Abstract— Identity and Access Management (IAM) is a pivotal element in modern cybersecurity strategies, enabling organizations to manage user identities and control access to digital resources securely. This paper focuses on Microsoft’s comprehensive suite of IAM solutions, emphasizing the innovative capabilities of Microsoft Entra ID as a central component within its ecosystem. The discussion spans Entra ID’s role in IAM, Multi-Factor Authentication (MFA), Conditional Access policies, and Microsoft Entra Privileged Identity Management (PIM). This research explores the dynamic landscape of IAM in the context of Microsoft security, addressing challenges and opportunities posed by contemporary cybersecurity threats and evolving work environments. Key topics include the integration of IAM solutions with Microsoft 365 services, the impact of remote work on identity governance, and the effective implementation of conditional access policies to enhance security without compromising user experience.
Furthermore, the paper investigates the role of IAM, specifically Microsoft Entra ID, in meeting security and compliance requirements. It delves into data protection, threat intelligence, and compliance reporting within the Entra ID framework. As organizations navigate hybrid environments that span on-premises and cloud infrastructures, the research examines the intricacies of managing user authentication in such diverse setups. The study concludes by emphasizing the importance of adapting IAM strategies continuously to address evolving cybersecurity challenges within Microsoft’s security ecosystem. By referencing the latest Microsoft Entra ID documentation and industry best practices, this research contributes to a deeper understanding of the significance of IAM, specifically Microsoft Entra ID, and its practical implications for organizations seeking robust identity and access management solutions.
Keywords— IAM, Microsoft Entra ID, MFA, Conditional Access, Identity Governance
Abstract—At present, embedded systems operate in every environment on the planet. Many complex applications with previously unheard-of capability have been made possible by recent technological advancements. Regardless of the ability to shield critical data from malevolent attacks, security and privacy remained a prevalent concern for these systems. These worries are warranted since horrifying tales about embedded systems are told by the past security lapses and their aftermath. With the development of technology, the attacks are gradually changing and becoming more sophisticated. As a result, fresh approaches to security implementation in embedded systems are needed. This paper uses a case study to illustrate how security features are integrated into fingerprint biometric systems during the requirements analysis stage and maintained throughout the embedded system life cycle. A comparative analysis is provided between different biometric technologies, including face, fingerprint, iris, palm print, hand geometry, gait, signature, and keystroke. In order to provide more precise safety requirements or functions, the goal of this work is to analyze, break down, and convert the risks and countermeasures found during the requirements analysis utilizing the abuse case. Additionally, by examining the system requirements and outlining the primary procedures for biometric system protection in this article, we have demonstrated how security features can be incorporated into the biometric fingerprint system.
Keywords—Information leaks, fingerprints, abuse cases, countermeasures, and threats
Abstract– Sentiment analysis is a recent phenomenon that involves comprehending individuals’ feelings across many situations in their everyday existence. The use of social media data, including textual information as well as emoticons, emojis, and other visual representations, will be employed throughout the whole of the process, including the analysis and classification operations. Previous study conducted several experiments utilising Binary and Triple Classification methods; however, it has been shown that multi-class classification offers more accurate and precise categorization. The data would be partitioned into many subcategories according to the polarity in a multi-class classification. Supervised Machine Learning Methods will be used throughout the categorization operation. Sentiment levels may be monitored or analysed using social media. This study investigates the use of artificial intelligence techniques to analyse sentiment in social media data with the purpose of understanding or detecting it. The poll used visual campaigns to analyse social media data, which included words, emoticons, and emojis, for the purpose of emotion detection using diverse machine learning methods. The SL-RBF Algorithm demonstrates higher accuracy in sentiment analysis.
Keywords– Sentiment Analysist, Radial Basis Function, Accuracy, Multi Class Classification, Precision.
Abstract– Software failure prediction and proneness have long been considered critical challenges for the IT industry and software professionals. Conventional approaches may detect software defects inside an application, but they need previous knowledge of problems or faulty components. Automated software fault recovery models enable the programme to significantly predict and recover from software issues via the use of machine learning techniques. This feature reduces mistakes, time, and money while also making the programme run more smoothly. A software defect prediction development model was given using machine learning techniques, which could enable the programme to carry out its intended purpose. A range of optimisation evaluation benchmarks, including as accuracy, f1-measure, precision, recall, and specificity, were also used to evaluate the model’s performance. The FPRNN-HTF (Forward Pass RNN with Hyperbolic Tangent Function) deep learning prediction model is based on convolutional neural networks and its hyperbolic tangent functions. The evaluation process showed how well CNN algorithms were used and how accurate they were. Additionally, a comparative metric is used to assess the proposed prediction model in comparison to other approaches. The collected data showed how well the FPRNN-HTF approach performed.
Keywords– FPRNN-HTF (Forward Pass RNN with Hyperbolic Tangent Function), precision, recall, specificity, F1-measure, and accuracy.
Abstract– Thyroid Disease is very common problem that affects lot of people around the world. That is why, it is important to diagnose this disease accurately and effectively. But it is not that, this disease has not been diagnosed yet. It is been done by doctors for long time. To facilitate doctors, I developed a deep learning model to identify Thyroid disease accurately. This is accomplished by providing dataset which was provided by Garavan Institute, located in Sydney . The dataset comprises features related to thyroid conditions, where thyroid positivity being labelled as 0 and negativity as 1. This model involves densely connected layers which employs Rectified Linear Unit (ReLU) activation function. Techniques such as dropout and batch normalization are incorporated to enhance generalization and preventing overfitting. The Adam optimizer is utilized for gradient descent. The training process is monitored using metrics such as accuracy and loss, and model’s performance is also evaluated using a separate test set. Standard evaluation metrics such as accuracy, precision, recall and F1_score is computed to access the model’s predictions. We can figure out how good our computer is at recognizing different thyroid conditions with the help of confusion matrices. A classification threshold is explored to convert continuous predictions into binary outcomes. This model involves preprocessing steps such as feature scaling with the help of Min Max Scaler. Data Augmentation is done to enhance the model’s ability. This model includes deep learning techniques such as neural networks, optimization algorithms and evaluation metrics to develop a robust binary classification model for thyroid disease diagnosis.