DETECTING ZERO-DAY ATTACK USING RECURRENT NEURAL NETWORKS

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying zero-day attacks through a recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of RNNs to learn complex patterns and identify anomalies that may indicate the presence of a zero-day attack. To enhance the model’s ability to generalize and accurately identify novel attack vectors that traditional methods may overlook, we trained it on a dataset that includes both known and synthetically generated zero-day attack signatures. Our methodology involves preprocessing network traffic data to extract relevant features, which are then fed into an RNN architecture. The RNN model was trained on a labeled dataset containing both normal and attack traffic samples, allowing it to learn the fundamental patterns of benign and malicious network activity. The recurrent architecture of the model enables it to retain a memory of previous observations, making it well-suited for identifying deviations from the expected network behavior in real-time. By leveraging the temporal dimension of network traffic data, we enhanced the ability to identify novel threats and bolster the security of computer systems and networks. This approach held the promise for mitigating the ever-evolving landscape of cyber threats and ensuring the integrity and availability of critical digital infrastructure. KEYWORDS machine learning, Recurrent Neural Networks, Zero-day Attack, Anomalies