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Implementation of A Deep learning Framework for intelligent Intrusion Detection in Internet of Things Networks 5

Implementation of A Deep Learning Framework for Intelligent ...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Jeyapriyanga, S. Ravi, C.N. Rathiya, R. Kalaivani, K. Rama Devi, C. Kumar, Kallakunta Ravi St.Joseph's Institute of Technology Department of Information Technology Chennai India Cmr Engineering College Computer Science and Engineering Department Telangana Hyderabad India Information Technology Dr. Ngp Institute of Technology Coimbatore India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India St.Joseph's College of Engineering Electrical and Electronics Engineering India Koneru Lakshmaiah Education Foundation Department of Ece AP Vaddeswaram India

ISBN: (纸本)9798350321425

Internet of Things (IoT) has ushered in a new age that benefits humanity greatly. From automated healthcare to energy and transportation, the Internet of Things covers many subject topics. IoT devices are vulnerable to several cyberattacks due to their limited resources. As data output has expanded exponentially from a zettabyte to a trillion per year, computer and IoT network expansion has surged. Network growth has created new security issues. In such large data sets, intrusions are hard to spot. Today's networks are used for 'smart' dwellings and cities, grids, devices, objects, online commerce, banking, government, etc. Due to internet data privacy and security concerns, Intrusion Detection Systems (IDS) have proliferated. Privacy, security, and resilience would suffer if IDS protection fails. Traditional defences can't handle modern attacks. Cutting-edge deep learning methods may automatically detect intrusions and network anomalies. This study's main goal is to evaluate intrusion detection using deep learning algorithms and compare the outcomes to other systems. This research work has proposed a novel deep-learning-based IoT invasion detection method. The cutting-edge Internet of Things dataset contains IoT traces and real-world attack traffic including DoS, DDoS, data harvesting, and theft attempts. Finally, all publicly available network-based IDS datasets are analyzed in this research study. The accuracy, false alarm rate, recall, precision, f-1 score, and detection rate of many deep learning IDS algorithms have been examined. Furthermore, this research study has investigated the network security and privacy issues and solutions. © 2023 IEEE.

关键词： Internet of things

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7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

7th International Scientific Conference on Intelligent Infor...

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7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

ISBN: (纸本)9783031437885

The proceedings contain 71 papers. The special focus in this conference is on international Scientific conference on intelligent Information Technologies for Industry. The topics include: About Explainable Machine learning Models for Anomaly Detection in Cyber-Physical Systems;intelligent Graph-Based Correlation of Security Events in Cyber-Physical Systems;classifier-Based Combined Measure of Syllable Pronunciation Similarity in Speech Rehabilitation;application of Computer Vision Technologies to Reduce Injuries in the Athletes’ Training;architecture of a Hybrid Clinical Decision Support System;Synthesis of PLL in Capture Mode with a Fuzzy Controller of Quasi-Optimal structure Based on the Maximum Condition of the Generalized Power Function;automated Discovery of Direct Speech to Increase Quality and Battery Saving in Telephony;temporal Prediction Models for Technological Processes Based on Predictive Analytics;distributed Control System of Protection of Autonomous Object, Based on Hybrid Technology of Artificial Intelligence;algebraic Bayesian Networks: The Generation of the Network Canonical Representation;using Semantic Annotation of Tabular data for Domain Knowledge Graph Population;robust Filtering of Nonlinear stochastic Processes in Machine learning Systems;selecting a Machine learning Model to Optimize the Burner Digital Twin;modeling of Intuition in Human-Machine Decision-Making Complexes in the Management of Transport Systems;intelligent Maintenance and Repair on Railway Transport;traction Induction Motor state Observer Based on an Luenberger Filter;intelligent Monitoring of Transportation Processes Based on Dynamic Fuzzy PCA Analysis;method for Determining and Fixing the Information Need of Users in the Low-Code Development;intelligent Design of Images of engineering Networks for Spatial Analysis;method of Dynamic Multi-Criteria Quality Assessment and Optimization for Proactive Complex Objects Functioning Schedules.

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7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

7th International Scientific Conference on Intelligent Infor...

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7th international Scientific conference on intelligent Information Technologies for Industry, IITI 2023

ISBN: (纸本)9783031437915

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21st Century engineering learning and Teaching: Malaysia Perspective and Direction 129

21st Century Engineering Learning and Teaching: Malaysia Per...

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129th ASEE Annual conference and Exposition: Excellence Through Diversity, ASEE 2022

作者： Hassan, Syed Ahmad Helmi Syed Mohd-Yusof, Khairiyah Yuan, Chin Chia Chia Phang, Fatin Aliah Rahman, Nor Farahwahidah Abdul Samah, Narina A. Zakaria, Zaki Yamani binti Ghazali, Nurzal Effiyana Center for Engineering Education Universiti Teknologi Malaysia Malaysia

engineering education in Malaysia has been recognized by the Washington Accord (WA) since 2009. The international engineering Alliance continuously updates the requirements for the Program learning Outcomes according to the needs of the current and future global graduating engineers that member countries of the WA must fulfill. Recent requirements emphasize lifelong learning, complex problem solving, engineering activities, and computing and digital tools capabilities geared towards developing engineering graduates ready for 21st century challenges. When brought upon engineering educators, these increasing requirements posed a dilemma because most are not trained to educate students beyond what they have experienced in their previous formal learning processes, which may be irrelevant for today's learners. In a survey conducted by the UK Royal Academy of engineering, engineering knowledge and skills are the topmost needed challenges to meet the needs of the 4th Industrial Revolution. Thus, to transform engineering education in Malaysia, the gap between the needs of the current and future engineering requirements, with the current engineering education practices, must be determined. Hence, this study aims to identify the gaps in Malaysian engineering education towards preparing 21st century ready educators, study the transition of engineering educators in their attempt to implement innovative education, and finally develop a framework for transforming engineering education through the infusion of innovative teaching and learning to support the development of the future-ready educators. The study uses a mixed-method research design to identify the gaps through document analysis, interviews, questionnaire surveys, and focus group discussions (FGD) among engineering education providers and industry players. The data will be analyzed and triangulated to chart the gaps in engineering education toward preparing 21st century educators. Later, this result will be used to deve

关键词： engineering education

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Semi-asynchronous Federated learning Optimized for NON-IID data Communication based on Tensor Decomposition 21

Semi-asynchronous Federated Learning Optimized for NON-IID D...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wu, Qidong Fu, Xuemei Yang, Xiangli Zhao, Ruonan Wu, Chengqian Zhang, Tinghua Hainan University School of Computer Science and Technology Haikou China Hainan University School of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350329223

Federated learning (FL) is a new distributed machine learning framework that enables reliable collaborative training without collecting users' private data, it can successfully address the issue of data silos. However, FL has had trouble scaling to statistically varied data and large-scale models because of its frequent communication and average aggregation procedures. In this paper, we propose a Semi-asynchronous stochastic Controlled Averaging based on Tensor Decomposition for Federated learning (SATD-SCAFFOLD), in which we perform tensor decomposition on the client model to reduce communication costs. In addition, we design a semi-asynchronous aggregation approach for the server, which can prevent server from wasting too much time waiting for data from late clients in the case of an unstable network, and the overall model still has excellent performance. Thorough experiments demonstrate that our proposed SATDSCAFFOLD algorithm can reduce both the communication cost and convergence time while maintaining good performance. © 2023 IEEE.

关键词： stochastic systems

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Label Semantics-Filtered Enhanced Prototypical Network for Few-Shot Named Entity Recognition 21

Label Semantics-Filtered Enhanced Prototypical Network for F...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wu, Xucheng Lian, Jia Gao, Hui Wang, Youbo School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan College of Architectural Technology Chengdu China

ISBN: (纸本)9798331519254

In the task of Named Entity Recognition (NER), few-shot learning has garnered significant attention due to its potential in scenarios with limited labeled data. Existing research often relies on label names to enhance semantic representation, but the information provided by label names is limited, which affects classification performance. To address this issue, this paper proposes a Label Semantics-filtered Enhanced Prototypical Network (LSE-PN), which integrates rich label annotation information to improve the model's knowledge representation. The model employs a BERT encoder to extract semantic features from both text and labels, and optimizes category differentiation through a semantic fusion module. The prototypical network learns intermediate representations from the support set to achieve category clustering, with the model matching the nearest labels and prototype centers based on distance metrics. Experimental results indicate that LSE-PN outperforms existing methods across multiple few-shot NER benchmark tests. Our model is highly effective in cross-domain scenarios. © 2024 IEEE.

关键词： Zero-shot learning

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Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation 21

Transferring Indoor Corrosion Image Assessment Models to Out...

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21st IEEE international conference on Machine learning and Applications (IEEE ICMLA)

作者： Josselyn, Nicholas Yin, Biao Considine, Thomas Kelley, John Rinderspacher, Berend Jensen, Robert Snyder, James Zhang, Ziming Rundensteiner, Elke Worcester Polytech Inst Dept Data Sci Worcester MA 01609 USA Worcester Polytech Inst Dept Comp Sci Worcester MA 01609 USA Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA DEVCOM Army Res Lab ARD Aberdeen Proving Ground MD USA DEVCOM Army Res Lab ARD Northeast Reg Extended Site Burlington MA USA DEVCOM Army Res Lab ARD Adelphi MD USA

ISBN: (纸本)9781665462839

Corrosion of materials impacts critical economic sectors from infrastructure, transportation, defense, health, to the environment. The development of safe anti-corrosive materials is thus an important area of study in materials science. Corrosion science of preparing materials and then monitoring their corrosion under adverse conditions is labor intensive, time consuming, and extremely costly. While deep learning has become popular in automating various engineering tasks, the development of deep models for corrosion assessment is lacking. We are the first to study deep domain adaptation (DA) models for the automated assessment of the corrosion status of anti-corrosive materials. Corrosion data, i.e., photographic images of treated corroding materials, is abundant when produced in artificially controlled laboratory settings, while corrosion image data sets from rich natural outdoor environments are more challenging to produce and thus much smaller. We leverage the more readily available indoor corrosion data to train a classifier and then transfer it via deep domain adaptation to also perform well on the small yet more realistic outdoor corrosion image data set - without requiring target labels. We empirically compare 5 popular domain adaptation models on real-world corrosion image data sets. Our study finds that DA achieves 27% improvement in test accuracy compared to the performance of the no-DA baseline for classifying real-world outdoor corrosion data.

关键词： Materials Science Domain Adaptation

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LstM-AE for Anomaly Detection on Multivariate Telemetry data 21

LSTM-AE for Anomaly Detection on Multivariate Telemetry Data

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21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Abdennebi, Anes Tuncay, Alp Yilmaz, Cemal Koyuncu, Anil Gungor, Oktay Sabanci University Computer Science and Engineering Istanbul Turkey Koçfinans Koçfinans Istanbul Turkey

ISBN: (纸本)9798350345889

Organizations and companies that collect data generated by sales, transactions, client/server communications, IoT nodes, devices, engines, or any other data generating/exchanging source, need to analyze this data to reveal insights about the running activities on their systems. Since streaming data has multivariate variables bearing dependencies among each other that extend temporally (to previous time steps).Long-Short Term Memory (LstM) is a variant of the Recurrent Neural Networks capable of learning long-Term dependencies using previous timesteps of sequence-shape data. The LstM model is a valid option to apply to our data for offline anomaly detection and help foresee future system incidents. Anything that negatively affects the system and the services provided via this system is considered an ***, the raw input data might be noisy and improper for the model, leading to misleading predictions. A wiser choice is to use an LstM Autoencoder (LstM-AE) specialized for extracting meaningful features of the examined data and looking back several steps to preserve temporal *** our work, we developed two LstM-AE models. We evaluated them in an industrial setup at Koçfinans (a finance company operating in Turkey), where they have a distributed system of several nodes running dozens of microservices. The outcome of this study shows that our trained LstM-AE models succeeded in identifying the atypical behavior of offline data with high accuracies. Furthermore, after deploying the models, we identified the system failing at the exact times for the previous two reported failures. While after deployment, it launched cautions preceding the actual failure by a week, proving efficiency on online data. Our models achieved 99.7% accuracy and 89.1% as F1-score. Moreover, it shows potential in finding the proper LstM-AE model architecture when time series data with temporal dependency property is fed to the model. © 2023 IEEE.

关键词： Long short-term memory

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Buddy App: Virtual Assistant for Old Aged and Visually Challenged People 21

Buddy App: Virtual Assistant for Old Aged and Visually Chall...

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21st OITS international conference on Information Technology, OCIT 2023

作者： Supriya, Supriya Singh, Kamakhya Narain Goswami, Biswadev Mukul, Manas Samui, Anupam Misra, Chinmaya KIIT Deemed University School of Computer Applications Bhubaneswar India Dr. B C Roy Engineering College Computer Science and Engineering Durgapur India KIIT Deemed University School of Electronics Engineering Bhubaneswar India

ISBN: (纸本)9798350358230

The modern world has made daily living more intelligent and technologically connected. People who are visually impaired or old aged faces numerous challenges while using the new technologies. In this work, we proposed a virtual voice assistant app called "Buddy App"to help them with their day-today activities. It receives vocal responses as input, speaks the output, and displays the text onto the screen. It receives the voice commands through our microphone and converts it into computer understandable language using speech recognition technology. Then it gives the voice output using text to speech conversion technique. Giving instructions verbally is advantageous for both normal and visual impairments persons who are unable to input data using a keyboard. The main agenda of our research is to improve the lives of visually impaired people and elderly persons who are unable to use modern technology. The proposed device is developed using natural language processing, speech recognition, and text to speech. Users were provided 12 different questions based on various categories like web search, news, music, weather, basic tasks, changing voice and command history that they were supposed to ask Buddy App. The evaluation of the results of the survey was based on user's satisfaction like whether the users are getting desired output to their queries or not. The model is tested on 100 people including 25 visually challenged and 75 normal people with an success rate of 96.25%. © 2023 IEEE.

关键词： Speech recognition

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Ensemble Methods of Machine learning Algorithms for Early Diabetic Detection in Comparison 1

Ensemble Methods of Machine Learning Algorithms for Early Di...

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1st international conference on Circuits, Power, and intelligent Systems, CCPIS 2023

作者： Aurpa, Tanjim Taharat Jeba, Samiha Maisha Rasel, Shoaib Ullah International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350315769

Diabetes can be an issue for people of all ages. Even everyone people among the nine is diabetic positive, which can bring more complications to humans. Furthermore, people in Bangladesh are hesitant to talk about this illness and frequently need clarification. They don't care until their life is in danger. Therefore, we researched diabetes prediction through machine learning so that people can know if they are diabetes positive quickly. The main goal is to contribute to the development of an automated system for diabetic prediction which can predict early diabetes using some basic human parameters. One of the significant parts of Machine learning is Feature Selection techniques. In this work, we intend to focus on this part of the ML paradigm. We analysed the research data and applied feature selection techniques to enhance the performance of ML algorithms. We use PCA, Chi-square test and F_classif as our feature selection technique. Then we use the LGBM Classifier, XGB Classifier, SVC, Random Forest Classifier, and Gradient Boosting Classifier and determine the accuracy to justify our selected techniques. Moreover, We applied a voting classifier and weighted average on these models. Determining the weighted average of ensemble ML methods is a new approach to diabetic prediction. The weighted average outperformed other models with an accuracy of 96.10%. However, the main goal of this research is to provide a comparison scenario of ML methods in diabetic prediction. © 2023 IEEE.

关键词： Forecasting

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