arxiv_ml 90% Match Research Cybersecurity professionals,Industrial control system engineers,Machine learning researchers,Anomaly detection specialists 3 weeks ago

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

ai-safety › robustness

📄 Abstract

Abstract: Zero-day anomaly detection is critical in industrial applications where novel, unforeseen threats can compromise system integrity and safety. Traditional detection systems often fail to identify these unseen anomalies due to their reliance on in-distribution data. Domain generalization addresses this gap by leveraging knowledge from multiple known domains to detect out-of-distribution events. In this work, we introduce a multi-task representation learning technique that fuses information across related domains into a unified latent space. By jointly optimizing classification, reconstruction, and mutual information regularization losses, our method learns a minimal(bottleneck), domain-invariant representation that discards spurious correlations. This latent space decorrelation enhances generalization, enabling the detection of anomalies in unseen domains. Our experimental results demonstrate significant improvements in zero-day or novel anomaly detection across diverse anomaly detection datasets.

Authors (5)

Padmaksha Roy

Tyler Cody

Himanshu Singhal

Kevin Choi

Ming Jin

Submitted

December 28, 2023

arXiv Category

cs.CR

European Conference of Machine Learning 2025

arXiv PDF

Key Contributions

This paper introduces a multi-task representation learning technique for zero-day anomaly detection in industrial applications. By fusing information across domains into a unified latent space using joint optimization of classification, reconstruction, and mutual information regularization, it learns a minimal, domain-invariant representation that enhances generalization to unseen domains.

Business Value

Enhances industrial and cybersecurity by enabling the detection of previously unknown threats, thereby improving system integrity and safety.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Moderate, requires careful implementation of the multi-task learning framework and potentially large, diverse datasets for training.

Limitations Addressed

The inability of traditional detection systems to identify novel (zero-day) anomalies and the challenge of domain generalization in anomaly detection.

Performance Gains

Demonstrates significant improvements in zero-day or novel anomaly detection across diverse anomaly datasets.

Technical Tags

anomaly detectionzero-day threatsdomain generalizationrepresentation learninglatent spacemulti-task learningout-of-distribution detectionmutual information regularization

Research Topics

Machine Learning SecurityAnomaly DetectionDomain GeneralizationRepresentation Learning

Methods & Architectures

multi-task representation learningjoint optimization (classification, reconstruction, mutual information regularization)latent space decorrelation encoder-decoder (implied by reconstruction loss)

Applications & Tasks

Industrial Security Cybersecurity Anomaly Detection Systems Zero-day Anomaly DetectionOut-of-Distribution DetectionDomain Generalization detecting novel anomaliesgeneralizing to unseen domainslearning domain-invariant representations

Related Fields

CybersecurityMachine LearningAnomaly DetectionIndustrial Control Systems

Keywords

anomaly detectionzero-daydomain generalizationrepresentation learninglatent spacemulti-task learningout-of-distributionmutual informationindustrial securitycybersecurity

Academic Context

#Machine Learning Security#Anomaly Detection#Domain Generalization#Representation Learning

Commercial Potential

Potential Products

Advanced intrusion detection systemsIndustrial anomaly monitoring softwareCybersecurity threat intelligence platforms

Target Industries

ManufacturingEnergyCritical InfrastructureCybersecurity Services

Use Case Examples

Detecting novel malware in industrial networksIdentifying unusual sensor readings in manufacturing processesMonitoring for sophisticated cyberattacks

Competitive Edge

Offers a novel approach to domain generalization for anomaly detection by learning domain-invariant representations, potentially outperforming methods that rely on specific domain knowledge.

Market Opportunity

Large, within the cybersecurity and industrial automation markets.

Revenue Models

Directthrough security software and services.

Resource Requirements

Compute Needs

High, due to multi-task learning and representation learning on potentially large datasets.

Data Requirements

Requires data from multiple related domains, including examples of normal operation and potentially some anomalies.

Deployment Constraints

Need for diverse training data across domains, computational resources for training.

Scalability

Scalability depends on the efficiency of the representation learning and the number of tasks/domains.

Regulatory Considerations

Data privacy and security regulationsespecially if dealing with sensitive industrial data.

Production Readiness

Maturity Level

Research

Time to Market

Medium to long, for robust deployment in industrial settings.

Patent Potential

Moderate, for the specific multi-task learning architecture and regularization techniques.

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