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Denial of Service (DoS) Attacks in SDN-Based VANETs: A Study

Denial of Service (DoS) Attacks in SDN-Based VANETs: A Study

引用

International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Jyotsana Sardana Sunil Kumar Dharminder Kumar Kamlesh Dutta Dept. of Computer Science & Engg Guru Jambeshwar University of Science and Technology Hisar Haryana India Dept. of AI & Data Science Guru Jambeshwar University of Science and Technology Hisar Haryana India Dept. of Computer Science & Engg Gurugram University Gurugram Haryana India Dept. of Computer Science & Engg National Institute of Technology Hamirpur H.P India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Vehicular ad hoc networks (VANETs) play a vital role in ensuring the success of Intelligent transport systems (ITS), but the architecture of VANETs is vulnerable to several security attacks. There are also some challenges associated with VANETs such as the management of large-scale dynamic heterogeneous networks and secure communication over the network. The research community stressed on the integration of Software-defined Networking (SDN) with traditional VANETs to address these challenges. The SDN enhances the functionality of traditional VANETs by adding a centralised control mechanism, programmability, and flexibility *** SDN also mitigates some security issues in VANETs to a limited degree. However, SDN's inherent features introduce new security vulnerabilities mainly in the control plane. This paper first examines the characteristics of SDN when integrated with VANETs. Then, this paper discusses the new security vulnerabilitiesthat arise as a result of the integration of SDN into VANET architecture. The mostcritical security attack in SDN-based VANETs is Denial of Service (DoS) attack. This paper also examines different types of DoS attacks in SDN-based VANETs along with their potentialdefence mechanisms. Finally, this paper highlights the future research directions in the domain of securing the network operations of SDN-based VANETs.

关键词： Prevention and mitigation Vehicular ad hoc networks Grid computing Real-time systems Heterogeneous networks Security Reliability Vehicle dynamics Software defined networking Resilience

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Detection of Web-Attack using DistilBERT, RNN, and LSTM 11

Detection of Web-Attack using DistilBERT, RNN, and LSTM

引用

11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Bokolo, Biodoumoye George Chen, Lei Liu, Qingzhong Sam Houston State University Dept. of Computer Science HuntsvilleTX United States Georgia Southern University Dept. of Info Technology StatesboroGA United States

ISBN: (纸本)9798350336986

The rise in usage of the Internet has tremendously helped those who use web applications. Web-based applications are becoming more susceptible to numerous security risks and network vulnerabilities as online attacks continue to develop. Malicious code or contents could be embedded in requests from HTTP causing attacks like SQL injections *** this research, an online intrusion detection system is presented to tackle the rise in web application attacks. Our web intrusion detection system uses a Distil-BERT, RNN, and LSTM model to identify attacks with body, URL, and User-data. The experimental findings demonstrate that our model successfully classifies the attacks with body, URL, and user data with a 94% accuracy. © 2023 IEEE.

关键词： HTTP

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： T. Rajendran N Mohamed Imtiaz K Jagadeesh Balaji Sampathkumar Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Dept. of Information Science and Engineering Nagarjuna College of Engineering and Technology Bangalore India Dept. of Computer Science and Engineering Sri Venkateswaraa College of Technology Chennai India Dept. of Computer Science and Engineering Akshaya College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework’s extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN.

关键词： Deep learning Training Accuracy Quantum computing Recurrent neural networks Threat assessment Convolutional neural networks

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Integration of weighted majority voting in machine learning algorithms to enhance pine tree crown mapping on UAV imagery

Integration of weighted majority voting in machine learning ...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Hosingholizade, Ali Erfanifard, Yousef Alavipanah, Seyed Kazem Pirasteh, Saied Garcia Millan, Virginia Dept. of Remote Sensing and GIS Faculty of Geography University of Tehran Tehran Iran Institute of Artificial Intelligence School of Mechanical and Electrical Engineering Shaoxing University China Department of Geotechnics and Geomatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Dept. of Computer Science and Programming Languages School of Computer Science and Enginering University of Málaga Málaga Spain

The shape and area of the crown of each tree are among the most influential parameters for identifying and controlling the processes of photosynthesis, respiration, transpiration and its management. In such a way that various physiographic functions, such as carbon dioxide absorption, light energy absorption, oxygen release and transpiration, which are vital for the growth and development of the tree, are done in the crown. In this research, the RGB image of the UAV with a spatial resolution of 2 cm was resampled to three pixel sizes of 10, 30 and 50 cm. Then, each image was classified separately by SVM, ANN and MLC algorithms, which are all part of Ensemble. In the next step, each of the obtained crowns was compared with the digitization of the same crown, and based on the area of the crown obtained from each classification and normalization method, the weight was obtained specifically for the same crown. Finally, by using the weighted majority voting method, classifications were fusioned at the decision level. The results showed that the ANN method gives better results in all pixel sizes compared to MLC and SVM. Also, the combination of different classification methods with the weighted majority voting method based on the weight assigned to the same crown based on each classification method has significantly increased the classification accuracy of the tree crown in all the sizes of the analyzed pixels. © Author(s) 2024.

关键词： Pixels

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Analyzing Cyberbullying Word Distribution Across Social Media to Develop Lexicon Dictionary

Analyzing Cyberbullying Word Distribution Across Social Medi...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： V Sahana K M Anil Kumar Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India Dept. of Computer Science and Engineering JSS Science and Technology University Mysuru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Social media platforms have become essential tools for communication, collaboration, and exchanging information, ideas, and knowledge among users worldwide. Despite their benefits, the anonymity offered by these platforms has unfortunately led to a rise in hate speech and cyberbullying, causing concern across the globe. This issue has drawn the attention of researchers and scholars now focused on devising methods for automatically detecting cyberaggression and hate speech. The goal is to mitigate these harmful behaviors and create safer online environments [1]. This study specifically aims to identify and analyze the usage of cyberbullying language within the English Tweet corpus, focusing on distinguishing between bullying and non-bullying language. This research contributes to developing more effective cyberbullying detection models by examining the frequency and patterns of cyberbullying words. These findings are vital for enhancing the efficacy of cyberbullying detection on social networks in future research efforts. The conclusions of the experiments indicate that the SVM achieved the highest average accuracy, approximately $84 \%$.

关键词： Support vector machines Accuracy Hate speech Text categorization Cyberbullying Focusing Media Safety Multimedia communication Public healthcare

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Info-ExtNet: An Information Extraction framework from Noisy Structured Documents

Info-ExtNet: An Information Extraction framework from Noisy ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sk Shamim Aktar Debapriya Banik Dept. of Computer Science & Engineering ICFAI University Tripura Agartala India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The exponential growth of digital data necessitates efficient and accurate information extraction (IE) from structured documents. Traditional rule-based systems, limited by domain-specific constraints, struggle with scalability and adaptability. This paper proposes Info-ExtNet, an innovative model that leverages the LayoutLM architecture by integrating text, 2D positional, and image embeddings for enhanced document understanding. Our approach operates through three key stages: preprocessing, modeling, and post-processing. Advanced Optical Character Recognition (OCR) techniques and layout parsing are used during preprocessing to transform scanned documents into structured data while retaining essential spatial information. The core modeling phase employs a transformer-based architecture with multimodal embeddings to capture both textual and visual features effectively, and the post-processing stage refines the extracted data to ensure precision and *** model was evaluated using benchmark datasets such as FUNSD and SROIE, achieving high performance metrics, including a Precision of 0.94, Recall of 0.94, and an F1-Score of 0.95. These results surpass state-of-the-art (SOTA) methods, demonstrating the robustness of our model in handling complex document layouts. The extracted information, saved in a structured CSV format, supports immediate integration into various downstream applications. This work provides a scalable solution for document intelligence, paving the way for future research in document processing and analysis.

关键词： Adaptation models Visualization Optical character recognition Layout Transforms Transformer cores Information retrieval Feature extraction Transformers data models

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Integrating Deep Learning Techniques for Enhanced Cyclone Prediction and its Impact on Air Quality Index Utilizing Satellite Imagery

Integrating Deep Learning Techniques for Enhanced Cyclone Pr...

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Students Conference on engineering and Systems (SCES)

作者： Sushruth Vijayakumar Sonakshi Jamadhiar Srujan M Raj Vishnudeep MR Saritha K Dept. of Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

Tropical cyclones (TCs greatly impact ecosystems and weather patterns, often leading to catastrophic outcomes this research explores dynamic relationship between the Air Quality Index (AQI) and Tropical Cyclones (TCs), examining changes in air quality during different stages of TCs. Focusing on Cyclone Biparjoy's impact on India's coastline in June 2023, we uncover its influence on air quality. The study shows good predictive performance by utilizing meteorological data, satellite observations, and machine learning algorithms such as CNN and ConvLSTM. Results show promising outcomes, with CNN achieving an RMSE of about 12.03 and an MAE of approximately 8.523 for TC intensity prediction, and ConvLSTM producing an RMSE of approximately 0.22 and a loss of around 0.04 for TC track prediction. Additionally, regression techniques using meteorological data from Tuetempo for AQI prediction were employed. The ExtraTrees Regression model exhibited strong predictive performance with an RMSE of approximately 19.627, while the Xgboost model had a slightly higher RMSE of around 21.72 our dataset included meteorological data from Tuetempo website for AQI forecasts and datasets like Hursat and INSAT 3D satellite photos for TC-related data, this work offers important insights into the dynamics of air quality during TC events.

关键词： Solid modeling Tropical cyclones Wind speed Atmospheric modeling Predictive models Air quality Satellite images

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An Approach for XAI Visualizations for Explainability of Alzheimer’s Detection

An Approach for XAI Visualizations for Explainability of Alz...

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International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Shraddha Khanapur Chirag B Bharadwaj Raghav Bhardwaj Jyothi S Nayak Dept. of Computer Science and Engineering BMS College of Engineering Bengaluru India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

In the field of medical image analysis, the integration of machine learning (ML) models has significantly enhanced the process of identifying and diagnosing Alzheimer’s disease. However, the opacity of these models, often termed as "black boxes," raises concerns regarding their interpretability and reliability in clinical settings. Explainable artificial intelligence (XAI) techniques have emerged as crucial tools to address these challenges by clarifying the reasoning behind model outputs. This paper offers an extensive review of literature concerning the employment of Explainable Artificial Intelligence (XAI) methodologies, such as SHAP, LIME, GradCAM, DeepLIFT, Saliency Maps, and LRP to augment the comprehensibility of ML models applied in the detection of Alzheimer’s disease.

关键词： Wireless communication Analytical models Image analysis Explainable AI Soft sensors Transforms Predictive models data models Alzheimer's disease Medical diagnostic imaging

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design

Gaze Detection and Text Extraction from Images using CNN wit...

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International Conference on Advanced computer Control, ICACC

作者： Jithin Jose Ajeesh Ramanujan Dept. Of Computer Science And Engineering College Of Engineering Trivandrum Trivandrum India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user’s gaze direction and extracts the necessary portion of text, which constitutes the user’s region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model’s accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical.

关键词： Human computer interaction computer vision Accuracy Content management Noise Assistive technologies Media Real-time systems Convolutional neural networks Optimization

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Flood Prediction System with Voting Classifier

Flood Prediction System with Voting Classifier

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Akshitha K M. Vijay Anand Dept. of Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

Flood prediction plays a vital role in reducing the influence of natural disasters among the communities and the infrastructure. This paper presents a machine learning approach for flood prediction using a voting classifier, combining information from turbidity, gauge height, and discharge as key features. The dataset, comprising historical records of these parameters, serves as the foundation for training and evaluating the predictive models. In our work, we employ a voting classifier, integrating Random Forest and Gradient Boosting algorithms to harness the strengths of diverse models. The ensemble approach aims to enhance prediction accuracy and robustness by aggregating the predictions of individual classifiers. The choice of features turbidity, gauge height, and discharge is motivated by their relevance in capturing hydrological dynamics associated with flood events. The dataset is preprocessed to handle missing values, normalize features, and encode the target variable. The voting classifier is being trained upon a subset of data, and its accomplishment is estimated on a discrete test set. Various metrics, including accuracy, are employed to assess the model's predictive capabilities. Results indicate promising performance in flood prediction, showcasing the potential of ensemble methods for such applications. This system assures an accuracy of about approx. 99% and alert notification will be sent to the residents of flood predicted areas. Hence, this contributes to the ongoing efforts in leveraging machine learning techniques and also provides an alert notification for enhancing disaster preparedness and response with proper communication.

关键词： Disasters Turbidity Predictive models Prediction algorithms Boosting Discharges (electric) Robustness

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