NikoTak – Tamara Shostak's blog

As a data scientist deeply immersed in cybersecurity, I’ve watched the challenge of False Positives (FPs) evolve dramatically. When I first wrote about using Machine Learning to combat false positives in 2019, I couldn’t have anticipated how transformer architecture would revolutionize our approach to this persistent challenge. Let me share both where we were and where I see us heading.

The False Positive Problem

FPs remain a critical issue in cybersecurity, but their implications have become even more significant in today’s landscape. When a security system misinterprets non-malicious activity as an attack, the consequences ripple far beyond simple operational disruption:

• E-commerce sites lose real customers
• Legitimate web crawlers get blocked, affecting SEO
• Training data becomes contaminated, creating cascading errors
• Resources get diverted from real threats

What fascinates me most is how the relationship between False Positives (FPs) and False Negatives (FNs) has become more complex in the age of sophisticated attacks. While analysts once suggested finding an “optimal ratio” between FPs and FNs based on monetary costs, I’ve come to believe this approach is fundamentally flawed. In today’s threat landscape, we need something more sophisticated than simple cost-benefit analysis.

The Evolution of Machine Learning in Cybersecurity

When I first explored this topic, we divided ML algorithms into two main categories: Shallow Learning (SL) and Deep Learning (DL). Today, I see these as stepping stones toward what’s becoming possible with transformer architecture. Let me break down this evolution:

Traditional ML (Shallow Learning):

• Supervised algorithms (Shallow Neural Networks, HMM, KNN, Random Forest)
• Unsupervised algorithms (Clustering, Association)
• Required extensive feature engineering

Deep Learning:

• Supervised (FNN, RNN, CNN)
• Unsupervised (SAE, DBN)
• Reduced need for manual feature selection

Transformer Architecture (The Next Frontier):

• Self-attention mechanisms
• Contextual understanding
• Ability to process sequential data with long-range dependencies

This evolution is particularly relevant for the core challenge of evaluating FP reduction effectiveness. We still use traditional metrics:

False Positive Rate = FP / (FP+TN)
Accuracy = (TP+TN) / (FP+TP+FN+TN)

But what excites me is how transformer models are redefining what’s possible in terms of accuracy while maintaining low FP rates. Their ability to understand context and relationships between events opens new possibilities for reducing false positives without compromising security.

Current Methods and Future Directions

The traditional approaches to FP reduction remain valuable:

• Parameter tuning in IDS systems
• Rule-based classification for specific attack types
• Neural network models like GHSOM
• Two-stage correlation systems

However, I’m now working on extending these methods with transformer-based approaches that can:

• Understand the broader context of security events
• Learn complex patterns across multiple time scales
• Adapt to evolving threat landscapes in real-time
• Process sequential events with long-term dependencies

The Real-World Challenge

Moving from theory to practice has always been challenging in cybersecurity. While our research showed promising results with traditional ML approaches, implementing these solutions in production environments revealed limitations that I believe transformer architecture is uniquely positioned to address:

1. The need for real-time analysis (not just offline processing)
2. The challenge of maintaining accuracy across different organizational contexts
3. The requirement for continuous adaptation to new threats
4. The balance between automation and human oversight

Looking Ahead

As we prepare to delve deeper into specific solutions in Part 2, I want to emphasize that we’re at a pivotal moment in the evolution of false positive reduction. The integration of transformer architecture into our security systems isn’t just another incremental improvement – it’s a fundamental shift in how we approach the problem.

We’re moving from systems that simply classify events to ones that understand them in context. From models that require extensive training to architectures that can adapt in real-time. From solutions that operate in isolation to ones that can learn from global patterns while maintaining local relevance.

In Part 2, we’ll explore how these concepts are being implemented in practice, and I’ll share my vision for the future of intelligent security systems that can virtually eliminate false positives while maintaining robust threat detection. Stay tuned.