Abstract
The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.
Highlights
Introduction and BackgroundThis article is an open access articleRapid advancements in technology have made the Internet accessible and it is actively used by the majority of people for a plethora of professional and personal tasks.Various sensitive activities including communication, information exchange, and business transactions are carried out using the Internet
When the model was evaluated, the results indicated an accuracy of 99%, outperforming all compared machine learning (ML) methods—Support vector machine (SVM), decision trees (DT), naïve Bayes (NB), random forest (RF), Booster, and logistic regression (LR)
An SAE-based deep learning (DL) approach was applied and the team collected network traffic from a real network and a private network for the evaluation of the model
Summary
Rapid advancements in technology have made the Internet accessible and it is actively used by the majority of people for a plethora of professional and personal tasks. This technique does not cover account applications that do not register their ports with the IANA or applications that use dynamic port numbers Another technique that has been proposed is the payload-based technique, known as deep packet inspection (DPI), where the network packet contents are observed and matched with an existing set of signatures stored in the database [1]. By analyzing past cyber-attacks, the model can be taught to prepare individual defensive reactions These applications of intelligent methods in network security, which is the focal point of this research paper, can be useful in big businesses, organizations, law enforcement agencies, and banks that store sensitive information as well as in personal networks. The last section provides a discussion of the findings and the ideas presented in the papers reviewed and sets out promising research directions
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