8333 research outputs found
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Designing a Web-Based Cloud Storage Management System to Optimize Data Retrieval, Storage Efficiency, Security, and Accessibility
No full textThis project involves the Designing of the cloud based File sharing and storing Application utilizing AWS services to increase effectiveness and dependability in file transmitting and user messaging. Due to this, the system incorporates AWS Amplify, AppSync as well as DynamoDB to support data storage, access and synchronizing. AWS SNS and Lambda handle notifications, making the system capable of providing real time notification on the events such as, when a file has been uploaded or updated. In order to give robust security, Amazon Cognito is used for user sign on and Amazon GuardDuty for malware scan of files to be uploaded. It utilizes CloudFront for distributing content and CloudWatch for tracking system and logs performances. Largely, this application focuses on scalability, security and usability issue that one can associate with most cloud application solutions. The project offers a sound framework for safe and efficient notification solutions and offers space for optimisation in the form of broadening and diversifying notifications and adding bespoke analysis features
Blockchain and AWS Integration for Financial Transaction
No full textToday blockchain represents an innovative breakthrough by creating secure digital transaction recording systems with immutable data storage capabilities. The distributed nature of blockchain architecture attracts finance institutions because they value data protection. As transaction numbers rise Blockchain technology becomes slower which creates performance obstacles. The proposed solution adopts both blockchain systems and Amazon Web Services (AWS) to build a comprehensive financial transaction management system which resolves current challenges. The integration of AWS with its high availability together with elasticity enhances blockchain capabilities through real-time monitoring and encryption features to establish seamless financial transaction processing across scalable systems. The proposed system implements three key features including robust data security during transmission alongside improved transaction speed and support for current financial network protocols. Through research this study presents an extensive solution which explains effective blockchain integration with AWS to meet financial institutions' escalating need for secure transaction processing solutions
Comparative analysis of AWS DataSync and Google Cloud Storage Transfer Service for cloud-to-cloud data transfer
No full textOver the past few years, the term multi-cloud has caught the attraction of many organizations across the globe. Utilizing the various services from single cloud service provider leads to vendor lock-in problem for the organizations as they need to rely on single cloud service provider for their requirements. Various cloud service providers offers multiple features to their users, but single cloud service provider may or may not be suitable for unique and different requirements of the organizations. Due to this limitations organizations started leveraging the offerings from multiple cloud service providers according to their requirements. Though this approach significantly reduces the risk of vendor lock-in, but there are also challenges associated with this multi-cloud approach such as challenges associated with porting data between multiple clouds including efficient transfer methods, integrity and security issues etc. Over the time, researchers and organizations put efforts to mitigate these challenges. Various cloud service providers offers service for the data transfer from one cloud to another. This study aims to provide comparative analysis of the data transfer services of two market leading cloud service providers: AWS and Google. The factors to be considered for the comparative analysis are throughput, execution time, usability and cost of the transfer
Mitigating Serverless Cold Start Latency with Predictive Function Invocation and Adaptive Caching
No full textThe serverless industry is estimated to be worth over $21.9 billion in 2024 due to its separation of infrastructure management from application, and at the same time, it provides scalability and immense cost reduction. There are few problems when it comes to serverless that is cold start latency, which hinders the performance. The purpose of the study is to create a hybrid approach by combining predictive modeling to predict the function invocation along with adaptive caching to reduce the cold start frequency and latency. The study involved setting up the OpenWhisk serverless platform on a cloud machine, simulating real-world serverless behavior, using machine learning techniques to predict the function invocation time, creating a robust cache algorithm, and combining these two by creating a framework called SmartFaaS, followed by comparing it with the vanilla OpenWhisk solution for benchmarking. The key finding from the study was that SmartFaaS consistently used 30% of resource throughout all the experiments and the overall execution time were maintained under 200 ms compared to OpenWhisk 1200 ms showing an approximate 83% reduction in latency. By Using the predictive model in SmartFaaS only 37% of cold starts were recorded showing efficient reduction in cold start frequency. From these results, SmartFaaS offers a more effective and cost-efficient solution for mitigating cold start issues in serverless computing. The research contributes to the current state of the art by demonstrating the effectiveness of combining predictive modeling with adaptive caching. In practice, this means developers and cloud service providers can achieve better performance and resource utilization. However, the limitations of this study were that a single node application was tested, and as of now, only Node.js applications can utilize SmartFaaS. Future work could involve testing SmartFaaS with diverse workloads and exploring other machine learning models
What settings specific to AWS configurations, services and best practices can be applied on AWS for ensuring the data security & compliance related to business expenses data storage by Bookkeeping organizations & processing in the territory of European & UK?
No full textWith a growing number of digital threats in today’s world, financial organizations need to have optimal data security, especially when it comes to compliance with such regulations as GDPR and CIS Controls. This paper explores implementation of AWS configurations in the case of financial services firm Capisso with specific focus on their data security and compliance requirements. The paper focuses on different AWS configurations relevant to GDPR articles and CIS Controls needed by the organization operating in the EU and UK dealing with financial information. The study results point at the importance of aligning the AWS services such as IAM, encryption solutions, and monitoring tools for addressing risks and compliance. Besides, it identifies gaps that may be addressed by Capisso to improve its security status and compliance strategy and serve as reference for other financial organizations effectively
Enhancing Phishing URL Detection by Leveraging Machine Learning and Deep Learning Models
No full textPhishing is one of the most popular types of cybercrime; it deceives the users to surrender some personal information through the help of fake sites that mimic the real ones. Phishing URLs are difficult to detect and are currently one of the biggest issues because of the growing complexity of these attacks and the inefficiency of the measures. This paper aims to compare the efficiency of multiple machine learning and deep learning techniques for the detection of phishing URLs with an emphasis on the impact of feature engineering. The work compares several machine learning models such as Random Forest, AdaBoost, Logistic Regression, LSTM, and TabNet using a labeled set of URLs comprising benign and phishing URLs. To enhance the classification performance, a few properties like the URL length, special characters in the URL, the use of the HTTPS protocol, and several subdomains are extracted. The research evaluates model performance based on the evaluation parameters including accuracy, precision, recall, and F1-score, and addresses issues like class imbalance and dataset complexity. The results reveal that the model with the highest accuracy was XGBoost with 88.93%, while deep learning models such as LSTM and TabNet were slightly lower. However, Random Forest and XGBoost enhance the performance in detecting phishing URLs, and the traditional machine learning methodologies are sufficient for detecting phishing URLs
Developing a Framework for Integrating Security Testing into the CI/CD Pipeline using Automation
No full textThe increasing frequency of security breaches in software applications proves the importance of more frequent and comprehensive security testing, integrated into CI/CD deployment pipelines. This research presents a novel framework that incorporates security testing at all stages of software development life cycle. The unique mix of SAST, DAST, and dependency vulnerabilities scanning, the framework provides developers and security team with a diverse set of security testing tools. The framework is implemented using GitHub Actions to streamline automation and integrates tools such as Lint for code formatting, CodeQL for static analysis, CodeClimate for maintainability checks, Snyk for dependency vulnerability scanning, and OWASP ZAP for dynamic application security testing. The output of these processes is then containerized and pushed to Docker Hub, ensuring a seamless integration with modern CI/CD workflows. OWASP Juice Shop, a deliberately vulnerable web application, serves as the target application for demonstrating the effectiveness of this framework. By automating security testing and embedding it into CI/CD pipelines, this research provides a robust approach to mitigating risks earlier in the development process while maintaining the agility and speed of modern software delivery practices
Enhancing Security in Node.js Applications to Prevent SQL Injection
No full textWeb applications’ emergence has made security a key issue in electronic business and interaction, including managing risks associated with the SQL Injection (SQLi) vulnerability. This paper assesses the ability of Node.js-based applications to defend against SQLi threats through adopting the event-driven approach. The key areas of the study are parameterized queries, input validation and Object-Relational Mapping (ORM), and their efficiency in protecting the application with regards to performance and usability. This project outcomes helps to prove that in case of the correct usage the mentioned security measures are fairly effective in the Framework reduction of SQLi vulnerabilities. Their effectiveness was then checked through test too through manual manipulation of the vulnerabilities and automated testing using OWASP ZAP. The first experiments demonstrated that SQLi attacks boasted an almost 100% chance of success when input was not sanitized – thus, the need for proper security measures. This work underlines how it is crucial to implement extensive security principles at every phase of web applications’ evolution, which will improve safety within the Node.js domain
Adopting Zero Trust Architecture for Robust Supply Chain Protection
No full textThe present supply chain has ill-defined and porous perimeter, making it vulnerable to intercept sensitive information and disrupt operations. This research investigates the introduction of ZTA into supply chain cybersecurity, with particular emphasis on micro-segmentation, secure identity management, and real-time monitoring that exploit tools such as Istio, SPIRE, and HashiCorp Vault. To achieve this, a Kubernetes-based environment was simulated to assess ZTA's effect on risk-minimization and resilience-enhancement. ZTA principles, although showing remarkable security improvements, also brought forward a lot of impediments. Just to name a few, the integration of complex tools, the solution's scalability, and limited monitoring were the highlighted challenges. This study will explore the practical steps for needed for ZTA implementation, challenges faced, and the impact of operational efficiency. Key findings will contribute to developing a framework to integrate ZTA in supply chain management
Addressing the Threat of Deepfakes: Detection Technologies, Societal Impacts, and Future Directions
No full textThe invention and proliferation of deepfake technology has brought about significant advancements in digital media manipulation. These advancements have created both opportunities and challenges. This thesis explores the creation, detection of and societal impacts of deepfakes and investigates the development of countermeasures. Through a comprehensive literature review, this study discusses the ethical and legal implications and identifies areas for future work. The ultimate goal is to provide a thorough understanding of deepfake detection and explore a multifaceted approach to developing countermeasures