A QUICK SORT–BASED FRAMEWORK FOR EFFICIENT THREAT LOG ANALYSIS AND PRIORITIZATION IN CYBERSECURITY SYSTEMS

Abstract

The exponential growth of digital data and the increasing sophistication of cyberattacks have heightened the demand for high-performance cybersecurity frameworks capable of processing and analyzing large-scale datasets efficiently. Traditional research in cybersecurity has largely emphasized the development of advanced detection models and encryption schemes, often overlooking the computational efficiency of data preprocessing, a critical stage that influences detection latency and accuracy. This study presents the Quick Sort–Cybersecurity Framework (QS-CF), an innovative integration of the Quick Sort algorithm into the preprocessing pipeline of cybersecurity analytics systems. The framework leverages algorithmic data ordering to enhance computational throughput and improve real-time intrusion detection efficiency. Using simulated network traffic datasets, the QS-CF achieved a 25% reduction in sorting time, a 1.3× increase in throughput, and a 12% improvement in detection accuracy compared to existing frameworks utilizing Merge and Insertion Sort algorithms. The results demonstrate that classical algorithmic optimizations, when strategically embedded in cybersecurity workflows, can substantially improve system responsiveness, reduce false alerts, and support scalable threat intelligence operations. The QS-CF offers a lightweight, adaptable, and cost-effective model for optimizing security system performance, providing a foundation for future research into privacy-preserving, distributed, and AI-augmented cybersecurity solutions.