VAEMax: Open-Set Intrusion Detection Model with OpenMax and Variational Autoencoder

Core Concepts

The author proposes the VAEMax model, combining OpenMax and VAE, to detect known and unknown network attacks effectively.

Abstract

The paper introduces VAEMax, a dual detection model using OpenMax and VAE for intrusion detection. It aims to classify known attacks while inferring unknown ones. The model extracts flow payload features using 1D-CNN, then employs OpenMax for classification, detecting some unknown attacks. Finally, VAE performs secondary detection based on reconstruction loss. Experiments on CIC-IDS2017 and CSE-CIC-IDS2018 datasets show superior performance compared to baseline models.

Stats

Experiments performed on dataset CIC-IDS2017 and CSE-CIC-IDS2018.
Achieved accuracy of 82.10% with VAEMax.
Recall rate of approximately 42% and 66% for unknown attacks.
False positive rate kept below 5%.

Quotes

"The proposed dual intrusion detection model, VAEMax, combines OpenMax and VAE."
"Experiments demonstrate the effectiveness of the VAEMax approach in realistic network environments."

Key Insights Distilled From

VAEMax

by Zhiyin Qiu,D... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04193.pdf

Deeper Inquiries

How can the VAEMax model be adapted to handle multi-classification problems with unknown classes

To adapt the VAEMax model for multi-classification problems with unknown classes, we can introduce a hierarchical approach. Instead of categorizing all unknown attacks into one class, we can create subcategories based on certain characteristics or features extracted from the data. By utilizing clustering techniques or additional neural network layers, we can group similar unknown attacks together and assign them to different classes within the "unknown" category. This way, the model can distinguish between various types of unknown attacks and provide more granular insights during classification.

What are the implications of removing either the VAE or OpenMax module from the VAEMax model

Removing either the VAE or OpenMax module from the VAEMax model would have significant implications on its performance:

Removing VAE: Without the Variational Autoencoder (VAE), the model would lose its ability to reconstruct features and calculate reconstruction loss. This means that it would no longer be able to determine whether a flow is an unknown attack based on reconstruction error. As a result, the detection accuracy for both known and unknown attacks could decrease significantly.
Removing OpenMax: If the OpenMax module is removed, the initial classification process based on OpenSet Recognition will not take place. This step is crucial for identifying some unknown attacks during preliminary classification. Without this module, there might be misclassifications of known flows as well as reduced sensitivity in detecting previously unseen attack patterns.

How can the stability of the proposed algorithm be further improved in real-world scenarios

To enhance stability in real-world scenarios, several strategies can be implemented:

Data Augmentation: Increasing training data through augmentation techniques like rotation, flipping images/data points can help improve generalization and robustness.
Regularization Techniques: Implementing dropout layers or L1/L2 regularization in neural networks helps prevent overfitting by reducing complex co-adaptations among neurons.
Ensemble Learning: Utilizing ensemble methods where multiple models are trained independently and their predictions are combined often leads to better stability and improved performance.
Hyperparameter Tuning: Fine-tuning hyperparameters using cross-validation techniques ensures optimal settings for improved stability across different datasets.
By incorporating these strategies along with continuous monitoring and evaluation of model performance under diverse conditions, we can further enhance stability in real-world applications of VAEMax algorithm.