Application of Probabilistic Data Structures for Cyber Security

Abstract

In the realm of cybersecurity, the exponential growth of digital data presents unprecedented challenges in threat detection, intrusion prevention, and data protection. Traditional methods often struggle to cope with the sheer volume and complexity of cyber threats. Probabilistic data structures offer a promising avenue for addressing these challenges by providing efficient, approximate solutions to key cybersecurity tasks. This paper explores the application of probabilistic data structures in cybersecurity, focusing on their use in threat intelligence, anomaly detection, and intrusion detection systems. We discuss how probabilistic data structures such as Bloom filters, Count-Min Sketches, and HyperLogLog counters can be leveraged to efficiently store, process, and analyse large-scale datasets while minimizing memory and computational overhead. Furthermore, we examine the trade-offs between accuracy and resource utilization in probabilistic approaches and discuss techniques for optimizing performance in real-world cybersecurity applications. Additionally, we highlight the potential of probabilistic data structures for privacy-preserving computations and secure data sharing in cybersecurity settings. Through a comprehensive review of existing literature and case studies, we demonstrate the efficacy and versatility of probabilistic data structures in enhancing cybersecurity defences and mitigating emerging threats in today's digital landscape.