Bir roman dört yönlü DNN modeli için siber saldırı tespiti ve önlenmesi

In an era marked by escalating cyber threats, traditional security measures struggle to contend with the surging prevalence of cyber-attacks. To address this challenge, we present a groundbreaking solution in the form of the Quad Directional Recurrent Neural Network (Quad-RNN). This novel architecture, featuring four directions of input and output, amalgamates the strengths of Bidirectional RNNs (BRNNs) and Simple RNNs. Our evaluation, conducted on the NSL-KDD and DDoS datasets, establishes the superiority of Quad-RNN over BRNN and Simple RNN counterparts. Demonstrating enhanced accuracy, precision, recall, and F1 score, the Quad-RNN architecture notably diminishes false positives. This research heralds a pivotal advancement in the realm of cyber-attack detection and prevention, addressing the imperative for resilient and adaptive security measures in the face of evolving threats.