Integrating AI and ML for Advanced Threat Detection in Cybersecurity

Cybersecurity requires innovative threat detection methods as complex threats evolve. This study introduces a hybrid model that uses AI and ML to detect sophisticated threats. Our model uses novel approach by integrating convolutional neural networks (CNN) and decision trees to improve threat detection using the UNSW-NB15 dataset, a cybersecurity benchmark. CNN can autonomously acquire hierarchical features from complex data, making the hybrid model ideal for analyzing cybersecurity dataset network traffic patterns. Decision trees interpretability and explicit rule extraction improve decision-making comprehension in addition to deep learning architecture. Our hybrid model experiment yielded stunning 99.23% accuracy. The model’s 98% true positive rate (TPR) shows its ability to identify and classify real threats. The high true positive rate (TPR) indicates a strong ability to detect the majority of actual positive instances, which is crucial in cybersecurity applications where missing real threats can have serious consequences. The proposed model is more applicable to real cybersecurity problems because we use the UNSW-NB15 dataset, which includes many authentic attack scenarios. Our hybrid model can fully understand and adapt to modern cyberthreats thanks to the dataset’s diverse network-based characteristics. This research improves AI and ML implementation in cybersecurity and emphasizes hybrid models’ role in advanced threat detection. The proposed method synergistically combines deep learning and traditional machine learning to boost model performance. As proposed hybrid model shows, combining AI and ML is a powerful way to address the growing challenges of detecting advanced cybersecurity threats. High accuracy and true positive rate (TPR) show that this approach can be used in real-world situations, strengthening cybersecurity systems against changing threats.