Zero Day Attack Prediction Using Improved Deep Neural Network

Zero-Day attacks (ZDA) seriously compromise security by using unidentified weaknesses and leaving systems exposed until they are found and fixed. Usually conventional defensive systems lack real-time detection of such threats, this RESEARCH presents a fresh method based on Improved Deep Neural Network (IDNN) architecture for ZDA prediction. Combining a multi-layered deep learning framework with ideal hyperparameter tuning methods improves feature extraction and anomaly detection in the proposed model. The IDNN learns patterns suggestive of possible ZDAs using a large dataset of network traffic, therefore allowing proactive identification before major harm occurs. Using Attention Mechanisms and Residual Learning to manage the complexity of big-scale data and reduce false positives helps the model be more efficient. Extensive studies show that the suggested model provides a strong solution for real-time ZDA avoidance because it beats current techniques in accuracy, precision, and recall. This study offers a vital first step towards more strong and robust cyber security systems able to resist developing risks.