Improving Intrusion Detection through Domain-Invariant Representation Learning in Latent Space

Core Concepts

A multi-task representation learning technique that leverages information from multiple related domains to improve the detection of classes from unseen domains by cultivating a domain-invariant latent space.

Abstract

The paper introduces a multi-task representation learning approach to improve the detection of out-of-distribution (OOD) intrusion classes. The key ideas are: Cultivate a latent space from data spanning multiple source and cross-domains to amplify generalization to OOD domains. Disentangle the latent space by minimizing the mutual information between the input and the latent space, effectively de-correlating spurious correlations among the samples of a specific domain. Jointly optimize the classification loss, the multi-domain reconstruction loss, and the mutual invariance regularization in the latent space. The authors evaluate the proposed method on multiple cybersecurity datasets, showing improved performance on both unseen in-distribution and OOD classes compared to contemporary domain generalization methods. The key is to leverage cross-domain data in a principled way and apply a mutual information-based regularization to learn a domain-invariant latent representation.

Stats

The authors use several cybersecurity datasets in their experiments: CSE-CIC-IDS2018: SOLARIS, GOLDENEYE as source domains, INFILTRATION, BOTNET as cross-domains, and RARE, SLOWHTTPS, HOIC as OOD domains. CICIoT 2023: BENIGN, DoS, DDoS as source, RECON as cross-domain, and WEB, MIRAI as OOD. CICIoMT 2024: BENIGN, DDoS, DoS as source, RECON, SPOOFING as cross-domain, and MQTT as OOD.

Quotes

"We consider the scenario where we know that each domain has its own spuriously correlated features which hurts the generalization performance of the model when tested on OOD domains." "Our methodology jointly optimizes the classification loss, the multi-domain reconstruction loss, and the mutual invariance regularization in the latent space." "We show that cross-domain data when added in a principled way, can improve generalization performance on the IN and OOD classes."

Key Insights Distilled From

Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space

by Padmaksha Ro... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2312.17300.pdf

Deeper Inquiries

How can the proposed approach be extended to handle temporal dependencies in intrusion detection data?

In order to handle temporal dependencies in intrusion detection data, the proposed approach can be extended by incorporating recurrent neural networks (RNNs) or long short-term memory (LSTM) networks. These types of neural networks are well-suited for modeling sequential data and capturing temporal dependencies. By integrating RNNs or LSTMs into the architecture, the model can learn patterns and relationships over time, which is crucial for detecting intrusions that may unfold gradually or exhibit temporal characteristics. Additionally, attention mechanisms can be employed to focus on relevant time steps and enhance the model's ability to detect anomalies in the temporal domain.

What are the potential limitations of the mutual information-based regularization, and how can it be further improved?

One potential limitation of mutual information-based regularization is the computational complexity involved in estimating mutual information, especially in high-dimensional spaces. This can lead to increased training time and resource requirements. Additionally, mutual information may not capture all aspects of the relationship between the input and latent spaces, potentially overlooking more nuanced dependencies. To address these limitations and improve mutual information-based regularization, several strategies can be implemented: Efficient Estimation Techniques: Utilize more efficient estimation techniques for mutual information, such as neural estimation methods or variational approaches, to reduce computational overhead. Adaptive Weighting: Introduce adaptive weighting for the mutual information loss term to dynamically adjust its influence during training based on the model's performance. Incorporate Auxiliary Tasks: Incorporate auxiliary tasks or additional regularization terms that complement mutual information-based regularization, enhancing the overall learning process and capturing a broader range of dependencies. Ensemble Methods: Employ ensemble methods to combine multiple models trained with different regularization strengths, leveraging the diversity of approaches to improve overall performance.

Can the domain-invariant latent representation learned by this method be leveraged for other security-related tasks beyond intrusion detection?

Yes, the domain-invariant latent representation learned by this method can be leveraged for various security-related tasks beyond intrusion detection. Some potential applications include: Malware Detection: The domain-invariant features can be used to identify and classify different types of malware based on their underlying characteristics and behaviors. Anomaly Detection: The learned latent representation can aid in detecting anomalies in network traffic, user behavior, or system logs, helping to identify potential security threats or unusual activities. Phishing Detection: By extracting domain-invariant features, the model can distinguish between legitimate and phishing websites based on common patterns and features present in the data. Security Incident Response: The latent representation can assist in prioritizing and responding to security incidents by providing a more generalized understanding of security threats and vulnerabilities across different domains. By leveraging the domain-invariant latent representation for these tasks, security professionals can enhance their capabilities in threat detection, incident response, and overall cybersecurity defense strategies.