Strengthening Threat Detection and Mitigation Strategies in Cybersecurity with Artificial Intelligence

Industry organizations and government bodies need to have state-of-the-art cybersecurity as cyber threat actors create ever more complex methods. These changes could introduce new cyber threats; thus, a security threat model has to account for them. Many attacks have been cropping up on the Internet as it has grown. This study focuses on enhancing threat detection capabilities in cybersecurity using deep learning models, particularly targeting cyberattacks. GRU model is employed for threat detection, leveraging advanced data preprocessing techniques like feature selection, label encoding, and Min-Max normalization to optimize performance. The GRU model surpasses other ML models like LSTM, CNN, and Naïve Bayes with an astounding accuracy of $99.99 \%$, a high precision of $99 \%$, a recall of $99.99 \%$, and an F1score of $99.99 \%$. Experiment findings show that the model can detect cyber risks with low false-positive and negative rates. The paper highlights the possibility of using GRU-based models for cybersecurity applications that need real-time threat identification.