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IoT Intrusion Detection: Evaluating ML-Based IDS on Image and Network Traffic Datasets 9

IoT Intrusion Detection: Evaluating ML-Based IDS on Image an...

引用

9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Pathak, Manan Patel, Anuj Shah, Bela Cspit Charotar University of Science and Technology Dept. of Computer Science and Engineering Anand India

ISBN: (纸本)9798350383249

With the increasing pervasiveness of Internet of Things (IoT) devices, ensuring their security has become a critical concern. Advanced intrusion detection systems (IDS) leveraging machine learning (ML) and Deep learning(DL) techniques offer promising solutions for protecting IoT networks. However, the success of ML models depends heavily on preprocessing and feature extraction processes. This research presents a survey on ML-based IDS for IoT, focusing on ML models applied to both image(IEEE Dataport) and UNSW-NB15 datasets. The survey evaluates several feature extraction methods, including image filters and Machine learning models for the image dataset. For the UNSW-NB15 dataset, a range of preprocessing techniques specific to network traffic analysis, such as statistical features and protocol-specific features, are considered. In addition to feature extraction, various ML algorithms are assessed on both datasets. The evaluation is based on accuracy, precision, recall, and F1 score performance metrics, providing a comprehensive analysis of the ML model's effectiveness in detecting intrusions. This research contributes to the field of IoT security by providing insights into the performance of feature extraction algorithms and ML models on both image and network traffic datasets. The findings offer guidance for developing effective IDS solutions that can accurately detect and mitigate intrusions in IoT networks, enhancing the overall security of IoT devices and systems. © 2023 IEEE.

关键词： Internet of things

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Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM 4

Deep Learning-Based Stock Price Prediction: A Comprehensive ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chy, Md. Kalim Amzad Masum, Abdul Kadar Muhammad Alam, Mohammed Shamsul Azad, Md. Abul Kalam Hassan, Md. Maruf Bhuiyan, Touhid International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Islamic University of Technology Dept. of Business and Technology Management Gazipur Bangladesh School of It Washington University of Science and Technology VA United States

ISBN: (纸本)9798331529635

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation. © 2024 IEEE.

关键词： Mean square error

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An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the Hypso-1 Satellite 13

An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Tru...

引用

13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Justo, Jon A. Garrett, Joseph Langer, Dennis D. Henriksen, Marie B. Ionescu, Radu T. Johansen, Tor A. Norwegian University of Science and Technology Dept. Engineering Cybernetics Trondheim Norway Norwegian University of Science and Technology Dept. Marine Technology Trondheim Norway University of Bucharest Dept. of Computer Science Romania

ISBN: (纸本)9798350395570

Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200 diverse hyperspectral images from the HYPSO-1 mission, available in both raw and calibrated forms for scientific research in Earth observation. Moreover, 38 of these images from different countries include ground-truth labels at pixel-level totaling about 25 million spectral signatures labeled for sea/land/cloud categories. To demonstrate the potential of the dataset and its labeled subset, we have additionally optimized a deep learning model (1D Fully Convolutional Network), achieving superior performance to the current state of the art. The complete dataset, ground-truth labels, deep learning model, and software code are openly accessible for download at the website https://***/hypso1_sea_land_clouds_dataset/. © 2023 IEEE.

关键词： Remote sensing

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Association Rules Mining for a Specific Time Period in a Day in Large Transactional Database 2

Association Rules Mining for a Specific Time Period in a Day...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Islam, Md. Rabiul Rahman, Mst. Sahela Nayeem, Md. Julker Pundra University of Science and Technology Dept. of Computer Science and Engineering Bogura5800 Bangladesh

ISBN: (纸本)9798350358667

Market basket analysis is a trendy topic in the field of data mining. Analyzing the daily transactions, several types of associations were noticed in the transacted item sets. The transactional database used in retail marketing has a large number of itemsets that are frequently purchased exclusively during specific hours of the day and are not usually considered to be frequently purchased. In some instances, association rule mining at a lower data level with uniform support doesn't reveal any notable patterns, yet it may be suppressing crucial data. For instance, the morning session sees the biggest demand for items like bread and cake, while the afternoon and evening sessions find higher demand for items like drinks and noodles. In the transaction at a supermarket, this study discovered the frequent item and the link among the frequent item sets. In this study, we used the tree-based FP-growth idea to locate the pertinent rules in the transaction based on the time of day, specifically the morning, afternoon, and evening. Our empirical analysis demonstrates that the suggested approach is computationally effective in a different time period of the day, enabling the Bangladeshi retailer Super Shop to make the best decisions and boost sales. © 2023 IEEE.

关键词： Association rules

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FPGA Implementation of Educational RISC- V Processor Suitable for Embedded Applications 3

FPGA Implementation of Educational RISC- V Processor Suitabl...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Banna Saif, Md. Hasanul Sadad, Nahin Ul Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350345360

Learning computer architecture has been a tough task for the learners. Sometimes it is not very effective when an architecture is learnt theoretically. Learning an architecture like RISC-V which has huge industrial interest has an additional benefit. The RISC-V instruction set architecture that is free to use was developed with flexibility and durability in mind. The adaptability of RISC-V enables a wide range of hardware implementations. Interests in Field Programmable Gate Array (FPGA) is growing in recent times due to its feasibility, flexibility and efficiency. In this paper, we implemented an educational processor based on the RISC-V architecture on FPGA with support for interfacing intended for embedded applications. An assembler was also designed and developed which can convert assembly codes into RISC-V standard machine codes that can help the users to operate the CPU easily. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sunanda, H.G. Kavitha, K.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru560004 India Visvesvaraya Technological University Belagavi590018 India Dayananda Sagar College of Engineering Dept. of Computer Science and Engineering Bengaluru560078 India

ISBN: (纸本)9798350372502

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare. © 2024 IEEE.

关键词： Motion estimation

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Sanjana Reddy, Karri Sai Reddy C, Kishor Kumar Doss, Srinath Lippert, Kari Stanley College of Engineering and Technology for Women Dept. of Computer Science and Engineering Hyderabad India Botho University Faculty of Engineering and Technology Botswana University of South Alabama Dept. of Systems Engineering Alabama United States

ISBN: (纸本)9798331540821

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care. © 2024 IEEE.

关键词： Logistic regression

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Mosquito Species Classification through Wingbeat Analysis: A Hybrid Machine Learning Approach

Mosquito Species Classification through Wingbeat Analysis: A...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rony, Jahid Hasan Karim, Nazmul Rouf, Md Abdur Noor, Sabah Binte Siddiqui, Fazlul Hasan Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

The potential of utilizing the features of wingbeat sound to detect different species of flying mosquitoes is explored in this paper. By analyzing the environmental sound using machine learning, it becomes possible to identify as well as classify different species of flying mosquito before it spreads mosquito-borne diseases. To accomplish the identification and classification, this paper presents a hybrid model that combines Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to analyze the audio in order to classify different species. The proposed model shows significant accuracy for detecting and classifying different species of flying mosquitoes as well as mitigate the weakness of individual models. If the hybrid model is widely used, could help to reduce the spread of mosquito-borne diseases and related fatalities. © 2023 IEEE.

关键词： Convolutional neural networks

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Data-Enhanced Prediction with Decomposition and Amplitude-Aware Permutation Entropy in Distributed Computing Systems

Data-Enhanced Prediction with Decomposition and Amplitude-Aw...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Hu, Qinglong Bi, Jing Zhang, Wei Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China City University of Hong Kong Dept. of Computer Science 999077 Hong Kong College of Computer Beijing University of Technology Beijing100124 China University of Connecticut Dept. of Computer Science and Engineering StorrsCT06269 United States Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively. © 2024 IEEE.

关键词： Quality of service

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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