Detecting DOS Attacks Using a Hybrid CNN-LSTM Model
Maryam Salehi, Alireza Yari · 2024
Given the increasing reliance of critical infrastructure on information and communication technology, the timely detection and prevention of attacks have become paramount. Extensive research in field of neural networks and deep learning being used due to the being compatible on large datasets has been devoted to this area. Previous studies have shown that combining neural network algorithms, particularly the Convolutional Neural Network and long short-term memory, significantly improve attack prediction compared to either CNN or LSTM models individually.This study introduces a novel parallel model that integrates these two networks. The parallel networks receive two inputs simultaneously, one for sequential processing by the CNN neural network and the other for processing by the LSTM network. Each model processes the data independently, and their outputs are merged to produce the final result. The integration of CNN and LSTM models in parallel, which extract unique features and temporal characteristics from input data through convolutional and recursive layers at the same time, achieved higher accuracy than previous studies. By utilizing the well-known NSL_KDD dataset, the proposed model in this study achieved an accuracy of 99.45% in detecting Denial of Service attacks, surpassing previous studies on the same dataset that achieved a maximum accuracy of 99.20%.