Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks to Generate Synthetic Attack Samples

المفاهيم الأساسية

Leveraging the power of Generative Adversarial Networks (GANs) to generate synthetic network traffic data that closely mimics real-world anomalous behavior, in order to enhance the performance of network intrusion detection systems (NIDS) by addressing the challenge of limited training data for attack samples.

الملخص

The research addresses the critical challenge of data scarcity in NIDS training datasets by integrating GANs into the NIDS framework. Three distinct GAN models (Vanilla GAN, Wasserstein GAN, and Conditional Tabular GAN) are implemented to generate synthetic network traffic data that closely resembles real-world anomalous behavior, specifically targeting the Botnet attack class.

The generated samples are extensively evaluated for their closeness and similarity to the original Botnet samples using various metrics and methodologies, including cosine similarity, cumulative sums, and machine learning algorithms. The generated Botnet samples are then integrated into the original CIC-IDS2017 dataset in varying quantities to train a Random Forest-based NIDS model.

The results demonstrate that the integration of GAN-generated samples significantly improves the NIDS performance in detecting Botnet attacks, with precision, recall, and F1-score reaching up to 1.00, 0.82, and 0.90 respectively. This represents a substantial enhancement compared to the baseline NIDS performance. The research establishes a new benchmark for Botnet classification on the CIC-IDS2017 dataset, outperforming previous state-of-the-art approaches.

The findings highlight the effectiveness of leveraging GANs to address the data scarcity challenge in NIDS and bolster the cybersecurity posture of organizations against evolving and sophisticated cyber threats.

الإحصائيات

The CIC-IDS2017 dataset contains 2,271,320 instances of Benign network traffic and 1,956 instances of Botnet traffic.
The Random Forest-based NIDS baseline achieved a precision of 0.87, recall of 0.46, and F1-score of 0.60 for the Botnet class.
After integrating 99 times the original number of Botnet samples generated by the WGAN model, the NIDS achieved a precision of 1.00, recall of 0.82, and F1-score of 0.90 for the Botnet class.

اقتباسات

"By harnessing the power of GANs in generating synthetic network traffic data that closely mimics real-world network behavior, we address a key challenge associated with NIDS training datasets, which is the data scarcity."
"Our findings show that the integration of GANs into NIDS can lead to enhancements in intrusion detection performance for attacks with limited training data, making it a promising avenue for bolstering the cybersecurity posture of organizations in an increasingly interconnected and vulnerable digital landscape."

الرؤى الأساسية المستخلصة من

Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks

by Xinxing Zhao... في arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07464.pdf

استفسارات أعمق

How can the proposed GAN-based approach be extended to generate synthetic samples for other attack classes beyond Botnet to further enhance the overall NIDS performance?

To extend the GAN-based approach to generate synthetic samples for other attack classes, a similar methodology can be applied by dividing the original samples of those classes into smaller, more homogenous segments. These segments can then be used as a basis for generating additional synthetic samples using GAN models. By tailoring the dataset divisions to align with the specific characteristics of each attack class, the GAN models can be trained to generate realistic and diverse samples that closely mimic the behavior of those attacks. This approach can help in enhancing the overall NIDS performance by providing a more comprehensive and diverse training dataset, enabling the IDS to effectively detect a wider range of cyber threats.

What are the potential limitations or drawbacks of relying solely on GAN-generated samples for NIDS training, and how can a hybrid approach incorporating both real and synthetic data be explored?

Relying solely on GAN-generated samples for NIDS training may have some limitations and drawbacks. One potential limitation is the risk of overfitting, where the IDS may become too specialized in detecting only the synthetic samples generated by the GAN models and may not generalize well to real-world attack scenarios. Additionally, GAN-generated samples may not fully capture the complexity and variability of real attack patterns, leading to potential gaps in the IDS's detection capabilities.
To address these limitations, a hybrid approach incorporating both real and synthetic data can be explored. This hybrid approach involves combining real-world attack samples with GAN-generated samples to create a more diverse and representative training dataset. By leveraging the strengths of both real and synthetic data, the IDS can benefit from a more balanced and comprehensive training set, improving its ability to detect a wide range of cyber threats effectively. This hybrid approach can help mitigate the risks of overfitting and ensure that the IDS is robust and adaptable to evolving attack techniques.

Given the advancements in adversarial machine learning, how can the robustness of the GAN-enhanced NIDS be evaluated against adversarial attacks aimed at evading the detection system?

To evaluate the robustness of a GAN-enhanced NIDS against adversarial attacks, several strategies can be employed. One approach is to conduct adversarial testing, where intentionally crafted adversarial samples are introduced into the system to assess its resilience. These adversarial samples can be designed to mimic sophisticated attack techniques and evasion strategies, challenging the NIDS to accurately detect and classify them. By analyzing the NIDS's performance in detecting these adversarial samples, its robustness against evasion attempts can be evaluated.
Another method is to implement adversarial training, where the NIDS is trained on a combination of real and adversarial samples to enhance its ability to recognize and counter adversarial attacks. By exposing the NIDS to adversarial scenarios during training, it can learn to identify and mitigate potential evasion tactics effectively. Additionally, ongoing monitoring and evaluation of the NIDS's performance in real-world scenarios can provide valuable insights into its robustness and effectiveness against adversarial threats. Regular updates and adjustments to the NIDS's algorithms and detection mechanisms based on these evaluations can further enhance its resilience to adversarial attacks.