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Advanced Network Malware Detection: Integrating PySpark and Machine Learning Techniques 6

Advanced Network Malware Detection: Integrating PySpark and ...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Dorrah, Mostafa ElSaadany, Abdallah Ashraf, Ahmed ElMaghraby, Asmaa Atta, Mohamed Adel, Yousef Yasser, Tawfik Fathi, Mostafa Abdelbaky, Ibrahim ITCS School Nile University Giza Egypt AI Program ITCS Nile University Giza Egypt Computer Science Program ITCS Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (纸本)9798350373875

This study introduces a novel approach for detecting malware using cutting-edge machine Learning with PySpark, which targets the increasingly complex cyber threats that conventional security measures struggle to counter. Our research aims to create a highly accurate system for detecting harmful network activities by examining network traffic patterns, including the duration and frequency of connections, the amount of data transferred, and the involved endpoints. We utilized a comprehensive dataset from Kaggle and performed thorough data preprocessing and feature selection to optimize our models' performance. The research employed a variety of algorithms such as Logistic Regression, Random Forest, Decision Tree, and Naive Bayes. Each model was fine-tuned and evaluated based on its recall in distinguishing between safe and dangerous software. Our work seeks to make a significant impact in the field of cybersecurity by presenting an effective machine learning-driven tool for accurate malware detection, thereby improving network security. © 2024 IEEE.

关键词： Decision trees

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Gender Detection from Hand Palm Images: A PySpark-Driven Approach with VGG19 Feature Extraction and MLP Classification 6

Gender Detection from Hand Palm Images: A PySpark-Driven App...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Henedak, Mayar Tarek Ezzeldin, Ganna Waleed Mohamed, Nada Adel Hamed, Malak Essam ElMasarawi, Sarah Mahmoud Fekry, Shahd Mohamed Yasser, Tawfik Fouad, Khaled M. Fathi, Mostafa Abdelbaky, Ibrahim BMD Program ITCS Nile University Giza Egypt AlProgram ITCS Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt Computer Science Program ITCS Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt

ISBN: (纸本)9798350373875

This paper presents a comprehensive methodology for gender detection using hand palm images, leveraging image processing techniques and PySpark for scalable and efficient processing. The approach encompasses a meticulous image preprocessing pipeline, incorporating essential stages like grayscale conversion, the application ofthe Difference of Gaussians (DoG) filter, and adaptive histogram equalization. This approach not only refmes image features but also ensures scalability, accommodating large datasets seamlessly. After preprocessing of hand images, the VGG19 model is employed as a powerful feature extractor, utilizing its pre-trained convolutional layers to capture high-level abstract features from hand palm images. The fmal step was to train and fit the tabular data and extracted features on the Multilayer Perceptron (MLP) model, which is a feedforward neural network for classification tasks in PySpark. The MLP model, with its ability to learn complex patterns, proves effective in accurately predicting gender categories based on the extracted features. Finally, the model results were 94% accuracy for training and 87% testing which indicates that the model is generalizing well to unseen data. Also, another evaluation metrics were used like Precision 0.87, Recall 0.87, Fl Score 0.87 and AUC 0.92. © 2024 IEEE.

关键词： Multilayer neural networks

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Exploiting Machine Learning and Remote Sensing for Precision Crop Mapping in Africa

Exploiting Machine Learning and Remote Sensing for Precision...

引用

International Conference on computer Theory and Applications (ICCTA)

作者： Omar Ayman Mosaab Emad Gamal Mohamed ElSayed Atwa Yassin Khaled Noha Gamal El-Din Saad Computer Science Program School of ITCS Nile University Giza Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

As the global population continues to grow, the demand for food intensifies, making precision agriculture essential for enhancing productivity and resource efficiency. A critical component of precision agriculture is accurate crop mapping, which enables optimized resource allocation and better yield prediction. This study focuses on developing a machine learning-based predictive model for crop mapping using multispectral remote sensing data from the Senegalese groundnut basin. The dataset, collected via Sentinel-2 satellites, includes optical bands and vegetation indices over four time periods in 2023. After preprocessing and addressing class imbalance through data augmentation and SMOTE, several machine learning models were evaluated, with Random Forest emerging as the most effective. The results show a significant improvement in classification accuracy, particularly for underrepresented crop types, with the Random Forest model achieving 93.57% accuracy after SMOTE, compared to 57.43% before SMOTE. The outcomes of this research extend beyond technological advancements, contributing to environmental sustainability by enabling more precise resource use, reducing waste, and minimizing the environmental impact of agricultural practices. Furthermore, this type of crop mapping supports food security by helping farmers optimize yields and manage crops more effectively, directly benefiting communities reliant on agriculture. The integration of machine learning and remote sensing in precision agriculture has the potential to foster a more resilient and sustainable agricultural system globally.

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ArabicQuest: Enhancing Arabic Visual Question Answering with LLM Fine-Tuning

ArabicQuest: Enhancing Arabic Visual Question Answering with...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Asmaa ElMaghraby Samaa Maged Mohamed Essawey Rawan ElFaramawy Esraa Negm Ghada Khoriba Artificial Intelligence Program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

In an attempt to bridge the semantic gap between language understanding and visuals, Visual Question Answering (VQA) offers a challenging intersection of computer vision and natural language processing. Large Language Models (LLMs) have shown remarkable ability in natural language understanding; however, their use in VQA, particularly for Arabic, is still largely unexplored. This study aims to bridge this gap by examining how well LLMs can improve VQA models. We use state-of-the-art AI algorithms on datasets from multiple fields, including electric devices, Visual Genome, RSVQA, and ChartsQA. We introduce ArabicQuest, a Text Question Answering (TQA) tool that combines Arabic inquiries with visual data. We assess the performance of LLMs across various question types and image settings and find that fine-tuning me thods su ch as LLaMA-2, BLIP-2, and Idefics-9B-Instruct models provide encouraging results, although challenges still arise in counting and comparison tasks. Our findings demonstrate the importance of advancing VQA further—especially for Arabic—to enhance accessibility and user satisfaction in a variety of applications.

关键词： Bridges Performance evaluation Visualization Large language models Semantics Refining Linguistics

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Cross-Modal Embedding Generalized Framework for Audio-Text Alignment

Cross-Modal Embedding Generalized Framework for Audio-Text A...

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International Conference on computer Theory and Applications (ICCTA)

作者： Noha Gamal El-Din Saad Ahmed El-Shafee Computer Science Program School of ITCS Nile University Giza Egypt Computer Science Department Faculty of Computer Science MSA University Giza Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

This paper presents a transformative cross-modal framework that unifies audio and text modalities through the synergistic use of Convolutional Neural Networks (CNNs) for rich audio feature extraction and BERT (Bidirectional Encoder Representations from Transformers (BERT) for sophisticated, context-aware text embeddings. The proposed framework seamlessly projects both modalities into a shared embedding space, facilitating precise audio-text alignment and retrieval. Unlike traditional models that treat audio and text in isolation, this framework introduces a dynamic CNN architecture tailored to capture intricate audio patterns, while BERT delivers deep semantic understanding of text. Employing cosine similarity loss, the framework ensures optimal cross-modal alignment, achieving high retrieval accuracy and robust generalization across diverse datasets. Tested on the Free Music Archive (FMA) and Speech Commands datasets, the model demonstrated a remarkable ability to cluster and align related audio and text samples with mean similarity scores of 0.82, and achieved cross-modal retrieval accuracy of 0.67, even when applied to domains outside its training scope. This innovative framework offers broad potential for applications such as music information retrieval, speech recognition, and multimedia content analysis, pushing the boundaries of multimodal learning and retrieval systems.

关键词： Visualization Adaptation models Accuracy Content management Bidirectional control Speech recognition Encoding Convolutional neural networks Music information retrieval Cross modal retrieval

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Advanced Network Malware Detection: Integrating PySpark and Machine Learning Techniques

Advanced Network Malware Detection: Integrating PySpark and ...

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Computing and Informatics (ICCI), International Conference on

作者： Mostafa Dorrah Asmaa ElMaghraby Abdallah ElSaadany Mohamed Atta Ahmed Ashraf Yousef Adel Tawfik Yasser Mostafa Fathi Ibrahim Abdelbaky ITCS School Nile University Giza Egypt AI Program ITCS Nile University Giza Egypt Computer Science Program ITCS Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

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Gender Detection from Hand Palm Images: A PySpark-Driven Approach with VGG19 Feature Extraction and MLP Classification

Gender Detection from Hand Palm Images: A PySpark-Driven App...

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Computing and Informatics (ICCI), International Conference on

作者： Mayar Tarek Henedak Ganna Waleed Ezzeldin Nada Adel Mohamed Malak Essam Hamed Sarah Mahmoud ElMasarawi Shahd Mohamed Fekry Tawfik Yasser Khaled M. Fouad Mostafa Fathi Ibrahim Abdelbaky BMD Program ITCS Nile University Giza Egypt AI Program ITCS Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computer Science and Engineering New Mansoura University Computer Science Program ITCS Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

This paper presents a comprehensive methodology for gender detection using hand palm images, leveraging image processing techniques and PySpark for scalable and efficient processing. The approach encompasses a meticulous image preprocessing pipeline, incorporating essential stages like grayscale conversion, the application of the Difference of Gaussians (DoG) filter, and adaptive histogram equalization. This approach not only refines image features but also ensures scalability, accommodating large datasets seamlessly. After preprocessing of hand images, the VGG19 model is employed as a powerful feature extractor, utilizing its pre-trained convolutional layers to capture high-level abstract features from hand palm images. The final step was to train and fit the tabular data and extracted features on the Multilayer Perceptron (MLP) model, which is a feedforward neural network for classification tasks in PySpark. The MLP model, with its ability to learn complex patterns, proves effective in accurately predicting gender categories based on the extracted features. Finally, the model results were 94% accuracy for training and 87% testing which indicates that the model is generalizing well to unseen data. Also, another evaluation metrics were used like Precision 0.87, Recall 0.87, F1 Score 0.87 and AUC 0.92.

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HistoryQuest: Arabic Question Answering in Egyptian History with LLM Fine-Tuning and Transformer Models

HistoryQuest: Arabic Question Answering in Egyptian History ...

引用

Intelligent Methods, Systems, and Applications (IMSA)

作者： Samaa Maged Asmaa ElMaghraby Ali Marzban Mohamed Essawey Amira Ahmed Esraa Negm Wael H. Gomaa Artificial Intelligence Program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Beni-Suef University

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Question answering (QA) in Egyptian history presents a unique and complex challenge for Arabic natural language processing (NLP). This study aims to explore and assess how large language models (LLMs) can enhance the accuracy and performance of Arabic question answering (QA), specifically in this domain. To conduct this investigation, we utilize two comprehensive datasets: the Arabic History-QA dataset and the Contextual Articles Dataset, which cover pivotal historical periods. We evaluate transformer-based models, including AraBERTv2, BERT-large-Arabic with Retrieval-Augmented Generation (RAG), fine-tuned LLaMa-2, and zero-shot LLaMa-3 with Retrieval-Augmented Generation (RAG). Through a rigorous and detailed evaluation process, we analyze how these models address various questions related to Egyptian history. This research contributes valuable insights into advancing the capabilities of Arabic NLP in specialized domains such as historical question answering. Our best results, summarized as the superiority of LLMs, beat those with transformers; additionally, the RAG significantly raised the performance level overall.

关键词： Analytical models Accuracy Large language models Transformers Question answering (information retrieval) History

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Diabetic Retinopathy Detection: A PySpark-Driven Approach with VGG 16 Feature Extraction and MLP Classification

Diabetic Retinopathy Detection: A PySpark-Driven Approach wi...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Toka Nabil Sama Ayman Samaa Ahmed Mawada Nagy Muhammad Megahed Abdulmoniem Elsaeed Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Infromation Technology and Computer Science Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and precise medical diagnostics. Three processes were involved in the development of a Diabetic Retinopathy (DR) diagnosis tool: feature extraction, feature reduction, and image classification. The research employed Apache Spark, a distributed computing framework, to manage large datasets and enhance the performance of the multilayer perceptron (MLP) model via hyperparameter tuning and cross validation. Utilizing resources more effectively and achieving faster training times were made possible by Apache Spark. To support data-driven decision-making, the study also emphasized the significance of distributed platforms for analyzing large amounts of real-time diabetic data. To produce discriminative features for classification, the VGG16 architecture was employed for feature extraction. In the last epoch, the MLP model performed remarkably well, with an accuracy of 97%. The study also underlined the value of distributed platforms for data-driven decision-making by analyzing substantial volumes of real-time diabetes data.

关键词： Diabetic retinopathy Computational modeling Distributed databases Cluster computing Machine learning Multilayer perceptrons Feature extraction Retina Real-time systems Medical diagnosis

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Apache Spark Powered: Enhancing Network Intrusion Detection System Using Random Forest

Apache Spark Powered: Enhancing Network Intrusion Detection ...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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