A SEMI-SUPERVISED FRAMEWORK FOR NETWORK INTRUSION DETECTION USING GANS

Abstract

This invention discloses a novel semi-supervised network intrusion detection system that utilizes Generative Adversarial Networks (GANs) to improve the accuracy and efficiency of detecting network intrusions, particularly novel or rare attacks. The system leverages both labeled and unlabeled data, minimizing the need for extensive manual labeling and enhancing the system’s ability to adapt to evolving threats.

Specification

Description:FIELD OF THE INVENTION
This invention relates to the field of cybersecurity, specifically network intrusion detection and prevention. It utilizes Generative Adversarial Networks (GANs) and a semi-supervised learning approach to improve the accuracy and efficiency of detecting network intrusions, particularly novel or rare attacks.
BACKGROUND OF THE INVENTION
Network security is of paramount importance in today's interconnected world. The increasing sophistication and frequency of cyberattacks pose a significant threat to individuals, organizations, and critical infrastructure. Traditional network intrusion detection systems (NIDS) often struggle to effectively identify and respond to these threats, particularly novel or rare attacks. These challenges stem from several key limitations of conventional NIDS approaches:
Data Scarcity: Fully supervised machine learning models for NIDS require large, labeled datasets of network traffic data, which are often difficult and expensive to acquire and maintain. Manually labeling network traffic data is a time-consuming and labor-intensive process.
Inability to Detect Novel Attacks: Traditional NIDS, especially those relying on signature-based or anomaly-based detection methods, struggle to detect new or previously unseen attacks, making them vulnerable to zero-day exploits and other advanced persistent threats.
High False Positive Rates: Many NIDS generate a significant number of false positives, overwhelming security personnel with alerts and potentially leading to alert fatigue and delayed responses to genuine threats.
Computational Resource Intensive: Fully supervised NIDS can demand significant computational resources, especially those employing complex machine learning algorithms, making them impractical for deployment in large-scale networks or resource-constrained environments.
This invention addresses these limitations by introducing a novel semi-supervised framework for network intrusion detection that leverages the power of Generative Adversarial Networks (GANs). The use of GANs and semi-supervised learning enables the system to learn from both labeled and unlabeled data, reducing the reliance on large labeled datasets while significantly improving the detection of novel or rare attacks.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention presents a novel semi-supervised framework for network intrusion detection using GANs. The system integrates a generator network that produces synthetic network traffic patterns, including simulated attacks, and a discriminator network that classifies network traffic as either benign or malicious. This framework utilizes both labeled and unlabeled data, significantly reducing the need for extensive manual data labeling while enhancing the detection of novel and rare attacks. The GAN continuously learns and adapts, improving its accuracy and reducing false positives over time. The system is scalable and adaptable to various network sizes and traffic patterns, providing a resource-efficient and highly effective solution for network security.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The semi-supervised network intrusion detection system comprises two main components: a generator network and a discriminator network, both based on Generative Adversarial Networks (GANs).
Generator Network: The generator network learns the distribution of both normal and malicious network traffic patterns from a dataset of labeled and unlabeled network traffic data. It then generates synthetic network traffic that closely resembles real-world network activity. This synthetic traffic includes examples of known attacks, and importantly, samples that represent novel or previously unseen attack patterns.
Discriminator Network: The discriminator network receives both real network traffic data and the synthetic network traffic generated by the generator network. It learns to classify the network traffic as either benign or malicious. By training the discriminator network against the generator network, the overall accuracy and robustness of the system is improved. The use of both labeled and unlabeled data enhances the discriminator's ability to differentiate between normal and malicious traffic patterns, and crucially, to identify novel attacks that may not be present in the training data.
The system operates in a semi-supervised manner, leveraging both labeled and unlabeled network traffic data. This reduces the need for extensive manual labeling, while enabling continuous learning and adaptation to evolving threats. The GAN architecture allows the system to effectively detect both known and unknown network intrusions, minimizing false positives, improving accuracy, and enhancing overall network security. The system is designed to be scalable and adaptable, capable of efficiently processing data from networks of various sizes and complexity.

, Claims:1. A semi-supervised network intrusion detection system, comprising a generator network and a discriminator network, both based on Generative Adversarial Networks (GANs).
2. The system as claimed in Claim 1, wherein said generator network generates synthetic network traffic patterns, including simulated attacks.
3. The system as claimed in Claim 2, wherein said discriminator network classifies network traffic as either benign or malicious using both labeled and unlabeled data.
4. The system as claimed in Claim 3, wherein said system utilizes a semi-supervised learning approach, reducing the reliance on large labeled datasets.
5. The system as claimed in Claim 4, wherein said system is capable of detecting both known and previously unseen network intrusions.
6. The system as claimed in Claim 5, wherein said system minimizes false positives compared to traditional anomaly detection systems.
7. The system as claimed in Claim 6, wherein said system is scalable and adaptable to various network sizes and traffic patterns.
8. A method for detecting network intrusions, as claimed in Claim 8, comprising the steps of: (a) generating synthetic network traffic patterns using a GAN-based generator network; (b) classifying network traffic as either benign or malicious using a GAN-based discriminator

network; and (c) leveraging both labeled and unlabeled network traffic data in a semi-supervised learning approach.

Documents