Enhancing Cybersecurity with Machine Learning to Strengthen Defenses Against Phishing Threats

Increasing of cyber threats, Phishing attacks are to be remain one of the most continuing challenges in cyber security. Machine Learning is wondering in detection of cyber threats. This Research explored the different machine learning techniques which are helpful for detecting threats. Leveraging the different models such as Ensemble models and Deep Neural Networks. A comparative study of algorithms was conducted using the Real-time phishing datasets, and are been highlighted by their effectiveness in predicting malicious activities. The random forest model consistently outperformed comparing to the other models, achieving of 97.3% with Large Dataset and with small Dataset, it has achieved 98.5% with eminent features, while ensemble models demonstrated greater adaptivity to dynamic phishing patterns. This study also showed the importance of feature selection techniques, which includes domain heuristics and behavioral analysis, to improve the detection capabilities with the Limitations provided. Integrating real time threat intelligence and self-learning AI system are have discussed as keys for enhancing cybersecurity frameworks. Additionally, the Emerging trends such as Quantum Cryptography and adversarial machine learning are explored to future proof security defenses. The findings are contributing to the ongoing efforts to create robust, adaptive phishing detection, and robust mechanisms, and ensuring improved security for digital ecosystems.