An Approach for Identifying Network Intrusion in an Automated Process Control Computer System

Abstract

Technology and networks have improved significantly in recent decades, and Internet services are now available in almost every business. It has become increasingly important to develop information security technology to identify the most recent attack as hackers are getting better at stealing information. The most important technology for security is an Intrusion Detection System (IDS) which employs machine learning and deep learning technique to identify network irregularities. To detect an unknown attack, we propose to use a new intrusion detection system using a deep neural network methodology which provides excellent performance to detect intrusion. This research focuses on an automated process control computer system that recognizes, records, analyzes, and correlates threats to online safety. In addition, two different methods are used to detect an attack (the binary classification and the multiclass classification). One of the most promising features of the proposed technique is its accuracy (98.99 percent with the multiclass classification and the binary classification). The proposed method's first step creates a model for a multiclass intrusion detection system based on CNN. FOA (Fruit Fly Optimization Algorithm) is used in the process's pre-training phase to address the class imbalance issue. Each batch is obtained during the training process using the resampling method following the resampling weights, which are the results of the pre-training procedure.

An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity

“…PROPOSED METHODOLOGY The suggested method uses a neural network to analyze a CSE-CIC-IDS2018 dataset made up of modern attack vectors. This work created a method for identifying anomalous network traffic using deep learning techniques based on a hybrid CNN and LSTM model with FOA [13] [14]. Notably, the timestamp values of each network flow are included, making the dataset better realistic.…”