Article
Version 1
This version is not peer-reviewed
Deep Learning for Intrusion Detection Systems (IDS)
Version 1
: Received: 4 September 2024 / Approved: 5 September 2024 / Online: 6 September 2024 (05:44:56 CEST)
How to cite: Olaoluwa, F.; Potter, K. Deep Learning for Intrusion Detection Systems (IDS). Preprints 2024, 2024090411. https://doi.org/10.20944/preprints202409.0411.v1 Olaoluwa, F.; Potter, K. Deep Learning for Intrusion Detection Systems (IDS). Preprints 2024, 2024090411. https://doi.org/10.20944/preprints202409.0411.v1
Abstract
In the rapidly evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a critical role in safeguarding networks and systems from unauthorized access and malicious activities. Traditional IDS approaches, relying heavily on predefined rules and signature-based detection, often struggle to keep pace with the dynamic nature of modern cyber threats. Deep learning, with its ability to automatically learn complex patterns and representations from large datasets, offers a promising solution to this challenge. This paper explores the application of deep learning techniques in enhancing the effectiveness and accuracy of IDS. By leveraging advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, deep learning-based IDS can detect both known and unknown (zero-day) attacks with higher precision. We discuss the advantages of deep learning in IDS, including its capability to handle large-scale data, adaptability to evolving threats, and reduced dependency on human intervention. Furthermore, this paper reviews recent advancements in deep learning for IDS, highlights the challenges associated with their implementation, and suggests future directions for research to overcome these hurdles. The integration of deep learning into IDS frameworks represents a significant step forward in the development of intelligent and autonomous cybersecurity systems.
Keywords
Deep Learning; Intrusion Detection Systems (IDS); cybersecurity systems
Subject
Computer Science and Mathematics, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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