Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification accuracy. The suggested study applies the encoding strategy which begins with the transformation of network traffic into genetic codes and then analyses the RNN-based technique. To be more exact, the RNN-based IDS is shown to be more efficient than the previous approaches due to the 98.2% rate of successful detection and 9.8% false alarm rate as well as 97.4 percent rate of correct attack detection. The mathematical modeling of the RNN-based system defines the Detection Rate (DR) and False Alarm Rate (FAR) and accuracy as key performance indicators, which proves the effectiveness of the mathematical model. The RNN identifies high-level genetic code patterns that allow the system to protect against the brute force attacks and Denial-of-Service (DoS) attacks and information-seeking attacks and botnet operations in real time. Learning by RNNs proposed system yields improved detection and allows reliable strategic adaptations that are beyond the traditional methods. Trained RNNs will be connected to CNNs to create hybrid structures and fine-tune data pre-processing modes to maximize the efficiency of the functions. The new method demonstrates that collaboration between various areas of expertise can result in successful means of resolving issues of cybersecurity.