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Preventing Spoofing Threats in IoT: machine learning Approaches for Intrusion Detection

Preventing Spoofing Threats in IoT: Machine Learning Approac...

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Applied Intelligence and Computing (AIC), IEEE World conference on

作者： Anshika Sharma Himanshi Babbar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The safety and reliability of Internet of Things (IoT) networks have to be guaranteed because of the quick spread of IoT devices. Significant risks to IoT deployments come from spoofing attacks, in which malevolent actors pretend to be trustworthy equipment to obtain illicit entry or interfere with normal operations. Utilizing the rich data supplied by an IoT intrusion detection dataset, this paper suggests a machine learning (ML)-based method for spotting spoofing attacks in IoT networks. In particular, investigate how well many ML algorithms such as Random Forests (RF), Naive Bayes (NB), Logistic Regression (LR), and Support Vector machines (SVM) identify spoofing occurrences. Train and assess classifiers using supervised learning techniques on the IoT intrusion detection dataset, which includes various transmission patterns and instances of attack. After extensive testing and analysis, determine how well each algorithm performs in terms of F1-scoring, recall, precision, and detection accuracy. The findings demonstrate how effective ML techniques are in spotting spoofing attacks in IoT environments and highlight the potential of NB, LR, SVM, and RF as helpful tools for enhancing the safety posture of IoT deployments. These results provide useful information to cybersecurity experts and researchers working to lessen the risks of spoofing attacks in the IoT domain, as well as aiding in developing Intrusion Detection Systems (IDS) for IoT environments.

关键词： Support vector machines Radio frequency Accuracy machine learning algorithms Intrusion detection Safety Classification algorithms Internet of Things Tuning Testing

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Measuring the Impact of machine learning on Real-Time data Analysis

Measuring the Impact of Machine Learning on Real-Time Data A...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： A Rengarajan Rohit Kumar Nitin N. Sakhare N. Thangarasu V. Mahalakshmi Ratnmala N. Bhimanpallewar Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India

machine studying is unexpectedly revolutionizing the manner facts is analyzed and utilized in actual-time selections. with the aid of utilising effective algorithms, gadget gaining knowledge of can unlock effective insights from actual-time input statistics greater speedy and as it should be than traditional strategies. however, to make sure its fulfillment and effectiveness, there have to be a quantitative size procedure to assess how device studying algorithms are impacting the effectiveness of actual-time information evaluation. This paper pursuits to provide a comprehensive evaluation of the cutting-edge nation of device learning and its effect on actual-time facts analysis, as well as unique techniques to degree the effectiveness of device learning algorithms. specifically, the paper will observe the use of supervised studying fashions, unsupervised learning fashions, and reinforcement mastering strategies to create efficient and correct fashions that could quickly analyze and produce beneficial insights from real-time statistics. Additionally, this paper will talk various techniques that can be used to quantify the effectiveness of device gaining knowledge of fashions which includes predictive accuracy, records and function selection, and model interpretability. finally, the paper can even discuss challenges and destiny issues for measuring the authentic effect of machine studying on actual-time records analysis.

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Fault Diagnosis and Predictive Maintenance of Electromechanical Equipment Based on Deep learning and Optimization Algorithm

Fault Diagnosis and Predictive Maintenance of Electromechani...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Tingting Yu Xinhai Xiang Weifang Engineering Vocational College Qingzhou Shandong China School of Rocket army NCO Qingzhou Shandong China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the development of industrial production, mechanical and electrical equipment plays a vital role in various engineering and production fields. However, the failure of electromechanical equipment has brought serious losses and potential safety hazards to production. Therefore, it is of great significance to realize rapid and accurate diagnosis and predictive maintenance of mechanical and electrical equipment faults. Aiming at this problem, this paper puts forward a method of fault diagnosis and predictive maintenance of electromechanical equipment based on deep learning and optimization algorithm. Firstly, a convolutional neural network (CNN) model suitable for fault diagnosis and prediction is constructed by using deep learning technology, which is used to automatically extract features from sensor data for fault diagnosis and prediction. Secondly, the optimization algorithm is introduced to optimize the parameters of the model and the training process to improve the performance and generalization ability of the model. Through a large number of experiments on actual engineering data sets, the results show that the proposed method has achieved remarkable performance improvement in the task of fault diagnosis and predictive maintenance of electromechanical equipment, and has good accuracy and generalization ability, which is of great significance for improving production efficiency and ensuring equipment safety. Therefore, the method based on deep learning and optimization algorithm proposed in this paper has broad application prospects and practical value, and has a positive role in promoting further research and application in related fields.

关键词： Deep learning Fault diagnosis Training Accuracy Production Prediction algorithms data models Robustness Optimization Predictive maintenance

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Melanoma Detection with Transfer learning

Melanoma Detection with Transfer Learning

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Pragadeshwar Vishnu S Krishvadana C Rani Rajeev Khoodeeram Ahmed Bouridane M Rajesh Kumar School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Emerging Technologies Université Des Mascareignes Mauritius Centre for Data Analytics University of Sharjah Sharjah UAE

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The abnormal growth of skin cells that are exposed to the sun is identified as skin cancer. Even though skin cancer is curable, late diagnosis and improper treatment can lead to severe effects and death. Melanoma has been identified as the deadliest type of skin cancer and it is responsible for $85 \%$ of skin cancer deaths. Even though research on melanoma identification is prominent, still it has not been perfected. Automated Melanoma recognition is a challenging task due to various artifacts such as low-contrast dermoscopic images, and large and similar varieties of other skin cancers. In this study, we take on this challenge using neural networks. We implement an approach in which two trained neural networks are combined to give a higher accuracy output. With the lack of training data for classification, we employ data augmentation techniques. The proposed Model makes use of two types of learning approaches by combining their weights and predicting the output. The combined or ensemble model categorizes the output into 6 different categories with an accuracy of up to $89 \%$. The approach is simple to implement and surpasses other avant-garde techniques in terms of accuracy.

关键词： Accuracy Neural networks Transfer learning Training data Melanoma learning (artificial intelligence) Predictive models

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Exploring the Effectiveness of BERT for Sentiment Analysis on Large-Scale Social Media data

Exploring the Effectiveness of BERT for Sentiment Analysis o...

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Intelligent Technologies (CONIT), international conference on

作者： Thulasi Bikku Jyothi Jarugula Lavanya Kongala Navya Deepthi Tummala Naga Vardhani Donthiboina Computer Science and Engineering NRI Institute of Technology Visadala Guntur India Computer Science and Engineering NIT Mizoram India Computer Science and Engineering K L Deemed to be University Vaddeswaram India Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

Sentiment analysis is a crucial task in the field of natural language processing (NLP) and has gained significant attention due to the widespread use of social media platforms. Social media data presents unique challenges for sentiment analysis due to its unstructured nature, informal language, and abundance of noise and irrelevant information. To tackle these challenges, advanced techniques such as BERT have emerged as powerful tools for sentiment analysis. In our study, we aim to explore the effectiveness of BERT specifically for sentiment analysis on large-scale social media data. BERT is a state-of-the-art language model that has demonstrated impressive performance on various NLP tasks by capturing contextual information from both left and right contexts of a given word. By leveraging the pre-training and fine-tuning capabilities of BERT, we investigate its potential for sentiment analysis in the context of social media. To establish a comprehensive evaluation, we compare the performance of BERT with traditional machine learning algorithms commonly used for sentiment analysis. Our experimental results indicate that BERT surpasses the performance of traditional machine learning algorithms, achieving state-of-the-art results in sentiment analysis on the social media dataset. BERT's ability to capture intricate contextual information and understand the subtleties of social media language contributes to its superior performance. The model demonstrates exceptional accuracy, precision, recall, and F1-score, showcasing its effectiveness in classifying sentiment labels accurately.

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Earthquake Prediction by Generating and Detecting Seismic Wave

Earthquake Prediction by Generating and Detecting Seismic Wa...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Angel Joy K M Mysa Anlin P Paul Joyson Johnson Binet Rose Siji Joseph Department of Electronics and Communication Engineering Sahrdaya College of Engineering and Technology Kodakara Kerala India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This project aims to develop a real-time earthquake detection and classification system using a combination of hardware, machine learning, cloud computing, and mobile application development. The system utilizes an MPU6050 accelerometer and vibration sensor to continuously monitor seismic activity, with data transmission handled by an ESP8266 WiFi module to store readings in Firebase for further analysis. The collected data is labeled under various conditions, namely normal, vibration, and earthquake, to train a Random Forest classifier that could distinguish between the seismic states effectively. The model is then used to classify the real-time inputs from the sensor, thus correctly detecting the events of earthquakes. To make the system more user-friendly, a Flutter-based mobile application is developed for real-time visualization of sensor data and state classification. The system sends out instant voice notifications and phone vibrations whenever it detects an earthquake-level vibration. The model was tested using different levels of vibration by simulating various seismic conditions, and the results proved that it was reliable in the detection of earthquakes. While the current implementation performs well, future enhancements could include integrating additional sensors, refining the classification model, and optimizing real-time data processing for increased accuracy. This project presents a scalable and practical earthquake detection system, leveraging sensor technology, machine learning, and mobile applications to provide an efficient early warning mechanism for earthquake-prone regions.

关键词： Vibrations Accelerometers Cloud computing Earthquakes Refining Real-time systems data models Mobile applications Reliability data communication

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Question-Aware Deep learning Model for Arabic machine Reading Comprehension 3rd

Question-Aware Deep Learning Model for Arabic Machine Readi...

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3rd international conference on Intelligent Systems and Pattern Recognition, ISPR 2023

作者： Al-Harbi, Marwa Obeidat, Rasha Al-Ayyoub, Mahmoud Alawneh, Luay Department of Computer Science Jordan University of Science Irbid Jordan Department of Information Technology Ajman University Ajman United Arab Emirates Department of Software Engineering Jordan University of Science Irbid Jordan

ISBN: (纸本)9783031463372

machine reading comprehension is one of the most long-standing challenges in artificial intelligence. It is a subtask question-answering system that aims to find a text span in a reading context that answers a question. It is the core of several applications, such as customer service systems and chatbots. However, However, few studies have targeted machine reading comprehension of Arabic text. Besides, The existing models generate a context representation independently from the question. As an analogy to some people’s comprehension styles who believe that by reading the question, they gain a better understanding of the reading passage, we present a question-aware neural machine reading comprehension model for Arabic. Our model extracts a representation of the context by incorporating the question using several bidirectional attention units to achieve various levels of question-centered context understanding. Our experimental evaluation shows that our model outperforms strong machine reading comprehension baselines, including DrQA and QaNET models applied to Arabic, by a significant margin with an F1 score of 59.6%. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Trends Matching as a dataset Attack Detection Strategy during machine learning 3

Trends Matching as a Dataset Attack Detection Strategy durin...

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3rd international Multidisciplinary Information Technology and engineering conference, IMITEC 2021

作者： Ramalivhana, Dakalo David Chibaya, Colin Madzima, Kudakwashe Sol Plaatje University Dept of Computer Science and Info Tech. Kimberley South Africa

ISBN: (纸本)9781665417495

machine learning systems rely on the validity and integrity of the datasets provided to make correct predictions. However, datasets attacks are real. datasets can be poisoned, spoiled, corrupted, or falsified to mislead machine learning system. Strategies for preventing datasets poisoning are, therefore, apparently needed. Models to defend datasets are scarce. In our context, datasets poisoning is about addition of malicious data into credible datasets during machine learning. We propose a strategy for preventing addition of malicious data into accepted datasets by verifying and matching the trends depicted in credible datasets to the patterns arising from the additional data. Precisely, we investigate the strength of association between the trends depicted in the two datasets before a recommendation to accept or reject the additional data into a machine learning process is made. The age detection datasets, gender classification datasets and uber pickups datasets were used for illustration purposes. These datasets are all freely available online on Kaggle. The age detection dataset was the selected credible dataset. The uber pickups was used as the malicious dataset, while the gender classification dataset was used as the valid additional data. An experimental assessment of the validity of the proposed trends matching strategy indicates that only those datasets with matching trends can be accepted as additional data into credible datasets. Malicious datasets are blocked from poisoning credible datasets. This intervention implicitly supports high quality and accurate machine learning. © 2021 IEEE.

关键词： machine learning

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A data-Driven Approach for Predicting Vehicle Component Failures

A Data-Driven Approach for Predicting Vehicle Component Fail...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Priyanka Niranjan Savadekar G. Giridhareswar A. Prudhvi Reddy R. Rukmananda Reddy Presidency university Yelahanka Bengaluru Karnataka India Department of CSE SoE Presidency university Yelahanka Bengaluru Karnataka India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The automotive industry has faced several issues with respect to sudden failures of vehicle components, which results in reduced efficiency of the vehicles, breakdowns, and increased maintenance costs. To this end, this research suggests a new approach to predictive maintenance based on machine learning to predict the failures of critical vehicle components. The proposed system is developed using historical data containing operational parameters like engine RPM, lubricant oil pressure, coolant temperature, and fuel pressure to develop prediction models. The analysis of the correlation between these parameters and their impact on the component was made in detail. Several machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector machine (SVM), and K-Nearest Neighbours (KNN), were also compared. Among the above mentioned, the best result was achieved by Logistic Regression with the accuracy of 66%, and the second best result was obtained by using SVM with the accuracy of 92 % . The predictive framework is also supported by a web-based dashboard that gives the user real time information on the state of the vehicle, the probability of failure and sending of maintenance reminders at the right time. This dashboard helps the fleet managers in making informed decisions as it provides suggestions based on the output of the model. Thus, the developed system brings down the occurrence of unplanned downtime, improves the maintenance schedule, and increases the dependability of vehicles and thus can have been an effectively large used number in of industries vehicles. that the use of machine learning in predictive maintenance is also introduced and the way for future also development suggested. such This as scalable real and time adaptable IoT, solution advanced is deep a learning valuable and contribution edge to computing the for growing failure field prediction of is automotive predictive maintenance and the potential to revolutio

关键词： Support vector machines Accuracy Scalability Predictive models Real-time systems data models Maintenance Internet of Things Predictive maintenance Automotive engineering

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Critically Analyzing the Nature of Deep learning, The Way, It is Adopted in Organizational Developments

Critically Analyzing the Nature of Deep Learning, The Way, I...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Melanie Lourens Somanchi Hari Krishna R. B. Dhumale Sonakshi Bhatia Kapil Joshi Vikas Tripathi Department of Human Resources Management Durban University of Technology Berea Durban South Africa Department of Business Management Vignana Bharathi Institute of Technology Aushapur Village Ghatkesar Mandal Malkangiri Medchal Dist. Hyderabad Telangana India AISSMS Institute of Information Technology Pune Maharashtra India Department of Management Uttaranchal University Dehradun Uttarakhand Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The ultimate focus of this study is to assess deep teaching approach and how its benefits business outcomes with a focus on the projects' economic value as deep learning-based transformations. The actions required to finish this research were as follows: Following are the aspects of reinforcement learning: learning based: What exactly is it? What happens when it works properly? How methods have been employed? What are the drawbacks and issues? What impact may machine learning has upon your organization's performance? vii) Deep learning instances; and (vi) paying close attention neural network. In order to understand how DL business value affects organizational performance, our study has made use of the concept of information technology capabilities. It encompasses a broad spectrum of technological advancements, including algorithms that evolve on their own and chat bots and language processing. The idea of the operation of replying to or agitating their world while contemporaneously increasing and enhancing their strategic and commercial advantages has been the driving force behind the deployment of ai and machine learning technological breakthroughs by enterprises. Due to its ability to detect, anticipate, and interact with people, DL surpasses the adversary when it comes towards improving the effectiveness of current processes and boosting the impact of automation, data, and revolutionary advances.

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