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machine learning Approaches to Securing IoT Networks: A Comprehensive Review of Recent Advances

Machine Learning Approaches to Securing IoT Networks: A Comp...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), international conference on

作者： Harnit Saini Sanjeev Kumar Prasad School of Computer Science and Engineering Galgotias University Greater Noida India Computer Science and Engineering Department Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

The IoT is a network of physical objects embedded with sensors and software, connecting and exchanging data over the internet. With over 7 billion connected devices, experts predict growth to 10 billion by 2020 and 22 billion by 2025. machine learning ML technologies are advancing quickly, with new algorithms and approaches emerging regularly. In this paper, a review is carried out that helps provide insights into their effectiveness in the current landscape. Different ML techniques are categorized and evaluated based on how they address diverse challenges. Reviewing current ML techniques helps in understanding their strengths and limitations in combating novel attack vectors. Later on, a comparative analysis of these techniques is conducted based on performance metrics. A comprehensive review identifies gaps in current research and practice, highlighting areas where ML techniques might need further development or improvement.

关键词： Support vector machines machine learning algorithms Reviews software algorithms Vectors software Sensors Internet of Things Cyberattack Random forests

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Development of Tongue Image-Based Diagnostic System Using Deep Convolutional Fused Features and machine learning

Development of Tongue Image-Based Diagnostic System Using De...

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Emerging Trends in engineering and Medical Sciences (ICETEMS), international conference on

作者： Dushyant V Mankar P.S. Choudhary Department of EXTC Prof. Meghe College of Engineering & Management Amravati Maharashtra

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Tongue diagnosis is in vital importance in Indian Ayurvedic medicine to monitor human health. Tongue image analysis is the examination of tongue images to identify different abnormalities in individuals. Tongue image analysis is a long-standing, traditional, noninvasive diagnostic technique widely used by conventional medical practitioners to diagnose pathologies. Several machine learning algorithms are presented to classify pathologies by analyzing tongue pictures. Yet, their classification capacity is impeded by the existence of variations in tongue appearance. We have developed a sophisticated approach for identifying tongue disorders that uses a deep-learning convolutional neural network to forecast diseases by analyzing tongue photos with exceptional speed and precision. In addition, the model utilizes an ensemble machine learning (ML) classifier to perform classification tasks. A simulation and study of the performance of the proposed system is conducted using the MA TLAB software.

关键词： Training Pathology Tongue Accuracy Transfer learning Mouth software Convolutional neural networks Medical diagnostic imaging Image classification

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Research on Data-Driven Decision Making for Multi-Objective Optimisation Based on Genetic Algorithm and ARIMA

Research on Data-Driven Decision Making for Multi-Objective ...

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Electrical, Automation and Computer engineering (ICEACE), international conference on

作者： Yingqi Li Qingdao University of Technology Qingdao China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study is based on a multi-objective optimisation model and uses various machine learning algorithms for the optimisation analysis of complex systems. Firstly, a bi-objective optimisation model was constructed using Excel and MATLAB to analyse the data for trend analysis and category correlation calculations and incorporating the cost plus rate. Genetic algorithm was used to solve the model to find the optimal solution and ARIMA model was used for future replenishment prediction. The LINGO software was further used to achieve multi-objective optimisation under a variety of constraints including maximising revenue, unit profit and minimising replenishment loss rate. The results of the study show that the model exhibits high reliability and effectiveness in data-driven decision making, demonstrating the potential of genetic algorithms with machine learning algorithms such as ARIMA for a wide range of applications in multi-objective optimisation.

关键词： machine learning algorithms Optimization models Decision making Predictive models Market research software MATLAB Complex systems Optimization Genetic algorithms

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A Comprehensive Review of SDP Using machine and Deep learning Techniques in software Testing

A Comprehensive Review of SDP Using Machine and Deep Learnin...

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Computing and Data Analytics (ICCDA), international conference on

作者： Suguna Angamuthu K.G. Maheswari Department of Information Technology Nandha Engineering College Department of Information Technology Government College of Engineering Tamil Nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

software Defect Prediction (SDP) is essential to ensure the high quality and dependability of the software Testing process in the software Development Life Cycle (SDLC). Early identification of defects plays a significant role in minimizing the efforts, resources and time. Conventional manual testing techniques have become insufficient due to the increasing intricacy of software programs and the demand for automated techniques. To evaluate vast amounts of data and find defect patterns, Deep learning (DL) and machine learning (ML) techniques have emerged as promising solutions for accurate SDP. A comprehensive analysis of recent developments in ML and DL techniques for SDP is provided in this review paper. The reviewed SDP methodologies, their advantages and limitations, along with their comparative performance, are discussed. The recent research trends, emerging methods, and promising paths for future research in this field are also identified in this domain. This paper aims to be a beneficial source for researchers to understand the current state of SDP and how to use ML, DL, and ensemble approaches to improve software quality assurance practices for effective software testing performance in SDLC.

关键词： software testing Deep learning Surveys Support vector machines Accuracy Reviews Stacking Predictive models software Ensemble learning

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Ensemble of Deep learning Models and machine learning Classifiers for the Classification of Brain Tumors

Ensemble of Deep Learning Models and Machine Learning Classi...

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Computer, Communication and Control (IC4), international conference on

作者： K Bhagyalaxmi B. Dwarakanath P. VijayaPal Reddy Department of Computer Science and Engineering School of Computing SRM IST Kattankulathur Tamilnadu India IT Department SRM IST Ramapuram Tamilnadu India CSE Department Matrusri EngineeringCollege Hyderabad India

Brain tumors are typically one of the more complex health issues in the world. It could result in death if not addressed in the beginning stages. It is important to adopt an automated system to diagnose tumors. Here this article proposes a model to automate the system. The proposed model unites two separate Convolutional Neural Network (CNN) architectures, VGG16 and ResNet-50, to improve the accuracy and robustness of brain tumor detection. The model is trained and evaluated on a comprehensive dataset of brain tumors for encompassing three distinct tumor types: pituitary, meningiomas, and glioma tumors. The proposed model uses MRI images for preprocessing and feature extraction through pre-trained CNNs. Various machine learning classifiers like SVM, KNN, LR, and RF are applied. Finally, an ensemble of classifiers, including LR, RF, and XGBoost, is used for tumor detection and classification. The model is evaluated on brain tumor dataset which is publicly available. The output demonstrates that the ensemble approach outperforms the individual classifiers, achieving an overall accuracy of 93.43%. The primary aim is to equip doctors with high-quality software for tumor identification and understanding their origins. This software is designed to save patients’ time, offer timely solutions during the early stages, and enable swift consultations.

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UUT: Unveiling Unknown Threats with machine learning for Exploit Detection

UUT: Unveiling Unknown Threats with Machine Learning for Exp...

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

作者： Aadil Khan Ishu Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In cybersecurity, attack detection indicates finding and countering efforts to take advantage of weak spots in computer systems using cutting edge technologies like AI and machine learning. Exploits are software or procedures that exploit vulnerabilities in computer systems, networks, or applications to compromise security. It's a tool or method used by attackers to exploit system flaws. software defects, configuration mistakes, and design weaknesses may be exploited. A successful exploit may provide unauthorized access, data alteration, or malicious code execution. AI quickly looks at how networks work, finds strange behavior, and spots possible security holes before they get worse. This proactive strategy enables firms to react to and decrease cyber hazards promptly, reducing their consequences and improving overall network security. Early cybersecurity vulnerability discovery is essential to minimize harm. Early exploit detection permits quick reaction, reducing cyberattack damage. Early detection helps firms fix vulnerabilities, protect sensitive data, and strengthen defenses, minimizing the chance of breaches and improving cybersecurity resilience. By trying out different machine learning methods for attack identification, this research article gives useful information. According to the results, the decision tree and XGBoost algorithms are the best at finding and reducing risks. Through its review and suggestions of strong attack detection methods, this research study helps to improve cybersecurity practices.

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Deep learning Models for software Defect Classification

Deep Learning Models for Software Defect Classification

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Kalpana Singh Alankrita Aggarwal Rattan Deep Aneja Rajender Kumar Punit Soni Apex Institute of Technology (CSE) Chandigarh University Mohali India Computer Science and Engineering Chitkara University Rajpura Punjab India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

With the increasing reliance on automatic software-based applications, it is critical to automate software defect classification and assure software dependability. This article proposes an expert-based automatic software flaw classification system. Defect report categorization is the process of categorizing defects into predefined groups. Many machine learning (ML) algorithms have recently been presented to categorize defect in various classes. This research proposes a classification model based on the Long Short Term Memory (LSTM) network and Convolution Neural Network (CNN) that uses these deep learning (DL) models to categorize defect reports by generating new word embedding from the defect reports. In terms of recall, accuracy and precision, the results are compared to pre-trained word embedding using Google's word2vec. According to the experimental results, LSTM beats the other models utilized in the study. On the redmine dataset, LSTM achieves a maximum accuracy of 70%.

关键词： Deep learning Performance evaluation machine learning algorithms Convolution Computational modeling Neural networks software algorithms

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Sentiment Analysis of Covid-19 Twitter Data using Deep learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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software Bug Report Detection Methods Based on machine learning Techniques

Software Bug Report Detection Methods Based on Machine Learn...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Ulligaddala Srinivasarao Akhil Chaganti Ajay Mannam Dhanush Kotrikeline Hemanth Katta Department of Computer Science and Engineering GITAM (Deemed to be) University Hyderabad India Department of Computer Science and Engineering and Hyderabad India GITAM (Deemed to be) University Hyderabad India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

software defect prediction, an integral facet of software engineering, proactively identifies and resolves potential flaws before they escalate into disruptive issues during production. Insights gleaned from dedicated research serve as solid support for development teams, pinpointing vulnerable areas within their codebase and optimizing resource allocation. This strategic approach significantly reduces overall expenditure on defect rectification. This paper conducts a comprehensive study, meticulously examining various machine learning algorithms in this domain. It intricately dissects deployment, functionality, and the comparative effectiveness of models like Logistic Regression, K-Nearest Neighbours, Naive-Bayes Classifier, Random Forest Classifier, and Recurrent Neural Networks. This thorough exploration enhances understanding of defect prediction methodologies. It provides a detailed roadmap for optimal strategies, reinforcing software quality measures and mitigating the impact of potential coding irregularities on system integrity and reliability. According to the experimental findings, RNN outperformed the baseline methods in terms of accuracy, F1 score, recall, and precision when identifying software bug reports.

关键词： Logistic regression Recurrent neural networks machine learning algorithms Computer bugs Prediction algorithms Approximation algorithms Solids

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection Systems Considering Domain-Specific Constraints 1

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25th international conference on Information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (数字)9789819616244

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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