Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection

This paper investigates the effect of heuristic algorithms on cybersecurity hazard detection and mitigation. To be able to do so, the authors analyze the performance of two heuristics-based algorithms—the simple Heuristic set of rules (SHA) and greedy Heuristic set of rules (GHA)—on 4 distinct datasets. The datasets contain malicious and non-malicious packets. Effects of the experiments show that each algorithms exhibit extraordinarily correct predictions of malicious packets while as compared to the floor-reality labels, and showcase an excessive level of detection accuracy in detecting threats. The SHA set of rules tested the strongest performance, achieving an accuracy of as much as 95 and ninety-seven percentage on datasets. The authors further take a look at that once both algorithms are utilized in aggregate, the overall performance progressed. The research findings may be generalized in further contexts and assist the notion that heuristics algorithms may be effective in threat detection and mitigation.