Siamese Network (FGSNet) is proposed in this paper. FGSNet considers the setting of Few-Shot Anomaly Detection (FSAD) and consists of two stages. The first stage is responsible for extracting finer-grained features and roughly aligning the image features, while incorporating an efficient Fine-grained Feature Fusion Module $(\mathbf{F}^{3}\mathbf{M})$ to enhance feature representation. Meanwhile, we design a Deep-supervised Loss to improve the level of fine-grained information extraction. The second is primarily used to refine and denoise the fused features, followed by detailed alignment operations for subsequent feature distribution modeling. Compared to most existing FASD models that use single-class training and single-model evaluation, FGSNet is more suitable for existing industrial scenarios. Extensive experiments are conducted on the publicly available MVTec AD and MPDD datasets in this paper. The results show that, for 2, 4 and 8-shot cases, FGSNet can increase the average Image-AUROC on MVTec AD by 0.87%, 1.27% and 1.33% respectively, compared to RegAD.

关键词： Training Feature extraction Information retrieval Anomaly detection Cybernetics

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A Practical Multi-Protocol Collaborative QKD Networking Scheme

arXiv

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arXiv 2023年

作者： Yao, Jia-Meng Li, Qiong Mao, Hao-Kun Abd El-Latif, Ahmed A. Chen, Nan The Department of the School of Computer Science and Technology Harbin Institute of Technology Harbin China The School of Foreign Languages Harbin Institute of Technology Harbin China The EIAS Data Science Lab. College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia The Department of Mathematics & Computer Science Faculty of Science Menoufia University Egypt

With the advancement of quantum computing technology, the security of classical public key cryptography is under serious threat. To guarantee security in the quantum era, Quantum Key Distribution (QKD) network technique has become a competitive solution and attracted wide attention. QKD networks can be classified into two main categories: measurement-device-dependent protocol-based network and measurement-device-independent protocol-based network. In measurement-device-dependent protocol-based networks, the transmitted information is available for all trusted relays. This means that all trusted relays are strongly trusted relays that require strict security control, which is very difficult to realize in real life. To address this issue, measurement-device-independent protocol-based networks reduce the proportion of strongly trusted relay nodes by introducing untrusted relays to alleviate the security control pressure. However, due to the higher key rate of measurement-device-dependent protocols over short distances, the communication capability of measurement-device-independent protocol-based networks has a large degradation compared to measurement-device-dependent protocol-based networks. Therefore, how to reduce the dependence of QKD networks on strong trusted relays without significantly affecting the communication capability of the network has become a major issue in the practicalization process of QKD networks. To address this issue, a novel Multi-Protocol Collaborative (MPC) networking cell is proposed in this paper. The QKD network built by the MPC networking cell reduces the dependence on strongly trusted relays by combining the two protocols to introduce weak trusted relays while maintaining the high communication capacity. What’s more, to further enhance the overall performance of the QKD network, an optimal topology design method is presented via the proposed flow-based mathematical model and optimization method. The simulation results show that the propo

关键词： Quantum cryptography

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A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification

arXiv

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arXiv 2025年

作者： Büyüktaş, Barış Sumbul, Gencer Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany Sion1950 Switzerland

Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharing the local data of the clients. Most of the existing FL methods assume that the data distributed across all clients is associated with the same data modality. However, remote sensing (RS) images present in different clients can be associated with diverse data modalities. The joint use of the multi-modal RS data can significantly enhance classification performance. To effectively exploit decentralized and unshared multi-modal RS data, our paper introduces a novel multi-modal FL framework for RS image classification problems. The proposed framework comprises three modules: 1) multi-modal fusion (MF);2) feature whitening (FW);and 3) mutual information maximization (MIM). The MF module employs iterative model averaging to facilitate learning without accessing multi-modal training data on clients. The FW module aims to address the limitations of training data heterogeneity by aligning data distributions across clients. The MIM module aims to model mutual information by maximizing the similarity between images from different modalities. For the experimental analyses, we focus our attention on multi-label classification and pixel-based classification tasks in RS. The results obtained using two benchmark archives show the effectiveness of the proposed framework when compared to state-of-the-art algorithms in the literature. The code of the proposed framework will be available at https://***/rsim/multi-modal-FL. © 2025, CC BY.

关键词： Deep neural networks

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