Abstract
In recent years, due to the rapid growth in network technology, numerous types of intrusions have been uncovered that differ from the existing ones, and the conventional firewalls with specific rule sets and policies are incapable of identifying those intrusions in real-time. Therefore, that demands the requirement of a real-time intrusion detection system (RT-IDS). The ultimate purpose of this research is to construct an RT-IDS capable of identifying intrusions by analysing the inbound and outbound network data in real-time. The proposed system consists of a deep neural network (DNN) trained using 28 features of the NSL-KDD dataset. In addition, it contains the machine learning (ML) pipeline with sequential components for categorical data encoding and feature scaling, which is used before transmitting the real-time data to the trained DNN model to make predictions. Moreover, a real-time feature extractor, which is a C++ program that sniffs data from the real-time network traffic and derives relevant data related to the features of the NSL-KDD dataset using the sniffed data, is deployed between the gateway router and the local area network (LAN). Together with the trained DNN model, the ML pipeline is hosted in a server that can be accessed via a representational state transfer application programming interface (REST API). The DNN has revealed outstanding testing performance results achieving 81%, 96%, 70% and 81% for accuracy, precision, recall and f1-score accordingly. This research comprises a comprehensive technical explanation concerning the implementation and functionality of the complete system. Moreover, leveraging the extensive explanations provided in this paper, advanced IDSs capable of identifying modern intrusions can be constructed.
Highlights
The internet has become the most significant resource in this century since it has become incorporated into our daily lives, assisting us in a variety of ways; because of its extraordinary popularity and accessibility, networks in the corporate and personal sectors are exposed to a range of manual and machine-generated attacks
The deep neural network (DNN) was selected as the machine learning (ML) algorithm for this experiment utilizing the conclusion of the previous research that we have done on comparative algorithm analysis for MLbased intrusion detection systems (IDS) using six ML algorithms: DNN, support vector machines (SVM), K-nearest neighbours (KNN), one-class SVM (OCSVM), K-means and expectation–maximization (EM) [3]
This research presents a descriptive technical information about real-time intrusion detection system (RT-IDS) based on DNN ML algorithm
Summary
The internet has become the most significant resource in this century since it has become incorporated into our daily lives, assisting us in a variety of ways; because of its extraordinary popularity and accessibility, networks in the corporate and personal sectors are exposed to a range of manual and machine-generated attacks. Even though firewalls are designed to secure networks, they are incapable of detecting intrusions in real-time. Destructive cyber-attacks pose severe security difficulties, necessitating the need for adaptable and reliable intrusion detection systems (IDS) capable of monitoring policy violations, malicious activity, and unauthorized access in real-time. Intrusion detection can be done in higher efficacy by employing ML algorithms since those have pattern identification capability utilizing the statistical modelling concept based on the past data. SN Computer Science (2022) 3:145 of predicting whether an intrusion or not based on the information captured from the inbound data packet in real-time. The originality of our research is that all the algorithms were constructed using industry-utilized frameworks and libraries to demonstrate the performance of ML in real-world applications.
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