Cloud computing has become an integral part of modern business operations, offering flexibility, scalability, and cost-efficiency. However, the migration to the cloud brings forth a new set of challenges, with security being a top concern. Organizations need to continuously monitor and audit their cloud environments to identify vulnerabilities, detect threats, and ensure compliance with security standards. In today\'s digital landscape, cloud computing has become the backbone of many organizations, offering scalability, flexibility, and cost-effectiveness.
However, with the increasing reliance on cloud services comes the crucial need for robust security measures to protect sensitive data and ensure compliance with industry regulation. Traditional security audits and monitoring processes are often manual, time-consuming, and prone to human error. They lack the agility and real-time insights required to effectively protect cloud assets. To address these limitations, the Automated Reporting and Dashboards for Cloud Security Audits project leverages Artificial Intelligence (AI) and Machine Learning (ML) technologies to revolutionize cloud security management.
This project, "Automated Reporting and Dashboards for Cloud Security Audits," seeks to revolutionize cloud security auditing by harnessing the capabilities of Artificial Intelligence (AI) and Machine Learning (ML). The primary objective is to automate the auditing process within cloud environments, alleviating the time-consuming and error-prone nature of manual audits.
This automation involves collecting data from cloud service providers through APIs, subjecting it to AI and ML analysis to detect anomalies and potential threats, and presenting the findings through user-friendly dashboards and reports. Real-time alerts and notifications are integrated to enable proactive responses to security incidents, while compliance monitoring ensures adherence to industry regulations and internal security policies. The project's ultimate aim is to enhance cloud security, reduce operational overhead, and provide organizations with actionable insights into the health of their cloud infrastructure.
II. RELATED WORKS
[Intelligent Role-Based Access Control Model and Framework Using Semantic Business Roles in Multi-Domain Environments]
This paper  introduces an access control framework, utilizing semantic business roles and intelligent agents in an Intelligent RBAC (I-RBAC) model. Occupational entitlements from real-world roles are integrated, while intelligent agents automate ontology creation. The model's efficiency is validated through implementation results in dynamic multi-domain environments.
2. [A Lightweight Identity - Based Remote Data Auditing Scheme for Cloud Storage]
The paper  introduces an identity-based data auditing (IBDA) scheme for secure cloud storage. The scheme utilizes data owner generated tags and data blocks, while the CSP ensures data integrity by concealing data during the challenge-proof phase, preventing TPA data theft. The proposed scheme's security is proven in the random oracle model, and efficiency analysis demonstrates its superiority over other schemes.
3. [An Efficient Data Auditing Protocol With a Novel Sampling Verification Algorithm]
The paper  elucidates that existing data auditing schemes, following Ateniese etal.'s framework, face challenges like repeated sampling leading to detection delays and data loss risk. This paper presents an efficient sampling verification algorithm that optimizes the scheme, enhancing data integrity in the cloud. The proposed scheme is secure, swift in detecting corrupted blocks, and offers dynamic auditing capabilities.
4. [Privacy-Preserving Cloud Auditing for Multiple Users Scheme with Authorization and Traceability]
The paper  introduces a privacy-preserving cloud auditing scheme for multiple users using certificate less signature technology. It ensures user identity anonymity, collaborative traceability by managers, and prevents denial-of-service attacks. The scheme supports user revocation, maintains security without certificate management complexities, and is proven secure and efficient in analyses.
5. [A Survey on Securing Federated Learning Analysis of Applications, Attacks, Challenges, and Trends]
This paper  discusses Federated Learning (FL) as a privacy-preserving approach for training machine learning models. It outlines vulnerabilities impacting user privacy and model performance, presents mitigation strategies, analyzes FL applications, and highlights the role of security strategies in protecting user privacy and model performance in FL applications.
6. [Machine Learning for Cloud Security: A Systematic Review]
The paper  conducts a Systematic Literature Review (SLR) on the use of Machine Learning (ML) for Cloud security. The SLR covers 63 studies, highlighting Cloud security threats, ML techniques (SVM being prominent), and outcomes. Key findings include 11Cloud security categories, focus on DDoS and data privacy, model efficiency comparisons, and varied evaluation metrics. KDD and KDD CUP’99 datasets are notably popular.
User-Friendly Dashboard: Create a user friendly dashboard to display security audit results, vulnerability reports, remediation progress.
Continuous Monitoring:Implement continuous monitoring to assess the security posture of AWS resources and applications regularly.
AI-Driven Anomaly Detection: Utilize machine learning or AI algorithms to detect security anomalies and suspicious activities in the AWS environment.
Documentation and Reporting: Generate comprehensive reports and documentation for security audits and compliance purposes.
A. Cloud Architecture Setup
This is the initial step where we establish the foundation for the cloud - based infrastructure. It involves launching the services that we will
monitor and setting up the necessary cloud resources, configuring security, and defining the overall structure of our architecture to support the subsequent steps. Some of the main AWS Services we are focusing on will be S3 and EC2 as they are the most popularly used services.
B. Setup AWS CloudWatch to Monitor Metrics
In this step, we will configure AWS CloudWatch, a monitoring service provided by Amazon Web Services, to monitor various performance and operational metrics from our AWS resources in focus. These metrics could include data related to the health and performance of our applications, servers, and other cloud resources which are running in the services.
C. Fetch the data out of AWS using CloudWatch APIs
Once AWS CloudWatch is set up, we will use its APIs (Application Programming Interfaces) to access and fetch the collected metrics and data out of AWS. This involves programmatically querying CloudWatch to extract specific information or time-series data relevant to the monitoring and analytics requirements.
 RUBINA GHAZAL, AHMAD KAMRAN MALIK, NAUMAN QADEER, BASIT RAZA , AHMAD RAZA SHAHID, HANI ALQUHAYZ “Intelligent Role-Based Access Control Model And Framework Using Semantic Business Roles In Multi-Domain environments”
• COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
• University Institute of Information Technology, Pir Maher Ali Shah (PMAS) Arid Agriculture University, Rawalpindi 46300, Pakistan
• Department of Computer Science, Federal Urdu University of Arts, Science, and Technology at Islamabad, Islamabad 44080, Pakistan
• Department of Computer Science and Information, College of Science Al-Zulfi,
Majmaah University, Al Majmaah 11952, Saudi Arabia - January 9, 2020
 LUNZHI DENG, BENJUAN YANG, AND XIANGBIN WANGA “A Lightweight Identity-Based Remote Data Auditing Scheme for Cloud Storage”
• School of Mathematical Sciences, Guizhou Normal University, Guiyang 550001, China
• College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
• School of Big Data and Computer Science,
Guizhou Normal University, Guiyang 550001, China - November 7, 2020
 XUELIAN LI,LISHA CHEN ,AND JUNTAO GAO, “An Efficient Data Auditing Protocol With a Novel Sampling Verification Algorithm”
• School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi 710071, China
• Guangxi Key Laboratory of Cryptography
and Information Security, School of Telecommunications Engineering, Xidian University, Xi’an, Shaanxi 710071, China - July 2, 2021
 XIAODONG YANG, (Member, IEEE), MEIDING WANG, TING LI , RUI LIU1, AND CAIFEN WANG, “Privacy-Preserving Cloud Auditing for Multiple Users Scheme With Authorization and Traceability”
• College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China
• College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China - July 15, 2020
 HELIO N. CUNHA NETO , JERNEJ HRIBAR2, IVANA DUSPARIC ,
DIOGO MENEZES FERRAZANI MATTOS, AND NATALIA C. FERNANDES, “A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends ”
• MídiaCom, PPGEET, Universidade Federal Fluminense (UFF), Niterói 24210-240, Brazil,
• Department for Communication Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
• School of Computer Science, Trinity College Dublin, Dublin 2, D02 PN40 Ireland - 24 April 2023
 ALI BOU NASSIF , MANAR ABU TALIB, QASSIM NASIR, HALAH ALBADANI, AND FATIMA MOHAMAD DAKALBAB “Machine Learning for Cloud Security: A Systematic Review ”
• Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates
• Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
• Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates - January 25, 2021