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An Efficient Penetration testing framework for Web Applications

An Efficient Penetration testing framework for Web Applicati...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： A. Roshini N. Suganthi Dhivakar. S Sai Sangeetha M K. Karthika Jayalakshmi. V Sriharisudhan P Computer Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the world currently networked, web applications play an important role in running businesses and are considered potential favourites for cyber-attacks. This paper incorporates an all-inclusive framework of web application penetration testing towards improving security within web applications. There could be incorporation of some functionalities like sub-domain enumeration, URL discovery, and parameter identification, ultimately proving to be a versatile one-stop-shop solution for identifying probable security weaknesses. Given the benchmark usage of tools such as Sublist3r, Subfinder, and ParamSpider, our framework offers several optimizations such as an enhanced hybrid discovery mechanism, intelligent crawling, and ***-based real-time visualization. Our architecture as designed will, in effect, avoid redundancy by using mechanisms like caching and will make it efficient as well as accurate. The tool improved by up to 20-30 percent as compared to conventional methods in terms of finding more subdomains and better parameter discovery rates. Further, the framework ensures minimum execution time with the multithreaded architecture that makes it several times faster than any of the available tools. Most of these performance metrics are visualized in bar charts and pie charts, which show precisely why our tool is more beneficial than the tools currently used today. This framework is a giant step forward in the automation of penetration testing, as it forms a holistic, efficient, and user-oriented solution for security professionals. Further work will be oriented to the expansion of this tool by the incorporation of machine learning algorithms to further enhance the prediction precision of detected vulnerabilities.

关键词： Uniform resource locators Visualization Accuracy Parameter estimation Limiting Automation Real-time systems Security distributed computing Penetration testing

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A Comprehensive Review of Swarm Intelligence-Based Meta-Heuristic Algorithms

A Comprehensive Review of Swarm Intelligence-Based Meta-Heur...

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Disruptive Technologies (ICDT), international conference on

作者： Abhineet Suman Gunjan Anupam Biswas Department of Computer Science and Engineering National Institute of Technology Delhi India Department of Computer Science and Engineering National Institute of Technology Silchar Assam India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems. offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Bat Algorithm (BA), and others. Each algorithm uses ideas from nature, like how ants find food and how bees interact with each other, to effectively solve nonlinear and multimodal problems. Comparative analysis focuses on convergence speed, solution quality, robustness, and computational complexity, highlighting the unique strengths and applicability of each algorithm. SI algorithms can be changed and scaled up or down easily, which makes them useful for dynamic and large-scale optimization tasks. This makes them future-proof solutions that can be used in many areas. Future research directions include new algorithms, hybrid approaches, real-world applications, and integration with machine learning techniques, promising continued advances and broader applicability. The development of SI algorithms is driven by their natural inspiration, simplicity, and potential for innovative applications, underscoring their importance in tackling diverse and complex challenges.

关键词： Machine learning algorithms Reviews Heuristic algorithms Machine learning Robustness Computational complexity Particle swarm optimization distributed computing Optimization Convergence

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Brain Magnetic Resonance Imaging Segmentation System in MS with Deep Neural Networks 18th

Brain Magnetic Resonance Imaging Segmentation System in MS w...

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18th EAI international conference on Pervasive computing Technologies for Healthcare, PervasiveHealth 2024

作者： Adamides, Giorgos Panayides, Andreas Loizou, Christos P. Nicolaou, Andria Pantzaris, Marios Pattichis, Constantinos Department of Computer Science University of Cyprus Nicosia Cyprus 3AE HEALTH LTD Nicosia Cyprus CYENS CoE Nicosia Cyprus Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus School of Molecular Medicine The Cyprus Institute of Neurology and Genetics University of Cyprus Nicosia Cyprus

ISBN: (纸本)9783031855719

Multiple Sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Nerves are covered by a layer called myelin, which is responsible for protecting and maintaining their functionality. In the case of MS, the myelin becomes damaged, resulting in the nerves functioning unpredictably. MS is a disease characterized by relapses, which can cause permanent and irreversible damage to the patient's mobility, vision, and sensations, unless appropriate medical treatment is administered in time. This highlights the importance of the early detection of the presence or differentiation of MS using computer assisted diagnosis (CAD) systems. This study proposes an automatic segmentation approach of MS damaged regions using deep neural networks that can be integrated with any 3rd party CAD system. The data utilized comprised of a total of 1838 T2-type Magnetic Resonance Imaging (MRI) images collected from a cohort of 38 patients who underwent imaging at two distinct time points. Each MRI image was accompanied by meta-data, detailing the specific locations of the observed damage attributed to MS. The experimental setup investigated and optimized a comprehensive combination of different data preprocessing and augmentation steps, using several variations of the U-Net convolutional neural network (CNN), like U-Net++, Attention U-Net, ResUNet-a and TransUNet. The proposed methods achieved a segmentation accuracy of 0.70 based on the Dice Similarity Coefficient (DSC), a performance comparable to 2D and 3D state-of-the-art approaches in literature. A software framework encapsulating the automated model was also developed to facilitate the clinical practice workflow and underpin adoption by medical practitioners. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Image segmentation

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Non-Governmental Organization Funding with Blockchain

Non-Governmental Organization Funding with Blockchain

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Disruptive Technologies (ICDT), international conference on

作者： Sumit Kumar Himanshu Vashistha Deepanshu Kumar Sharad Singhal Department of Information Technology ABES Institute of Technology Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Although a relationship between NGOs and their donors is based more on trust, donors would expect N GOs to demonstrate legitimacy and accountability this time. Transparency is at the core of building such confidence, and recent technological innovation advances, specifically blockchain, reveal new possibilities for greater accountability in the NGO sector. Blockchain is a trust less system that can confirm transactions on its own without the assistance of an external party, thus building trust between NGOs and donors. There are various major domains related to the application of blockchain in the N GO sector. Many research gaps exist regarding effective knowledge of blockchain compared to other approaches that validate its effectiveness in promoting transparency and accountability. Research has shown that blockchain technology has a high-efficiency level of about 95% in increasing the reliability of donation-tracing mechanisms. However, almost 90% of non-governmental organizations (NGOs) are reluctant to adopt such innovations. The most significant technologies driving such innovations include blockchain, smart contracts, decentralized applications (DApps), distributed ledger technology (DLT), and data analytics, which all playa vital role in increasing transparency in donation systems. While blockchain has tremendous potential to transform the way NGOs engage with donors, organizations are faced with severe challenges in its application. Such challenges tend to come from change resistance and the difficulty in adopting and maintaining new technologies. The need to carry out comprehensive research to overcome such challenges and investigate how blockchain can increase transparency and accountability in N GO operations remains the number one priority.

关键词： Technological innovation Data analysis distributed ledger Smart contracts Buildings Transforms Decentralized applications Blockchains Reliability Non-governmental organizations

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High Performance Visualization for Astrophysics and Cosmology

High Performance Visualization for Astrophysics and Cosmolog...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Nicola Tuccari Eva Sciacca Fabio Vitello Iacopo Colonnelli Yolanda Becerra Enric Sosa Cintero Guillermo Marin Milan Jaros Lubomir Riha Petr Strakos Sebastian Trujillo-Gomez Emiliano Tramontana Robert Wissing Istituto Nazionale di Astrofisica Catania Italy Università di Torino Torino Italy Barcelona Supercomputing Center Barcelona Spain IT4Innovations National Supercomputing Center Ostrava Czech Republic Heidelberg Institute for Theoretical Studies Heidelberg Germany Università degli Studi di Catania Italy University of Oslo Institute of Theoretical Astrophysics Oslo Norway

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Modern Astrophysics and Cosmology (A&C) projects produce immense data volumes, necessitating advanced software tools for data access, storage, and analysis. Visualization Interface for the Virtual Observatory (VisIVO) is one such tool enabling multi-dimensional data analysis and knowledge discovery across complex astrophysical datasets. Leveraging containerization and virtualization, VisIVO has been deployed on various distributed computing platforms. Additionally, Blender, an open-source 3D suite, provides robust tools for rendering and processing volumetric data, making it suitable for visualizing complex datasets. At the SPACE Center of Excellence these tools are being adapted for high-performance visualization of cosmological simulations performed with GADGET and ChaNGa on pre-exascale systems. However, implementing high-performance visualization on diverse HPC platforms presents several challenges, including hardware and software compatibility, data management, scalability, performance portability, and efficient resource allocation. This paper outlines strategies to integrate VisIVO with workflow frameworks and streaming platforms to address these challenges. Workflow frameworks enhance portability, scheduling, and reproducibility of visualization workflows on pre-exascale systems.used in A&C simulations. We also discuss the use of streaming platforms to enable concurrent (i.e. in-situ) analysis and visualization of simulations, reducing the need to store full simulation data by leveraging distributed databases that stream the output data in real time. Lastly, we present an adaptation of Blender to handle large-scale particle-based astrophysical data, offering high-quality visualization with interactive exploration capabilities.

关键词： Adaptation models Analytical models Astrophysics Computational modeling Scalability Data visualization distributed databases Data models Reproducibility of results Real-time systems

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A Hybrid Approach to Feature Selection for Cyber Threat Detection in IoT Networks

A Hybrid Approach to Feature Selection for Cyber Threat Dete...

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

作者： Mohammad Ubaidullah Bokhari Mohd Zain Khan Faheem Syeed Masoodi Department of Computer Science Aligarh Muslim University Aligarh India School of ICT Bahrain Polytechnic Kingdom of Bahrain Research Fellow INTI International University Malaysia

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The quick expansion of Internet of Things (IoT) devices have greatly impact multiple sectors, including healthcare, smart agriculture, and environmental monitoring, thereby impacting human lives. The heterogeneous nature and limited resources of these devices make them highly vulnerable to cyber threats. To address these security challenges, machine learning plays a significant role in the development of adaptive intrusion detection systems. Machine learning helps in identifying patterns from the vast amounts of data produced through different IoT devices, particularly for enhancing security. It facilitates in identifying assaults by analyzing trends and abnormalities in data that indicate threats, offering a proactive strategy for cyber security. In this study, we have explored the impact of different Methods of feature selection on the effectiveness of machine learning models in detection of IoT attacks. Feature Selection Methods such as Principal Component Analysis (PCA), Autoencoders (AE), Linear Discriminant Analysis (LDA) and a hybrid selection method combining SelectKBest and Recursive Feature Elimination (RFE) are employed to asses different machine learning models. The UNSW-NB15 dataset is used to evaluate the effectiveness of Random Forest (RF), XGBoost and Support Vector Machine (SVM) classifiers. The experimental results indicate that the hybrid feature selection approach considerably improves classification efficiency, particularly for the RF model which outperform the other having accuracy 99.91%, surpassing traditional dimensionality reduction techniques. The suggested approach effectively eliminates redundant attributes while retaining the most informative elements, leading to improved detection accuracy and other performance metrics. These findings underscore the practical applicability of hybrid feature selection techniques in cyber security, offering a scalable and efficient solution for IoT attack detection.

关键词： Support vector machines Radio frequency Accuracy Autoencoders Feature extraction Threat assessment Internet of Things Security Computer crime Principal component analysis

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Mitigating the Technological Challenges of Regenerative Farming with an Integrated Framework Merging IoT, Ontologies, and GIS 3rd

Mitigating the Technological Challenges of Regenerative Farm...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Mokgetse, Tshepiso L. Sridaran, Rajagopal Hlomani, Hlomani Botswana International University of Science and Technology Palapye Boseja Botswana Marwadi University Gujarat Rajkot India

ISBN: (纸本)9783031863011

Regenerative farming offers a sustainable approach to agriculture by promoting ecosystem restoration, soil health, and biodiversity conservation. However, the adoption of regenerative practices faces significant technological challenges, including data collection, management, interoperability, integration, mapping, and spatial analysis. These challenges hinder the scalability and effectiveness of regenerative farming, particularly in terms of monitoring, decision-making, and resource optimization. This article addresses these challenges by proposing an integrated framework that combines the Internet of Things, Ontologies, and Geographic Information systems. IoT is leveraged for real-time data collection and management, Ontologies ensure seamless data interoperability across systems. and GIS enables advanced spatial analysis and mapping. Using a meta-synthesis of existing literature, this study identifies the key technological obstacles to regenerative farming and presents a solution that integrates these emerging technologies. The proposed framework aims to enhance the effectiveness of regenerative practices, support ecosystem health, and improve the sustainability of agricultural systems. Farmers will also leverage from sharing practices across different regions. The data will allow farmers to get practical results of the effectiveness of regenerative farming. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk perception

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Scalable Intrusion Detection Using Recurrent Neural Networks in distributed Data Engineering systems 3rd

Scalable Intrusion Detection Using Recurrent Neural Networks...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Jeyaram, A. Muthukumaravel, A. Chennai India Chennai India

ISBN: (纸本)9783031862892

Traditional Intrusion Detection systems.(IDS) frequently rely on anomaly and signature-based methods, which have limits in terms of accuracy, scalability, and flexibility. Anomaly-based systems.generate a high number of false positives, causing operational problems, but signature-based solutions cannot protect against zero-day assaults. To overcome these problems, the proposed system improves IDS performance in dispersed data engineering contexts by utilizing Long Short-Term Memory (LSTM) networks, which are a type of Recurrent Neural Network (RNN). Furthermore, network data has temporal correlations, which the LSTM-based IDS can utilize to reduce false positives and improve detection accuracy. The methodology ensures maximum fault tolerance and resiliency while scaling to handle terabytes of real-time data by distributing the LSTM model across a cluster and cloud-based architecture. Research results reveal that the proposed approach outperforms existing IDS approaches. For instance, the KDD Cup 99 dataset, achieved 92.5% accuracy, which is higher than other top algorithms in this scenario defined as: Precision (88.4%), and recall (91.2%). F1 score: 89.7%. However, the scores acquired from the NSL-KDD dataset were 91.2% F1 score and 93.2% accuracy, which is rather low in comparison to our results but can provide us with an elementary understanding of the outliers present in both approaches. Furthermore, the techniques greatly reduced false positives and false negatives by overcoming modern cybersecurity challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Identifying Application-Level distributed Denial-of-Service (DDoS) Attacks with Machine Learning (ML) 3rd

Identifying Application-Level Distributed Denial-of-Service ...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Yaqub, Muhammad Atif Mustafa, Muzzamil Chuhan, Saad Hussain Sattar, Basit Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Qaiser, Muhammad Saad Abdin, Zain ul Bilal, Afshan Ali, Jibran Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Artificial Intelligence University of Management and Technology Lahore Pakistan Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan Department of Computer Science Bahria University Lahore Campus Lahore Pakistan Premier Systems Karachi Pakistan

ISBN: (纸本)9783031863042

The modern digital age has seen an exponential growth in internet penetration, which in turn has increased the probability of a denial-of-service attack. When an evil user launches a Denial of Service (DoS) attack, it’s with the goal of flooding computing and network resources and blocking access to them for normal users. These kind of assaults may happen at any level of the OSI model, from the application layer all the way down to the transport layer. This research focuses on application layer DoS attacks instead of transport and network DoS attacks and attempts to detect Denial of Service using Machine Learning and Neural Network approaches. An updated dataset of Denial of Service attacks divided into many subcategories was used in this experiment. According to the research, the decision tree approach performed well in terms of accuracy and splitting compared to the logistic regression approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862953

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using Machine Learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on Machine Learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on Machine Learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention Mining for Mental Health Diagnosis Using Logistic Regression;machine Learning for Accessible and Precise Assessment in Smart Monitoring systems.an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on Data Mining and Machine Learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive Learning Techniques;asthma Prediction Using Fuzzification and Machine Learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep Learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of Machine Learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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