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共 17201 条记录，以下是221-230 订阅

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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Afroj, Maharin Rifat, Khan Md Sadbin Rahman, Md. Saifur Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures. © 2024 IEEE.

关键词： Denial-of-service attack

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Fabric Defect Detection using Deep Learning 3

Fabric Defect Detection using Deep Learning

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Kailasam, Khushi Singh, Jaswant Gopika, G.S. Sreekrishna, M. Supriya, S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Fabric defect detection plays a crucial role in the textile manufacturing industry's quality control process since it guarantees that customers will receive perfect products. Deep Learning (DL) models have demonstrated a great deal of promise in recent years for accurately identifying fabric defects. The proposed fabric defect identification method combines the best features of two architectures: You Only Look Once (YOLOv4) and Region-based Convolutional Neural Network (R-CNN). While YOLOv4 offers quicker and more effective detection, R-CNN generates region recommendations that allow accurate localization of fabric defects. There are two stages in the proposed system. Initially, region proposals are detected using the R-CNN feature extractor and then a convolutional neural network is used for defect classification. YOLOv4 is used for performing object detection in the second step, where the flaws are concurrently classified and localized. The tests carried out on a fabric defect dataset show that the proposed method outperforms the R-CNN and YOLOv4 models separately. The proposed system is appropriate for real-Time fabric flaw detection applications in a variety of sectors due to its high detection accuracy and quick processing time. This study demonstrates how well R-CNN and YOLOv4 together work for fabric flaw detection, providing hope for future developments in the area. © 2024 IEEE.

关键词： Object detection

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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Machine Learning based Anxiety Detection using Physiological Signals and Context Features 2

Machine Learning based Anxiety Detection using Physiological...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Jain, Arushi Kumar, Rajeev National Institute of Technology Hamirpur Dept. of Computer Science and Engineering Hamirpur India

ISBN: (纸本)9798350371314

Anxiety is a common mental disorder that affects millions of people worldwide. Anxiety can be detected using physiological signals such as heart rate, skin conductance, blood pressure, and respiration. Machine learning is the ability to learn from data and forecast future events. Physiological signals can be analyzed and classified into different anxiety levels using machine learning. This paper aims to present the contribution of context features in detection of anxiety using physiological signals via machine learning. It is an imbalanced multiclassification problem using ECG and EDA signals with context features collected from 15 healthy individuals. These signals of sampling rate 700 Hz collected using RespiBAN from the Wearable Stress and Affect Detection (WESAD) dataset are used here with the 6-STAI questionnaire and the context collected about the subject during the experiment. Scores for each subject are calculated from this questionnaire which categorized subjects into different levels of anxiety. Multiple features are extracted from these signals and scaling is done on the extracted features. With the help of different data balancing techniques and different machine learning algorithms, this study will classify anxiety into three classes, namely, low, moderate and high. Classification of anxiety with fusion of multimodal physiological signals gave better results as compared to single modal physiological signals with an accuracy of 89.8% with ECG signal, 85.9% with EDA signal and 96.7% with ECG and EDA both using Gradient Tree Boosting algorithm with LOOCV after random oversampling. While, the best results are achieved when multimodal physiological signals are fused with context features, it gives the accuracy of 97.3% with Gradient Tree Boosting and LOOCV algorithm. © 2024 IEEE.

关键词： Electrocardiograms

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IoT Enabled Water Flow Management and Monitoring System for Hydroelectric Power Generation

IoT Enabled Water Flow Management and Monitoring System for ...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Shaikat, Abu Salman Tasnim, Rumana Hasan, Jahid Zonayed, Md Hussein, Molla Rashied Shithil, Shaekh Mohammad Al Amin, Abdullah Laurentian University of Sudbury Dept. of Bharti School of Engineering and Computer Science Ontario Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Mechanical Engineering Dhaka Bangladesh University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh University Technology Malaysia Dept. of Mechanical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350369106

Water energy sources play a crucial role, particularly in providing electrical energy through hydroelectric power. Both the water flow rate and the water head determine the potential energy available from water. The hydroelectric system's energy generation directly depends on the water flow rate. A lower flow rate means less water is available to generate energy, even if the head is high. It is essential to increase the potential of the water flow so that it can generate enough electricity. Traditionally, the flow rate is restricted due to the limited capacity of single penstock. This work addresses this issue by introducing multiple penstock-based water flow management systems. Also, conventional water flow monitoring systems often face limitations, like relying on manual measurement, as it only provides periodic data, making it difficult to respond quickly to changes in flow conditions. IoT-based flow sensors leverage IoT technology to monitor water flow and transmit flow data in real-time, enabling operators to detect fluctuating flow conditions and implement timely adjustments to processes, therefore enhancing the overall efficiency. This work aims to design and develop an IoT-enabled prototype hydroelectric power generation system using multiple water flows for enhancing power generation efficiency. The prototype indicates the use of multiple pipes as penstocks in a dam. Additionally, if generating a significant amount of electricity is not required, the water flow can be discontinued, thereby automatically closing each pipeline. This work uses flow sensors, Kitsware software, Arduino IoT Cloud, and an LCD display to generate flow measurement data every ten seconds from multiple pipelines. Moreover, the system automatically generates Excel data and graphically represents multiple water flows, enabling the user to compare the water flow data. The completed work resulted in a hydropower system demonstrating enhanced output and effective compatibility with IoT for

关键词： Penstocks

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Simulator for Swarm Feature Selection Algorithms for Classification Problem 2

Simulator for Swarm Feature Selection Algorithms for Classif...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Kulkarni, Mayuri Diwakar Shahade, Makarand Deore, Shailesh Khandekar, Nilesh Bhavsar, Ishwari Garud, Jayesh Ahirrao, Kunal SVKM's Institute of Technology Dept. of Computer Engineering Dhule India SSVPS's B.S. Deore CoE Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350360523

Swarm Intelligence (SI) algorithms address difficult optimization and decision-making issues using cues from collective behavior. These behaviors are inspired by social creatures like ants, bees, and birds. Using these creatures' behaviors algorithms have demonstrated amazing promise in several fields, such as distributed computing, robotics, and optimization. Assessing SI algorithms' performance under various conditions and scenarios is still a significant difficulty, though. We introduce a new Swarm Intelligence Algorithm Performance Analysis Simulator (SIAPAS) to tackle this issue. This proposed simulator introduces a novel approach to automating the process of selection for Swarm Intelligence (SI) algorithms on datasets. The system empowers users to upload datasets into a specialized simulator, which then orchestrates a comparative analysis using a suite of SI algorithms. The simulator evaluates and selects the most suitable algorithm based on the dataset's characteristics and problem domain. This provides the optimal selected features by the respective algorithms based on measured parameters. © 2024 IEEE.

关键词： Swarm intelligence

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm 3

Network Intrusion Detection System Using Decision Tree and K...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Km Shivani Singh, Khundrapam Johnson National Institute of Technology Dept. of Computer Science and Engineering Manipur Imphal India

ISBN: (纸本)9798350309409

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between 'normal' and 'attacked' categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model's accuracy scores. We also assess the two approaches' results according to their accuracy in order to decide which way is more effective. © 2024 IEEE.

关键词： Decision trees

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Exploring Machine Learning Techniques for Accurate Heart Disease Detection: A Comprehensive Study

Exploring Machine Learning Techniques for Accurate Heart Dis...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Ehsanu Hoque, Md. Habib Mahbub, Mrittika Bin Md Tomari, Mohd Razali Bashar, Rezaul Rashed, Md. Golam Das, Dipankar University of Rajshahi Dept. of Information and Communication Engineering Bangladesh Pundra University of Science and Technology Dept. of Computer Science and Engineering Bangladesh Dept. of Electronic Engineering Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (纸本)9798350391213

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification. © 2024 IEEE.

关键词： Traffic signs

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