Recurrent Neural Networks and Deep Neural Networks Based on Intrusion Detection System

Abstract

The computer security has become a major challenge. Tools and mechanisms have been developed to ensure a level of compliance. These include the Intrusion Detection Systems (IDS). The principle of conventional IDS is to detect attempts to attack a network and to identify abnormal activities and behaviors. The reasons, including the uncertainty in searching for types of attacks and the increasing complexity of advanced cyber-attacks, IDS calls for the need for integration of methods such as Deep Neuron Networks (DNN) and Recurring Neuron Networks (RNN) more precisely long-term memory (LSTM). In this submission, DNN and LSTM were used to predict attacks against the Network Intrusion Detection System (NIDS). In this memory, we used four hidden layers for all deep learning algorithms, forty-one layers of inputs and two layers of outputs and with 100 iterations. In fact, learning is kept constant at 0.01 while the other parameters are optimized. After that for DNN, the number of neurons of the first hidden layer was further increased to 1280 but did not give any appreciable increase in accuracy. Therefore, the number of neurons has been set to 1024 and the LSTM we set the number of neurons of all hidden layers to 32. The results were compared and concluded that a three-layer LSTM performs better than all other conventional machine learning and deep learning algorithms.

A Hybrid DNN-RBFNN Model for Intrusion Detection System

“…It measures how well the model can identify true negative cases, indicating its capability to correctly classify non-intrusive in-stances. The results obtained from the Dense Neural Network were consistent with those obtained by [6]. The DNN model demonstrates good performance in terms of specificity and sensitivity for most classes, except for the R2L class where the sensitivity is 0 and an overall accuracy of 0.977.…”

“…One significant advantage of dense neural networks in IDS is their ability to learn complex patterns and relationships from large-scale network data. Zarai, R. et al [6] conducted an experimental study and reported that a dense neural network architecture achieved an accuracy of 94% in detecting various types of network intrusions, outperforming traditional rule-based IDS approaches. Furthermore, the application of deep learning techniques, including the combination of convolutional neural networks (CNN) and dense neural networks, has demonstrated encouraging outcomes in Intrusion Detection Systems (IDS).…”

Intrusion Detection Method of Internet of Things Based on Multi GBDT Feature Dimensionality Reduction and Hierarchical Traffic Detection

“…The LM-BP neural network model was applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm was given. Zarai et al [8] proposed an intrusion detection system based on deep neural network and short-term memory artificial neural network. Li [9] proposed a malicious attack detection method for IoT based on clustering and classification.…”