检索结果-内蒙古大学图书馆

Deep learning and ensemble methods for anomaly detection in ICS security

International Journal of Information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

来源：评论

学校读者我要写书评

暂无评论

A Study on the Transmission Dynamics of the Omicron Variant of COVID-19 Using Nonlinear mathematical Models

引用

computer Modeling in engineering & sciences 2024年第6期139卷 2265-2287页

作者： S.Dickson S.Padmasekaran Pushpendra Kumar Kottakkaran Sooppy Nisar Hamidreza Marasi Department of Mathematics Periyar UniversitySalemTamilnadu636011India Faculty of Engineering and Natural Sciences Istanbul Okan UniversityIstanbulTurkey Department of Mathematics College of Science and Humanities in AlkharjPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Mathematics Faculty of MathematicsStatistics and Computer ScienceUniversity of TabrizTabrizIran

This research examines the transmission dynamics of the Omicron variant of COVID-19 using SEIQIcRVW and SQIRV models,considering the delay in converting susceptible individuals into infected *** significant delays eventually resulted in the pandemic’s *** ensure the safety of the host population,this concept integrates quarantine and the COVID-19 *** investigate the stability of the proposed *** fundamental reproduction number influences stability *** to our findings,asymptomatic cases considerably impact the prevalence of Omicron infection in the *** real data of the Omicron variant from Chennai,Tamil Nadu,India,is used to validate the outputs.

关键词： Omicron local stability reproduction number steady states global stability

来源：评论

学校读者我要写书评

暂无评论

On Kernel Search Based Gaussian Process Anomaly Detection 1

引用

1st International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2020 and 2nd International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2021

作者： Hüwel, Jan David Besginow, Andreas Berns, Fabian Lange-Hegermann, Markus Beecks, Christian Department of Computer Science University of Münster Münster Germany Department of Electrical Engineering and Computer Science OWL University of Applied Sciences and Arts Lemgo Germany

ISBN: (数字)9783031372285

ISBN: (纸本)9783031372278

Anomaly detection becomes more important with increasing automation. Especially for time series data, prevalent in industry, there are numerous methods that have been well researched. In this work we provide a proof of concept for a novel approach using the interpretability of Gaussian processes. To detect an abnormal section, the data is split into equally sized segments which are then interpreted individually using separate kernel searches. The resulting kernels can then be compared and clustered by one of multiple presented methods. The segments that contain an anomaly end up in their own cluster. To test all possible configurations of our proposed approach, we applied them to a subset of the SIGKDD 2021 anomaly dataset mutliple times and evaluated the results. Almost all configurations were able to succeed, although not yet reliably reproducible. The results of our performance evaluation indicate that kernel searches are in principle applicable to anomaly detection in univariate time series data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for MRI-Based Brain Tumour Identification and Classification 1

引用

8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Jareena Begum, D. Chokkalingam, S.P. Sundaravadivazhagan, B. Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences University of Technology and Applied Sciences Al Mussana Oman

ISBN: (数字)9789819732456

ISBN: (纸本)9789819732449

Radiology tumour spotting is complicated and requires medical knowledge. Thus, a lack of doctors should not delay cancer detection programmes. Biomedical image processing software helps find brain tumours in MRI data. This study segmented and detected brain tumours using MRI sequence images. This process is complicated by the similarity of normal tissues and the wide range of tumour tissues in different patients. Brain tumour detection is the main goal. Brain tumour diagnosis requires precise tumour size and position. This paper presents a deep learning-based brain tumour MRI segmentation and classification method. We preprocessed the photos with the Gaussian blur filter and image enhancement tool. Binary thresholding segments. Morphological methods reveal traits. CNN will assess brain MRI regularity. This study uses the Kaggle Dataset. We train with 255 brain MRIs, 155 with tumours and 98 normal, and achieve 97% accuracy. We test the model using Kaggle brain MR datasets. Brain tumour detection, segmentation, and categorization;medical image processing. This study proposed an easy-to-implement way for identifying and classifying bone tumours. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

引用

Neural Computing and Applications 2024年第27期36卷 16727-16767页

作者： Paheding, Sidike Saleem, Ashraf Siddiqui, Mohammad Faridul Haque Rawashdeh, Nathir Essa, Almabrok Reyes, Abel A. Department of Computer Science and Engineering Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Computer Science West Texas AM University 2501 4th Ave CanyonTX79016 United States Department of Mathematics Computer Science and Data Science John Carroll University 1 John Carroll Blvd University HeightsOH44118 United States

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field. © The Author(s) 2024.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Face Identification with Feature Learning Using Quasilinear Partial Differential Equation 15th

Face Identification with Feature Learning Using Quasilinear ...

引用

15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Muthukumaran, V. Satheesh Kumar, S. Radhika, E.K. Arpana, G.K. Nalini, R. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Computer Science Sindhi College Bangalore India Department of ECE East West College of Engineering Bangalore India

ISBN: (纸本)9783031810855

This study suggests a novel soft computing approach for enhancing face recognition performance using the feature learning methodology. The suggested system of Quasi-linear Partial Differential Equations (QPDE) based face recognition model is used to increase the accuracy of facial identification. The webcam first catches the user's image and saves it to the user's computer. The inference model receives the captured image. The input image is pre-processed to enhance the image, and then the image is split into numerous integral images at the inference model. Using speed-up robust features, an intriguing point is constructed for each integral image. The computed interest point is converted into a QPDE based weight matrix by representing the strength of features at all the regions of the face. With the generated weighted matrix of features extracted from the facial image, the method generates a Fast Artificial intelligence (AI) network with many layers. Each layer of the neural network has many neurons with dedicated functions to compute the similarity and feature weight using QPDE. The proposed approach has produced efficient results compared to earlier methods for the Flickr-Faces-HQ database (FFHQ) datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Soft computing

来源：评论

学校读者我要写书评

暂无评论

A UWB Based Tracking and Management System for Streamlining Steel Ladle Operations 10

A UWB Based Tracking and Management System for Streamlining ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Srinivasan, V. Vaishnavi, S. Aravindh, V. Ashwin, V. Nishok, M. Sri Ramakrishna Engineering College Department of Mechanical Engineering Tamil Nadu Coimbatore India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Ladle monitoring systems are crucial for optimizing steel melting operations, enhancing material and equipment flow, reducing wait times, boosting production, facilitating rapid problem-solving, and ensuring steel quality. While GPS and RFID are common tracking methods, GPS's accuracy is indoor limited, and RFID's precision is constrained by range and proximity. However, a proposed Ultra-Wideband (UWB) system, offering precise tracking by utilizing radio waves. UWB translates pulses into data, operating at a high frequency. UWB anchors transmit signals to UWB tags, calculating roundtrip time for location determination (Time of Flight). These values are converted into location coordinates using a plane position algorithm and stored in a database. A web app in the centralized control system retrieves and displays tracked data, including the ladle's current position, ensuring efficient material flow and real-time monitoring of steel quality. © 2024 IEEE.

关键词： ESP 32 Indoor Positioning System Plane position algorithm Real Time monitoring RFID and GPS Time of Flight Turtle Graphics UWB

来源：评论

学校读者我要写书评

暂无评论

Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of DSR, AODV and MP-OLSR Routing Protocols Using NS-2 Simulator in MANETs 4th

Performance Evaluation of DSR, AODV and MP-OLSR Routing Prot...

引用

4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Khan, Hameed Kushwah, Kamal Kumar Thakur, Jitendra Singh Soni, Gireesh Gaurav Tripathi, Abhishek Rao, Sandeep Department of Computer Science and Engineering Jabalpur Engineering College M.P. Jabalpur India Department of Applied Physics Jabalpur Engineering College M.P. Jabalpur India Department of Applied Physics and Optoelectronics SGSITS M.P. Indore India

ISBN: (纸本)9783031488900

MANET (Ad-Hoc Mobile Network) is a systematic aggregation of identical types and varieties of nodes. These nodes are dynamically created as desirable and capable of communicating, barring a primary infrastructure-based system. As these nodes connect devices such as mobiles, tablets, etc., they can develop a range of provider delivery with an appreciation for network performance. Network traffic is an essential assignment in the ad-hoc mobile area network. Route agreements efficiently enhance carrier exceptionality in better access, partial delivery of packets, and minimal storage delays. The predominant motive of this analysis is to evaluate the legal method concerning the parameters of the various quality of service enhancement services. The simulation outcomes affirm that the proposed scenario affords a better dimension of the exceptional testing of the compliance offerings at MANET. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

Facial Expression Recognition (FER) System Using Deep Learning 1

Facial Expression Recognition (FER) System Using Deep Learni...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Yan, Ooi Juin Ashraf, Humaira Ihsan, Uswa Jhanjhi, Nz Ray, Sayan Kumar Kavita Taylor's University Masters of Applied Computing Selangor Malaysia Taylor's University School of Computer Science SCS Malaysia International Islamic University Department of Computer Science Islamabad Pakistan Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

Sentiment analysis of both text and images have been widely implemented machine learning application. Facial expression recognition (FER) is a widely known application of sentiment analysis of image data. This application has already been implemented in various industry, but challenges such as the complexities of facial features and different image angle still remain. This project aims to develop a FER system capable of classifying human expressions through facial images using deep learning techniques. A CNN-based architecture was proposed and training using the FER2013 dataset, which is a commonly used facial expression dataset. Overall, the trained model achieved an accuracy of 63.97% through the dataset. The trained CNN model was then employed to develop a web-based prototype of a FER system through Python Flask. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：