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IEEE 7th International Conference on Service-Oriented Computing and Applications (SOCA)

作者： Otsuka, Takanobu Torii, Yoshitaka Ito, Takayuki Nagoya Inst Technol Gokiso Showa 4668555 Japan

ISBN: (纸本)9781479968336

In recent years, we have witnessed an unpresented increase localized heavy weather phenomena such as tornadoes and localized heavy rain which can not be expected by the conventional weather forecast system. However, the number of observation posts is few little for forecasting for tornadoes and heavy rain. It is necessary to increase dramatically the observation points in order to perform ware correct prediction using real data. We have developed a compact and low-cost pressure information acquisition system, to detect the signs of localized abnormal weather. This research proposes an algorithm to predict local weather by detecting anomalous pressure values in the time series of the pressure sensor information, and then to notify users.

关键词： geophysics computing pressure sensors rain time series weather forecasting wireless sensor networks anomalous pressure value detection anomaly detection algorithm localized abnormal weather localized heavy rain forecasting localized heavy weather phenomena low-cost wireless sensor nodes pressure information acquisition system pressure sensor information tornado forecasting user notification Atmospheric measurements Cities and towns Clouds Time series analysis Tornadoes Weather forecasting anomaly detection Localized abnormal weather detection Wireless Sensor Network weather forecasting tornadoes Wireless sensor networks anomaly detection Cities and towns geophysics computing atmospheric monitoring Time series Pressure Sensor Device Component time series analysis Clouds rain detection algorithms rainstorms Weather

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anomaly detection algorithm with Blockchain to Detect Potential Security Attacks in the IIoT Model of Industry 5.0 2nd

Anomaly Detection Algorithm with Blockchain to Detect Potent...

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2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023

作者： Pant, Piyush Goyal, S.B. Rajawat, Anand Singh Potgantwar, Amol Bedi, Pradeep Djeddi, Chawki School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Sandip Institute of Technology and Research Center Nashik India Galgotias University Greater Noida India Laboratoire de Vison et d’intelligence Artificielle Université Larbi Tebessi Tébessa Algeria

ISBN: (纸本)9783031509193

The research presents a model for detecting potential security attacks in the Industry 5.0’s Internet of Things (IIoT) model using an anomaly detection algorithm, with Blockchain technology to further enhance security. One-class Support Vector Machines (SVM) is used as the anomaly detection algorithm, to identify any unusual behavior in the IIoT system. The proposed model ensures the integrity of data by implementing the decentralized features of Blockchain technology. This paper aims to address the current security challenges faced by Industry 5.0 and enhance the reliability of the IIoT model. Since Industry 5.0 is not here yet, hypothetical data is used to train the model which is generated after seeding using Numpy. The Blockchain technology enhanced the overall security of the Industrial Internet of Things (IIoT) model whereas, to secure it even further by detecting anomalous activities, the machine learning algorithm is proposed. anomaly detection algorithm with Gaussian distribution is proposed through One-class SVM. The threshold for an activity to be classified as unusual or anomalous is discussed in the paper along with the difference between classification algorithm and anomaly detection algorithm. The research implemented One-class SVM algorithm to train the model by randomly seeding data using Numpy with an average accuracy of 92.8% after 5 different runs with different datasets. The algorithm also focused on other applications of the model like detection of faulty driver, device, or equipment. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： anomaly detection algorithm Artificial Intelligence Blockchain Gaussian distribution IIoT Industry 5 Machine learning

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Real-Time Building Management System Visual anomaly detection Using Heat Points Motion Analysis Machine Learning algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第1期30卷 318-323页

作者： Avci, Isa Bidollahkhani, Michael Karabuk Univ Kastamonu Yolu Demir Celik Kampusu TR-78050 Kilavuzlar Karabuk Turkiye

The multiplicity of design, construction, and use of IoT devices in homes has made it crucial to provide secure and manageable building management systems and platforms. Increasing security requires increasing the complexity of the user interface and the access verification steps in the system. Today, multi-step verification methods are used via SMS, call, or e-mail to do this. Another topic mentioned here is physical home security and energy management. Artificial intelligence and machine learning-based tools and algorithms are used to analyze images and data from sensors and security cameras. However, these tools are not always available due to the increase in data volume over time and the need for large processing resources. In this study, a new method is proposed to reduce the usage of process resources and the percentage of system error in anomaly detection by reducing visual data to critical points by using thermal cameras. This method can also be used in energy management using home and ambient temperature and user activity measurements. The statistical results of the visual comparison between the proposed method and the legacy CCTV -based visual and sensory surveillance shown in the results section demonstrate its reliability and accuracy.

关键词： anomaly detection algorithm building management system digital image processing internet of things machine learning

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Ubiquitous Computing of Low-Cost Pollutant Sensors for Monitoring Pollutant Gases in the Construction Sector

Ubiquitous Computing of Low-Cost Pollutant Sensors for Monit...

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3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Geddam, Rebakah Zuhair, Mohd Shah, Kush Vyas, Karm Kanani, Naitik CSE Department School of Technology Nirma University Ahmedabad India

ISBN: (纸本)9783031863042

The growing urbanization in India has been closely associated with the expansion of the cement industry. Acknowledging the significant impact of air pollution from the cement industry on the environment, human health, and the economies of affected nations is imperative. To safeguard individuals from health issues arising from air pollution, it is crucial to accurately and promptly anticipate the ambient air quality index. The pollution control board conducts an annual audit to ensure the implementation of Sustainable Development Goals (SDG) policies aimed at climate mitigation. A low-cost sensor employing a cloud-edge architecture was installed in the cement industry to monitor Ambient Air Quality for ubiquitous computing. The innovative approach in this study involves monitoring air quality by detecting anomalies during a calibration drive. Comprehensive emissions monitoring is achieved across various operational scenarios by combining data from multiple sensors, including pollution sensors, PPM sensor arrays, and environmental sensors. The results demonstrate a substantial enhancement in the accuracy of air quality monitoring and the early detection of potential health risks. The article elucidates anomaly detection algorithms, namely Isolation Forest (IFor), Local Outlier Factor (LOF), and DBSCAN, compared between the industry-set sensor sn and the environment lab-tested referenced sensor sv. After removing anomalous readings, RMSE scores of 22.75 and 17.7 were recorded. The article emphasizes adaptable solutions for avoiding unhealthy air, benefiting the environment, and improving public health. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ambient Air Quality (AAQ) anomaly detection algorithm Cement Industry Environmental Sensor Public Health Sensor Drift

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anomaly detection for Connected and Automated Vehicles: Accident Analysis

Anomaly Detection for Connected and Automated Vehicles: Acci...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Girdhar, Mansi Hong, Junho You, Yongsik Song, Tai-Jin Univ Michigan Dearborn Dept Elect & Comp Engn Dearborn MI 48128 USA Chungbuk Natl Univ Dept Urban Engn Cheongju South Korea

ISBN: (纸本)9798350397420

Smart mobility is a key component of smart cities, and the switch from traditional automotive systems to connected and automated vehicles (CAVs) is recognized as one of the evolving technologies on urban roads. Although the current autonomous vehicle (AV) mobility environment may be geared toward infrastructure and road users, it cannot facilitate the adoption of CAV in the future due to the presence of different modules that are nested in the cyberspace. Furthermore, the ability to make accurate decisions in real-time is essential for the success of autonomous systems. However, cyberattacks on these entities might skew the decision-making processes, which can result in complex CAV mishaps. Furthermore, the method utilized by the police to conduct accident investigations cannot be used to identify road accidents brought on by cyberattacks. Therefore, this paper proposes a 5Ws & 1H-based investigation approach to deal with cyberattack-related accidents. Also, a stochastic anomaly detection system is proposed to identify the abnormal activities of the automated driving system (ADS) functions during a crash analysis. Further, two case studies are shown to validate the results of the proposed algorithms.

关键词： Accident investigation anomaly detection algorithm connected and autonomous vehicles cyberattacks accidents cybersecurity 5Ws & 1H

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anomaly detection from the operation history of DLNA devices

Anomaly detection from the operation history of DLNA devices

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2nd International Conference on Ubiquitous and Future Networks, ICUFN 2010

作者： Baek, Sunhye Si, Hua Saruwatari, Shunsuke Morikawa, Hiroyuki Hjelm, Johan Oda, Toshikane Research Center for Advanced Science and Technology University of Tokyo Tokyo 153-8904 Japan Ericsson Research Japan Tokyo 112-0004 Japan

ISBN: (纸本)9781424480883

With the development of DLNA technology, more and more DLNA devices appear in our lives. We can extract users' activity events from the operation history of DLNA devices, and provide various services based on them. In this paper, we discuss how to detect anomalous events from operation history of DLNA devices. As the basic technology, we first propose DLNA Probe, a system to acquire operation history from communication between DLNA devices by using ARP spoofing. Then, we discuss the selection of anomaly detection algorithms. Finally, we introduce a system for detecting anomalies from one of DLNA device. © 2010 IEEE.

关键词： ARP spoofing anomaly detection anomaly detection algorithm DLNA anomaly detection Surgery

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An approach for chest tube detection in chest radiographs

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IET IMAGE PROCESSING 2014年第2期8卷 122-129页

作者： Mercan, Cem Ahmet Celebi, Mustafa Serdar Istanbul Tech Univ Inst Informat TR-34469 Istanbul Turkey

It is known that overlapping tissues cause highly complex projections in chest radiographs. In addition, artificial objects, such as catheters, chest tubes and pacemakers can appear on these radiographs. It is important that the anomaly detection algorithms are not confused by these objects. To achieve this goal, the authors propose an approach to train a convolutional neural network (CNN) to detect chest tubes present on radiographs. To detect the chest tube skeleton as the final output in a better manner, non-uniform rational B-spline curves are used to automatically fit with the CNN output. This is the first study conducted to automatically detect artificial objects in the lung region of chest radiographs. Other automatic detection schemes work on the mediastinum. The authors evaluated the performance of the model using a pixel-based receiver operating characteristic (ROC) analysis. Each true positive, true negative, false positive and false negative pixel is counted and used for calculating average accuracy, sensitivity and specificity percentages. The results were 99.99% accuracy, 59% sensitivity and 99.99% specificity. Therefore they obtained promising results on the detection of artificial objects.

关键词： diagnostic radiography learning (artificial intelligence) lung medical image processing object detection sensitivity analysis splines (mathematics) chest radiograph tissue catheter pacemaker anomaly detection algorithm convolutional neural network chest tube skeleton detection nonuniform rational B-spline curve artificial object detection lung region automatic detection scheme mediastinum pixel-based ROC analysis true positive pixel true negative pixel false positive pixel false negative pixel

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State-of-Charge Estimation for Lead-Acid Battery Using Isolation Forest algorithm and Long Short Term Memory Network With Attention Mechanism

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IEEE ACCESS 2023年 11卷 49193-49204页

作者： Zhang, Xinwei Li, Zhanying Zhou, Danhua Chen, Ming Dalian Polytech Univ Sch Informat Sci & Engn Dalian 116000 Peoples R China Jiangsu Acad Safety Sci & Technol Nanjing 210000 Peoples R China Shenzhen DACEEN Technol Co Ltd Shenzhen 518004 Peoples R China

State of charge (SOC) is the most direct embodiment of the state of a lead-acid battery, and accurate estimation of SOC is helpful to ensure the safe use of the battery. However, the traditional estimation model has low precision and weak anti-interference. In this study, a new SOC estimation structure is proposed. This structure is based on the effective combination of the Isolation Forest (IF) anomaly detection algorithm and Long Short-Term Memory (LSTM) Network combined with Attention Mechanism (IF-LSTM-Attention). The Isolation Forest algorithm is used to effectively detect the missing values and outliers contained in the original data. Based on the actual charging and discharging data, a sliding window is constructed as the data of the model to give full play to the LSTM network length dependence. And LSTM network combined with Attention Mechanism achieves high-precision SOC estimation. In addition, the conventional dropout technique and Bayesian optimizer are used to improve the model training convergence rate. The results show that the IF-LSTM-Attention model proposed in this study has higher accuracy and better generalization ability than the conventional LSTM model and Back Propagation (BP) neural network model.

关键词： State of charge Batteries anomaly detection Lead acid batteries Forestry Long short term memory Random forests Lead-acid battery anomaly detection algorithm long short-term memory network state of charge

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Adaptive Load Allocation for Combining anomaly Detectors Using Controlled Skips

Adaptive Load Allocation for Combining Anomaly Detectors Usi...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Berger, Mario Erlacher, Felix Sommer, Christoph Dressler, Falko Barracuda Networks Innsbruck Austria Univ Innsbruck Inst Comp Sci Comp & Commun Syst A-6020 Innsbruck Austria

ISBN: (纸本)9781479923588

Traditional Intrusion detection Systems (IDS) can be complemented by an anomaly detection algorithm (ADA) to also identify unknown attacks. We argue that, as each ADA has its own strengths and weaknesses, it might be beneficial to rely on multiple ADAs to obtain deeper insights. ADAs are very resource intensive;thus, real-time detection with multiple algorithms is even more challenging in high-speed networks. To handle such high data rates, we developed a controlled load allocation scheme that adaptively allocates multiple ADAs on a multi-core system. The key idea of this concept is to utilize as many algorithms as possible without causing random packet drops, which is the typical system behavior in overload situations. We developed a proof of concept anomaly detection framework with a sample set of ADAs. Our experiments confirm that the detection performance can substantially benefit from using multiple algorithms and that the developed framework is also able to cope with high packet rates.

关键词： multiprocessing systems real-time systems resource allocation security of data ADA IDS adaptive load allocation anomaly detection algorithm controlled load allocation controlled skips high-speed networks intrusion detection systems multicore system multiple algorithms real-time detection resource intensive unknown attacks High-speed networks Intrusion detection Probabilistic logic Reliability Uplink World Wide Web

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GAN-IVDS: An Intrusion detection System for Intelligent Connected Vehicles Based on Generative Adversarial Networks 8

GAN-IVDS: An Intrusion Detection System for Intelligent Conn...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Chen, Xiansen Xiao, Kun Luo, Lei Li, Yun Chen, Lirong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Guangdong Weichen Information Technology Co. Ltd Dongguan China

ISBN: (纸本)9798350331035

This paper proposes an anomaly detection algorithm based on Generative Adversarial Networks (GANs) for intrusion detection in Intelligent Connected Vehicles (ICVs). To address the imbalance between normal network traffic and attack traffic in vehicular networks, as well as the difficulty in collecting attack data, we train the neural network solely with normal traffic. We employ the Gini index to perform data dimensionality reduction, selecting 34 features with the highest purity of information and enhancing model training speed via normalization functions. Our algorithm combines the generative capabilities of GANs and the reconstructive power of autoencoders, detecting abnormal network data by learning the distribution of normal network data, thus eliminating the need to analyze packet payloads and safeguarding user privacy. We also develop a vehicular network traffic data collection scheme to collect network traffic data from ICVs. Experimental results indicate that our algorithm demonstrates high accuracy in detecting anomalous network traffic in ICVs, with an accuracy rate of 94.9%. © 2023 IEEE.

关键词： anomaly detection algorithm Generative Adversarial Networks Intelligent Connected Vehicles Intrusion detection Network Security

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