Enhancing Cybersecurity in the Digital Age with Machine Learning for Threat Detection and Prevention

Information security cannot be underemphasized today especially with the increased use of Information Communication Technology which requires improved threat identification techniques. In this study, we propose a novel methodology comprising four variants—CyberShield, SentinelGuard, ThreatHunter, and SecureNet—and evaluate their performance against existing approaches in terms of key performance metrics: which are efficiency, precision, recall, F1-score and AUC. In all variants above experiments that we had demonstrated, there is rather more distinctive feature towards threat identifiers guaranteed other than what is presently offered. Interestingly, CyberShield with +3.8% and ThreatHunter with +3.8%, both the highest, are more accurate in terms of classifications of cyper-attacks than other algorithms. Finally, all proposed variants are ranked higher than the current approaches in terms of recall with possible variation between 4.2% and 2.2 for TP rates, meaning these approaches have the potential to minimize false negatives. In addition, the advance variants actually cause slight increase in Precision and F1-Score but with aids to make Precision equivalent to Recall, the achieved performance is relatively higher. What this would do is expose the advantages of the new proposed variants in terms of the degree of influence human behavior and the rate of risk of cyber threats.