Threat Detection Research#
Overview#
Our threat detection research focuses on developing advanced systems capable of identifying, analyzing, and responding to cybersecurity threats in real-time. We leverage cutting-edge machine learning and AI techniques to create adaptive frameworks that can detect both known and emerging threats across various digital environments.
Research Challenges#
Modern threat detection faces several significant challenges:
- Evolving Threat Landscape: Cyber threats constantly evolve, requiring detection systems that can adapt to new attack vectors and techniques.
- False Positives: Traditional detection systems often generate excessive false positives, creating alert fatigue and overlooking real threats.
- Encrypted Traffic: Increasing use of encryption makes traditional packet inspection ineffective.
- Resource Constraints: Many organizations have limited computational resources for advanced threat detection.
- Advanced Persistent Threats (APTs): Sophisticated attackers employ stealthy techniques designed to evade detection for extended periods.
Our Approach#
At OCRG, we're addressing these challenges through several innovative research directions:
Self-supervised Anomaly Detection#
We're exploring frameworks that:
- Learn normal patterns from unlabeled data
- Detect deviations without requiring examples of every threat type
- Continuously adapt to changing environments and behaviors
Behavioral Analysis#
Our research focuses on:
- Analyzing patterns of behavior rather than simply matching signatures
- Building comprehensive behavior profiles for users, systems, and networks
- Detecting subtle deviations that may indicate compromise
Multimodal Threat Intelligence#
We're developing systems that:
- Integrate and correlate data from multiple sources
- Apply contextual analysis to reduce false positives
- Generate actionable intelligence from raw security data
Current Projects#
Adaptive Network Anomaly Detection#
This project aims to develop a framework that continuously learns normal network behavior patterns and identifies anomalies that may indicate security threats, without requiring signatures of specific attacks.
Contextual Security Event Analysis#
We're researching methods to analyze security events in context, correlating multiple data points to distinguish between benign anomalies and actual threats.
Lightweight Endpoint Protection#
This project focuses on developing threat detection techniques that can operate effectively on resource-constrained endpoints while maintaining high detection rates.
Future Directions#
Our roadmap includes:
- Incorporating federated learning for privacy-preserving threat detection
- Developing explainable AI approaches for security analysts
- Exploring real-time threat mitigation through automated response systems
- Creating frameworks for detecting threats in IoT and embedded systems
Collaboration Opportunities#
We're seeking collaborators interested in:
- Developing and testing new threat detection algorithms
- Creating realistic evaluation environments and datasets
- Implementing practical threat detection solutions
- Researching novel approaches to emerging threat categories