In the ever-evolving landscape of cybersecurity, the application of game theoretic models has emerged as a powerful and innovative approach to enhance our understanding and management of cyber threats. This abstract explores the application of a variant of game theoretic models within the context of a Cyber Threat Intelligence (CTI) framework. With the proliferation of cyber-attacks targeting critical infrastructure, sensitive data, and national security, it has become imperative to develop proactive and adaptive strategies for threat detection, mitigation, and response. The variant of game theoretic models discussed in this abstract departs from traditional game theory by incorporating elements of dynamic adaptation and machine learning. This adaptation enables the framework to model and analyze the intricate and rapidly changing interactions between threat actors and defenders in real-time, thereby providing a more accurate representation of the evolving threat landscape. By leveraging machine learning algorithms, the model can continuously learn and adapt to new threats and tactics, making it a versatile tool for CTI. This abstract also explores the practical applications of the variant model in various aspects of cybersecurity, including threat actor profiling, vulnerability assessment, and decision support for incident response. By considering the strategic motivations and behaviors of threat actors, organizations can make informed decisions regarding resource allocation, risk assessment, and security investments. The integration of this variant of game theoretic models into CTI holds great potential to revolutionize our approach to cybersecurity, enabling organizations to stay one step ahead of adversaries. As the digital world becomes increasingly complex, the ability to predict, mitigate, and adapt to cyber threats is crucial for safeguarding critical assets and ensuring the resilience of digital infrastructure. This paper highlights the significance of this innovative approach and its potential to shape the future of cyber threat intelligence and cybersecurity practices.

Differential privacy has emerged as a prominent framework for safeguarding individual privacy in the context of data analysis and statistical computations. With the proliferation of data-driven applications and the necessity to share sensitive information while preserving confidentiality, the study of composition algorithms in differential privacy becomes increasingly vital. This paper presents a critical examination of the various composition techniques employed to combine multiple privacy-preserving computations while upholding the integrity of the overarching privacy guarantees. The paper begins by outlining the fundamental principles of differential privacy and its significance in contemporary data-centric environments. It subsequently delves into a critical analysis of sequential composition, parallel composition, and advanced composition theorems. The inherent strengths and limitations of each technique are scrutinized; emphasizing their practical implications in preserving privacy across different scenarios. The study extends beyond basic composition strategies to explore intricate facets of composition algorithms. Post-processing and renewal-based composition methodologies are evaluated in the context of preserving privacy amidst evolving data landscapes. The paper also investigates adaptive data analysis and hierarchical composition, elucidating their roles in addressing complex privacy challenges that arise in multi-agent and multi-level environments. Through a meticulous survey of literature and practical implementations, this paper unveils the nuanced interplay between composition algorithms and differential privacy mechanisms. It highlights the need for judicious application of composition techniques while considering the dynamic interactions between privacy parameters, data characteristics, and adversarial behaviours. Furthermore, the paper underscores the significance of incorporating advanced composition theorems to yield more precise privacy bounds, providing a comprehensive understanding of the intricacies involved.