Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms

Due to the Internet of Things and the digitalization revolution, there is nowadays an overwhelming amount of cybersecurity information in the tech world. Dealing efficiently with cyber deviations and attacks is an emerging challenge to any cyber security industry all over the world. Traditional security solutions can no longer address today's security threats because of the rapid proliferation of various cyber-attacks and threats. Artificial intelligence understanding, especially machine learning technology can make a security system up to date by analyzing its security data so that the safety mechanism can be upgraded continuously and so that it can remain programmatic and automated. This study provides a comprehensive overview of existing machine-learning techniques inspired by the notion of AI. We explore its capabilities of advanced information analytics and management by deriving meaningful conclusions out of cyber data in Cybersecurity. The performance of the proposed method will be compared against that of a standard machine learning technique, Support Vector Machine (SVM), using cross-validation. This scheme is known as a Secured Learning based Data Evaluation Scheme (SLDES). Based on its analysis of past incidents and knowledge summaries from experts, the proposed AI approach SLDES (Shift Left Diverse Experiments and Simulations) would change or reform newly appearing sections as it retrieves recent events. Last but not least we present our research about the future of artificial intelligence in information security etc. Machine learning and its related approaches are being investigated and used in cyber defense, and the focus of the described section is to evaluate the current and future uses of machine learning in cyber defense, for the scheme proposed the section presented the way to demonstrate how to effect the proposed scheme is and the result metrics are plotted in histogram form To derive such insights and information necessary for designing countermeasures for such attacks, even as stated in, a thorough data analysis is important. The main aim of this research is to establish that clients can develop a safe situation to protect their electronic data from intruders.

The growing reliance on cloud platforms has introduced a heightened need for robust cybersecurity measures. Traditional security methods, while effective, struggle to keep pace with evolving cyber threats. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools in enhancing cloud security, offering dynamic and automated approaches to threat detection, prevention, and response. This paper explores the critical role of AI and ML in addressing key cybersecurity challenges within cloud environments. It reviews various AI/ML techniques such as supervised learning, deep learning, and reinforcement learning, demonstrating their effectiveness in identifying vulnerabilities and responding to sophisticated cyberattacks. Through a discussion of real-world case studies, the paper highlights the advantages of integrating AI/ML models in cloud security architectures. Additionally, the paper identifies existing limitations, such as adversarial attacks on AI systems and ethical concerns related to data privacy. Finally, it outlines future directions for leveraging AI/ML in creating proactive, adaptive, and secure cloud environments.

Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in industrial automation and cybersecurity has become increasingly vital as industries transition towards smarter and more efficient systems. With the rapid advancement of technology, AI and ML are playing a key role in optimizing operations, enhancing safety, and improving overall system performance. This paper explores the applications of AI and ML in industrial automation, focusing on their transformative impact on manufacturing, process control, and predictive maintenance. Additionally, the paper highlights the critical role these technologies play in enhancing cybersecurity frameworks, particularly in the context of industrial control systems and critical infrastructure. The combination of AI and ML allows for real-time decision-making, anomaly detection, and the mitigation of security risks in an increasingly interconnected industrial environment. The findings suggest that AI and ML are not only optimizing operational efficiency but also providing advanced threat detection mechanisms to combat sophisticated cyber threats. This paper presents a detailed review of these technologies' capabilities and challenges, offering insights into their future potential in industrial settings.

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.

An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models

As Artificial Intelligence (AI) shows excellent performances, electric power industry also applies AI to various fields. Even though a lot of data and infrastructures for data analysis are prepared, AI experts in the industry are insufficient. Automated machine learning can be a solution in the industry to apply the excellence of AI to many fields despite the shortage of professionals. Recently, several automated machine learning services have shown good performances on general purpose. However, due to the specificity of data, they does not perform well in the electric power industry. Therefore, we develop an automated machine learning pipeline system based on beam search especially for electric power industry. The proposed system is applied to three real-word problems, one for each of regression, classification, and text classification. The significance of our work is first to apply automated machine learning to electric power industry with high performance. The performances of the models to predict hourly peak power demand and to detect illegal use of electricity for Bitcoin mining are improved by 3.42% and 3.23% respectively compared to the existing models. Moreover, our work shows 95.23% accuracy in classification of the questions’ indents of chatbot, which gives the possibility to replace the existing model.

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

Modern machine learning services and systems are complicated data systems --- the process of designing such systems is an art of compromising between functionality , performance , and quality . Providing different levels of system supports for different functionalities, such as automatic feature engineering, model selection and ensemble, and hyperparameter tuning, could improve the quality, but also introduce additional cost and system complexity. In this paper, we try to facilitate the process of asking the following type of questions: How much will the users lose if we remove the support of functionality x from a machine learning service? Answering this type of questions using existing datasets, such as the UCI datasets, is challenging. The main contribution of this work is a novel dataset, MLBench, harvested from Kaggle competitions. Unlike existing datasets, MLBench contains not only the raw features for a machine learning task, but also those used by the winning teams of Kaggle competitions. The winning features serve as a baseline of best human effort that enables multiple ways to measure the quality of machine learning services that cannot be supported by existing datasets, such as relative ranking on Kaggle and relative accuracy compared with best-effort systems. We then conduct an empirical study using MLBench to understand example machine learning services from Amazon and Microsoft Azure, and showcase how MLBench enables a comparative study revealing the strength and weakness of these existing machine learning services quantitatively and systematically. The full version of this paper can be found at arxiv.org/abs/1707.09562

Study question Can an image recognition model, utilising automated deep learning, be trained to diagnose and classify the severity of adenomyosis on ultrasound? Summary answer The automated deep learning model developed in this study can accurately diagnose and classify the severity of adenomyosis from ultrasound images. What is known already Adenomyosis is prevalent across all stages of life, starting from adolescence to perimenopause, however it remains underdiagnosed. The diagnosis and classification of adenomyosis are essential for correlating with symptom severity and counselling about disease prognosis, management options and long-term impact. Diagnosing and classifying the severity is a time-consuming and complex process that depends on the operator’s experience. Machine learning (ML) has an enormous potential to assist healthcare professionals with image classification tasks. It can directly process and automatically learn mid to high-level abstract features acquired from images using a deep architecture model without requiring the manual definition of features. Study design, size, duration This observational cohort study utilised ultrasound images from four assisted conception units in the United Kingdom from February 2023 to February 2024. Labelled images were used as input for training and testing of the model. The complete dataset was manually split into 9:1 ratio; 90% of all images were used as training dataset (80% for training and 10% for validation) and 10% as test dataset. Ethics approval was obtained from the Institutional Review Board (IRB). Participants/materials, setting, methods An automated ML tool (Vertex AI Vision) was trained and tested to diagnose and classify adenomyosis severity. Two investigators, experienced in gynaecological ultrasound scanning, labelled two and three-dimensional images into four categories: normal uterus, mild, moderate, and severe adenomyotic uterus, as per standardised ultrasound criteria. The labelled images were formatted and uploaded on Google Cloud to train the neural network based on pattern recognition. The model’s performance was determined for an internal test dataset. Main results and the role of chance A total of 3,500 ultrasound images were obtained from 3,194 patients, and the internal test dataset consisted of 350 ultrasound images from 292 patients. An overall and per-category score threshold of 0.5 was adopted to provide a balance between reasonable precision and recall during the evaluation. The investigator’s categorization was used as the reference standard to evaluate the performance of the automated machine-learning model. The model’s overall accuracy for diagnosing and classifying adenomyosis, as determined by the area under the precision-recall curve, was 80.24% across all score thresholds. The overall precision was 83.00%, and recall was 65.75% at a confidence threshold of 0.5. The precision and recall were 91% and 78% for normal uterus, 67% and 53% for mild adenomyosis, 86% and 68% for moderate adenomyosis, and 88% and 64% for severe adenomyosis. The algorithm achieved a high accuracy for the internal dataset. The time taken by a trained healthcare professional to label and process 3,500 images into 4 categories was 70 hours. The time taken by an untrained automated ML tool to get trained, process, and label the images into 4 categories was 4 hours, saving 66 hours per healthcare professional. Limitations, reasons for caution Due to the inherent nature of non-invasive investigations for the population attending the assisted conception unit, histological confirmation of adenomyosis was not possible. In addition, the model’s performance has been tested using an internal test dataset and needs further validation using an external test dataset from a different population. Wider implications of the findings This novel study leverages the power of artificial intelligence to develop an automated machine learning model for diagnosing and classifying adenomyosis from ultrasound scans. This tool can potentially enhance the diagnostic capacity and efficiency of healthcare professionals and empower women by providing better care through early and uniform diagnosis. Trial registration number No

Deep learning's fast integration into cloud computing services enables businesses to perform scalable AI model training and real-time analysis in diverse sectors. The combination of deep learning with cloud platforms results in important security vulnerabilities that stem from adversarial threats and data breaches as well as model inversion vulnerabilities and unauthorized system intrusions. Data infringement combined with weakened model reliability and non-compliance requirements require cloud AI systems to adopt more robust security controls. Experts analyze security issues facing deep learning models in the cloud through an assessment of attacks which manipulate model inputs, pollute training data and exploit APIs and create insecurity across multiple cloud user environments. The research compares encryption protocols and federated learning capabilities and access control systems and differential privacy features of AWS, Google Cloud, Microsoft Azure, and IBM Cloud. The assessment evaluates regulatory compliance requirements of GDPR HIPAA and CCPA in order to detect security governance gaps for AI systems. Research outcomes show that Amazon Web Services along with Google Cloud deliver excellent encryption features as well as anomaly detection solutions yet Microsoft Azure stands out through its advanced federated learning functions. The security features aimed at AI operations are insufficient in IBM Cloud which demonstrates divergent approaches to security implementation across platforms. Homomorphic encryption and differential privacy have progressed but practical use remains restricted by high operational costs and regulatory uncertainty as well as attacks by adversaries. The distributed AI training method known as federated learning protects against poisoning attacks but still needs improved protection mechanisms to remain secure. The proposed solution for safe and privacy-compliant AI implementation uses a security system that joins sophisticated cryptographic methods with adversarial attack prevention mechanisms along with methods for protectively training AI. Future research needs to improve encryption speeds as well as strengthen federated learning resistance to attacks and create AI-based compliance systems which will address new cybersecurity threats against cloud-based AI platforms

The rapid advancement of cloud computing has revolutionized how businesses operate, offering scalable resources and cutting - edge technologies. This paper presents a comparative study of the three leading cloud providers?Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) - focused on their integration of Artificial Intelligence (AI) with DevOps practices. We evaluate their AI service offerings, DevOps toolchains, and the seamless integration of these technologies to enhance automation, scalability, and reliability. The analysis provides insights into the strengths and weaknesses of each provider, helping organizations make informed decisions on their cloud strategy. By analyzing the AI capabilities, DevOps toolchain integration, and overall performance of these cloud providers, this study aims to provide insights into selecting the most suitable platform for AI - driven DevOps workflows. The comparative analysis will focus on factors such as AI services, scalability, automation capabilities, and integration with DevOps tools to help organizations make informed decisions when choosing a cloud provider for their AI and DevOps initiatives.

The integration of machine learning (ML) models with cloud computing has transformed the landscape of predictive analytics, offering scalable, efficient, and flexible solutions for organizations. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure enable businesses to deploy and manage complex ML models without the need for extensive on-premise infrastructure. However, optimizing these ML models for performance and cost-efficiency in cloud environments presents unique challenges, including resource management, latency, scalability, and data security. This paper focuses on strategies to optimize machine learning models specifically for predictive analytics in cloud environments. It explores key techniques such as auto-scaling, model compression, and hyperparameter tuning, which are critical for improving the accuracy and speed of predictions while minimizing computational costs. The research also examines advanced tools such as containerization, serverless computing, and cloud-native services that further streamline the deployment and management of ML models. In the Indian context, where cloud adoption is growing rapidly, optimizing ML models is crucial for businesses across various sectors, including finance, healthcare, and e-commerce. By leveraging cloud-based ML solutions, Indian companies can enhance their predictive analytics capabilities, driving smarter decision-making and operational efficiency. This abstract presents an overview of how optimized machine learning models can unlock the full potential of predictive analytics in cloud environments, leading to better business outcomes. Through case studies and practical applications, this paper provides actionable insights into the best practices for optimizing ML models in a cloud-based setting.

Massively using conventional machine learning requires many people to understand the process. However, the design of an automatic machine learning system using the Pycaret Library can make it easier for users by reducing some of the operations of machine learning in general. This automated machine learning design is managed in the Power BI application so that the display form can be better understood and easily visualized. By carrying out several method stages in the form of literature studies, data collection, designing classification methods using conventional machine learning and AutoML in Power BI, analysis of results, and reporting/publication. The result of comparison of machine learning and Auto ML: In machine learning, 98 lines of line code are used, while in AutoML, eight lines of code are used. Another advantage derived from using AutoML in PowerBI is the feature to be able to visualize processed datasets so that they can obtain information in the form of diagrams, charts, and others. In testing using the breast cancer dataset, the use of Machine Learning is considered better based on the score obtained. Machine Learning received the highest Accuracy using GaussianNB is 0.94. In testing using the Diabetes dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using CatBoost Model is 0.777. In testing using the heart failure dataset, the use of AutoML is considered better based on the score obtained. AutoML received the highest Accuracy using Random Forest Model with a score of 0.85.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in Quality Assurance (QA) automation is transforming software testing by enhancing accuracy, efficiency, and adaptability. AI-driven techniques, such as intelligent test case generation, self-healing automation scripts, and predictive analytics, enable faster defect detection and reduced maintenance efforts. ML models can analyze historical test data to optimize test execution, prioritize critical test scenarios, and identify patterns in software defects. Despite challenges such as data dependency, integration complexity, and the need for specialized expertise, AI and ML continue to drive innovation in QA automation. This paper explores the key applications, benefits, challenges, and future prospects of AI and ML in QA automation, highlighting their role in improving software reliability and development agility.