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Attacks Detection in 6G Wireless Networks using Machine Learning 9

Attacks Detection in 6G Wireless Networks using Machine Lear...

引用

9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Saeed, Mamoon M. Saeed, Rashid A. Gaid, Abdulguddoos S. A. Mokhtar, Rania A. Khalifa, Othman O. Ahmed, Zeinab E. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Department of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya International Islamic University Malaysia Electrical and Computer Engineering Dept Malaysia University of Gezira Computer Engineering Dept Sudan

ISBN: (纸本)9798350325218

Unlike the fifth generation (5G), which is well recognized for network cloudification with micro-service-oriented design, the sixth generation (6G) of networks is directly tied to intelligent network orchestration and management. The Attacks Detection in 6G (AD6Gs) wireless networks created by this research uses a Machine Learning (ML) algorithm. The pre-processing stage of the ML-AD6Gs process is the initial step. The second stage involves the feature selection approach. Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. It selects the best subset feature and reduces dimensionality for each independent analyses of the dataset CICDDOS2019. The voting average method is used as an aggregation step, and two classifiers - Random Forest (RF) and Support Vector Machine (SVM) - are modified to be used as ML Algorithms. The proposed method shown an outperformed the existing classification method. The accuracy was 99.9%% for CICDDOS2019 dataset with a false alarm rate of 0.00102 © 2023 IEEE.

关键词： Support vector machines

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IoT Attack Detection Method based on Synthetic Minority Over-Sampling with Random Forest Technique 10

IoT Attack Detection Method based on Synthetic Minority Over...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Tripathi, Neha Mishra, Amit Kumar Bagla, Piyush Pandey, Neeraj Kumar Mittal, Saksham Dept. of Computer Science and Engineering Dehradun India National Institute of Technology Dept. of Computer Science and Engineering Jalandhar India School of Computing Dit University Dehradun India

ISBN: (纸本)9789380544472

The primary objective of this study is to create a self-protection system for the Internet of Things (IoT) that relies on machine learning. One such resampling approach is the proposed IoT Attack Detection Method based on Synthetic Minority Over-sampling with Random Forest (IADMSMO-RF). To identify risks in the IoT ecosystem, we employ IADMSMO-RF, which improves network search computation. Assessment is performed on the Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) dataset to see how effective the trial is in recognizing attacks on the IoT. For the success of the IoT, it is crucial to have a reliable system for detecting and protecting against cyberattacks. Compared to Synthetic Minority Oversampling Technique - Synthetic Minority Oversampling Technique (PPDT -SMOTE), the suggested technique outperforms it in attack detection and categorization. The proposed system has an accuracy of 97.46%, a false alarm rate of 0.2%, and a curve area of 98.76%. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Botanical Bio Vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. This study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. This approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. This research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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A Survey on Eliminating Botnet and Intrusion Attacks through Machine Learning

A Survey on Eliminating Botnet and Intrusion Attacks through...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Faseela, M.H. Manjith, B.C. Dept. Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9798350325188

With the rising linkage and dependence on computer networks, the threat of intrusions and botnets has become a critical concern for organizations and those involved. Malicious actors are in control of unauthorized access or actions that harm the security and integrity of computer systems and insecure devices. The identification of attacks and botnets is of utmost importance in ensuring the security of networks and safeguarding confidential information. In recent years, there has been a growing interest among researchers and organizations in developing unique evaluation methods that can adapt to emerging threats and apply intelligent algorithms such as sparse modelling, transfer learning, distributed computing, or meta-learning capable of detecting, classifying, and predicting previously unknown attack patterns, which can result in machine learning and deep learning models that are more accurate and efficient. This review paper compares the performance of machine learning and deep learning approaches such as Naïve Bayes, Support Vector Machines (SVM), and Logical Regression for intrusion and botnet attacks in various network platforms. This paper provides a comprehensive comparison of machine-learning approaches used in the cyber realm for botnet and intrusion detection and encompasses an analysis of the datasets utilized, the specific cyber issues addressed, and the resultant outcomes. Furthermore, it offers valuable insights into the challenges and advancements within the realm of cybersecurity, thereby facilitating the development of novel approaches and technologies aimed at enhancing digital safety. © 2023 IEEE.

关键词： Botnet & Intrusion detection Cybersecurity Machine learning & Deep learning Techniques

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Efficient Diagnosis of Coronary Artery Disease: A Hybrid Convolutional-XGBoost Approach 2

Efficient Diagnosis of Coronary Artery Disease: A Hybrid Con...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Mishra, Arkashree P. Panigrahi, Suvasini Veer Surendra Sai University of Technology Dept. of Computer Science & Engineering Burla India

ISBN: (纸本)9798350344882

Cardio Vascular Diseases (CVDs) are the prime reason of death globally. Particularly, the cases of coronary artery disease (CAD) have been rising in the post Covid era, especially amongst middle-aged people. Thus, the before time detection of the disease might evade the growing number of fatalities and morbidities. Two most common and accurate methods used to detect CAD are angiography and electrocardiography (ECG). While the former is invasive and a risky procedure, the latter one although non-invasive lacks correctness. Over a decade, non-invasive Machine Learning (ML) as well as Deep Learning (DL) methods have shaped the healthcare scenario by facilitating early diagnosis of the diseases through the application of various computer based algorithmic models over clinical data and images. A hybrid Convolutional- Extreme Gradient Boosting (Convolutional XGBoost) model has been proposed to accurately detect CAD by outperforming some of the existing methods. © 2023 IEEE.

关键词： Diseases

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N-gram Features and Machine Learning Models for Recognizing Human Enzyme Protein Sequences 4

N-gram Features and Machine Learning Models for Recognizing ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mehta, Disha M. Prajapati, Rashmin B. Upadhyay, Keyur N. Valani, Nisha S. Soni, Neha R. Patel, Shrina Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering Gujarat Vasad India

ISBN: (纸本)9798350300093

The identification of enzyme proteins is crucial for understanding the underlying mechanisms of various biological processes. In recent years, the development of machine learning techniques has made it possible to automate this task with high accuracy. In this study, we propose a method that uses 3-gram features and machine learning models for recognizing human enzyme protein sequences. First, the 3-gram features are extracted from the amino acid sequences of enzymes and use these features as input to several machine learning models, including naive bayes, decision tree, random forest, and Extra tree (ET). We compare the performance of these models using metrics such as accuracy, precision, recall, and F1 score. The results obtained show that the ET model performs the best, achieving an accuracy of 96%, precision of 93%, recall of 93%, and F1 score of 93%. Overall, the proposed approach demonstrates the potential of using machine learning models and 3-gram features for accurate identification of human enzyme protein sequences. © 2023 IEEE.

关键词： Decision trees

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CrimeVerse: Exploring Crime Scene through Virtual Reality 6

CrimeVerse: Exploring Crime Scene through Virtual Reality

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Deshmukh, Jay Shetty, Srishti Waingankar, Mrunmayee Mahajan, Gauri Joseph, Richard Dept. of Computer Engineering Vivekanand Education Society's Institute of Technology Mumbai India

ISBN: (纸本)9798350398496

Modern technologies are applied in order to enhance the caliber of crime investigations. The documentation of crime scenes plays a crucial role in the investigation and prosecution of criminal cases. The challenges faced by traditional methods of crime scene documentation such as photographs, sketches, and written notes have their limitations in terms of accuracy, completeness, limited accessibility, and the risk of losing crucial evidence. The precise scene recording offered by 3D data capture techniques is free from the spatial distortion effects present in 2D photographic recordings. This research study explores the use of LIDAR scanning and virtual reality in enhancing crime scene documentation. The paper focuses on evaluating the effectiveness of LIDAR scanning and VR walkthroughs and also focuses on evidence tagging for enhancing crime scene documentation. It also helps in investigating the usability of the VR system for law enforcement professionals. The proposed model faces several challenges during its development, including the high cost and technical complexity of LIDAR scanning, the need for efficient data processing and visualization, and the limited accessibility of the VR system due to hardware and software requirements. Through user testing and analysis, the project demonstrated significant improvements in the accuracy and completeness of crime scene documentation, providing a more detailed and immersive experience for investigators. The VR system was found to be user-friendly and effective in aiding decision-making and communication among law enforcement professionals. Overall, the project provides valuable insights which let you see through the eyes of a witness by documenting their perspective, which aids in the investigation and comprehension of the incident. They can also be used as proof in court. © 2023 IEEE.

关键词： Object detection

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Visual Weather Analytics-Leveraging Image Recognition for Weather Prediction 2

Visual Weather Analytics-Leveraging Image Recognition for We...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Swetha, Kora Kumari, E.N. Vijaya Reddy, V. Adinarayana Gupta, K.Gurnadha Mlr Institute of Technology Computer Science and Engineering India Ece Ksrm College of Engineering Kadapa India Koneru Lakshmaiah Education Foundation Dept of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350325799

Image classification is a procedure that makes an effort to understand an image in its entirety. The objective is to label the picture so that it may be filed away in the appropriate category. Image data that has been collected typically suffers from degradation due to opaque means such as haze, smoke, fog, rain, or snow. De-weathering is the procedure that removes the effects of weathering from an image so that it can be used again. This is done so that similar issues do not arise. As a result, the preprocessing phase is required to concurrently evaluate the quality and other characteristics of an input image, and then additionally execute de-weathering (for example, removals of haze, rain, or snow). These checks and removals must take place in sequence. In this research, an idea for a deep learning-based weather image recognition model is presented. The model takes into account 11 various weather situations, including dew, fog/smog, frost, glazing, hail, lightning, rain, rainbow, rime, snow, and sandstorm. In addition, the paper gives an idea for a deep learning-based weather image recognition model. © 2023 IEEE.

关键词： Weather forecasting

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A Deep Learning based Bangladeshi Vehicle Classification using Fine-Tuned Multi-class Vehicle Image Network (MVINet) Model

A Deep Learning based Bangladeshi Vehicle Classification usi...

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

作者： Ahmed, Tanvir Sharmin Mou, Farzana Samad, Abu Anas Ibn. Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Dhaka Bangladesh University of Birmingham Dept. of Computer Science Birmingham United Kingdom Computer Vision Engineering Lab Sigmind.ai Limited Dhaka Bangladesh

ISBN: (纸本)9798350316001

The classification of vehicles is an important task in many applications, including traffic management, surveillance, and law enforcement. In Bangladesh, vehicle classification has become increasingly challenging due to the rapid growth in the number of vehicles on the roads. In this study, a novel deep learning-based Bangladeshi vehicle classification model using fine-tuned Multi-class Vehicle Image Network (MVINet) based on DenseNet201 with four added layers has been proposed. Our approach uses convolutional neural networks (CNNs) to extract features from images of vehicles. To make our dataset, we employ a YOLO (You Only Look Once) model for initial vehicle detection, followed by manual filtering and augmentation of the dataset to improve classification accuracy. The proposed method demonstrates superior performance compared to manual data collection methods. We evaluated our approach using standard metrics such as accuracy, precision, recall, and F1 score. Our model achieved an accuracy of 97.06%, precision of 97.17%, recall of 97.24%, and F1-score of 97.12% indicating high performance in classifying vehicles. We also compared our approach with state-of-the-art techniques and found that the proposed deep learning-based approach outperformed them significantly. The proposed approach has the potential to improve traffic management and law enforcement in Bangladesh. It can be used to monitor traffic flow, detect traffic violations, and identify stolen vehicles. Overall, this study demonstrates the effectiveness of deep learning in Bangladeshi vehicle detection and classification and highlights the importance of using advanced technologies to address the challenges of a rapidly changing transportation landscape. © 2023 IEEE.

关键词： Deep learning

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Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry 4

Advancing Image Security Through Deep Learning and Cryptogra...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumar, Prajwal Laroia, Ankur Kumar, Mehul Laroia, Avi Upreti, Kamal Parashar, Jyoti Maharshi Dayanand University Computer Science Engineering Haryana India University of Texas at Dallas Ms in Business Analytics TX United States Amity University Computer Science Engineering Uttar Pradesh India Birla Institute of Technology Civil Engineering Pilani India Christ University Dept. of Computer Science Delhi NCR Campus India Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350353266

Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. © 2024 IEEE.

关键词： Electronic health record

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