From Data Packets to Secure Networks with Advanced Analytical Frameworks for a Comprehensive Understanding of Threat Landscapes
Srilakshmi K H, Akshay Kumar, Vaishali Singh · 2023
As the current digital world becomes increasingly linked, network security becomes more important. The purpose of this research was to create a unique technique that, by leveraging more advanced analytical frameworks, has the potential to improve computer network security. The solution we provide makes an attempt to provide consumers with a complete overview of the current security condition. To do this, machine learning, behavioral analysis, and real-time threat intelligence feeds are employed in unison. We are better able to mitigate risk now that we have this information. With the ability to adapt to continually changing hazards, this technique enables accurate threat identification in real time. Because of its scalability, the system is well-suited for usage with massive networks, and the adaptive response mechanisms it provides enable optimal resource allocation and the mitigation of the impacts of disruptive occurrences. This article contrasts the proposed approach with more established approaches, indicating the superiority of the latter. Our approach has a 98% detection rate and a 1% false positive rate, making it exceptional at identifying threats while lowering the number of unnecessary alerts. It presently has a detection rate of 98%. It may be used in a variety of network configurations because of its rapid reaction time, good scalability, and agility. Furthermore, it provides a full awareness of risk situations, which encompass both known and previously unknown hazards. The need to use advanced analytical frameworks to enhance network security in the face of shifting threat environments is highlighted in this study.