MLP-Based Defense Mechanisms Against Cyber Crime: Insights from Dataset-Driven Attack Prevention Strategies

In today's digital world, cybercrime is a constant danger that calls for creative security strategies to lessen its effects. This work examines how well dataset-driven attack prevention tactics work to stop cyberattacks when applied to protection measures based on MLP technology. The design and training process of MLP models are explained theoretically, and practical implementations demonstrate how well they function in identifying malware, phishing scams, and network breaches. By highlighting the benefits of combining MLP-based protections with more established security technologies, integration with current cybersecurity frameworks strengthens defensive coverage against changing threats. Analysis shows that MLP models are effective at spotting harmful activity early on, which strengthens a cybersecurity posture. Further improvements are also shown by the suggested work, which has better performance metrics such as 96% accuracy, 0.94 precision, 0.95 recall, 0.95 F1 score, 0.04 false positive rate, and 0.05 false negative rate. The results highlight how important it is to use MLP-based security mechanisms to strengthen cyber defenses and lessen the effects of cybercrime. To further improve MLP-based defensive tactics and guarantee strong resistance against new cyberthreats, cooperation and research are essential.