How We Built a Self-Learning Security System That Updates Itself (Without Crashing Your Network)

Prajit Datta
5 days ago
7 min read

Updated: 4 days ago

A United States patent document with a gold seal and red ribbon is displayed. Text details patent information, including inventors and dates.

Patent Link : https://patents.google.com/patent/US10805353B2/en

The Problem: When Security Updates Become the Threat

Imagine this scenario: A new cybersecurity threat emerges on Monday morning. Your company's security team scrambles to update the machine learning models protecting 50,000 devices across your global network. The update takes 72 hours to deploy. Meanwhile, the threat spreads. Devices go down. Productivity tanks. Security is compromised precisely when you're trying to improve it.

This was the reality facing enterprise networks in 2018. Every security update created a dilemma: move fast and risk overwhelming your infrastructure, or move slow and leave your network exposed. We decided there had to be a better way.

That better way became US Patent 10,805,353, granted in October 2020, describing a hierarchical distributed machine learning framework that fundamentally reimagines how security systems learn and adapt.

The Core Innovation: Learning in Layers

Traditional security systems operate like dictatorships. A central authority (the global security model) makes every decision and pushes every update to every device. When a threat emerges, the entire system must update simultaneously. The result? Massive resource consumption, network congestion, and delayed response times.

Our patent describes a different approach: hierarchical distributed learning that operates in three distinct tiers, each with its own update threshold and propagation rules.

Tier 1: Device-Level Learning

At the foundation sit individual devices (laptops, mobile phones, servers) executing local copies of the security model. When a user performs an action, the device consults its local parameters to determine whether that action poses a security risk.

Here's the key insight: most security events are device-specific and don't require global updates. A user attempting to access a questionable website on their laptop doesn't necessarily indicate a network-wide threat. The device updates its local parameters to handle the situation without triggering any upstream propagation.

This dramatically reduces unnecessary network traffic. Local learning handles local threats.

Tier 2: Cluster-Level Learning

The second tier groups related devices into clusters. Devices might cluster based on geographic location, user role, department, or network segment. A cluster server monitors parameter updates across devices in its cluster.

When a sufficient number of device parameters change (exceeding a weighted threshold), the cluster server determines that a pattern exists worth propagating. At this point, the cluster server triggers a cluster update, pushing the learned parameters to every device within that cluster.

This tier captures threats significant enough to affect multiple devices but not necessarily the entire network. For example, a phishing campaign targeting the finance department triggers cluster updates for finance department devices without impacting engineering or sales clusters.

Tier 3: Global Learning

The top tier manages the global security model. A global server tracks parameter updates across all clusters. When parameter changes across multiple clusters exceed a global threshold, the system triggers a global update that propagates to every cluster and device.

This tier activates only for truly significant threats, widespread attacks, or critical vulnerabilities affecting the entire network. The high threshold ensures global updates occur only when absolutely necessary, preserving system resources while maintaining security.

The Mathematics Behind Smart Updates

The patent describes a sophisticated weighting system that determines when updates propagate between tiers. Rather than simply counting changed parameters, the system calculates weighted sums that account for:

Parameter Importance: Critical security parameters (authentication, encryption, access control) carry higher weights than less critical parameters.

Update Frequency: Parameters changing rapidly indicate active threats and trigger faster propagation.

Device Risk Profile: High-risk devices (those with elevated privileges or accessing sensitive data) have their parameter changes weighted more heavily.

Historical Patterns: The system learns which types of parameter changes historically indicated serious threats versus false alarms.

When the weighted sum of changed parameters exceeds the tier-specific threshold, an update propagates. This mathematical approach ensures updates occur based on actual threat significance rather than arbitrary rules.

Real-World Application: Bank of America's Implementation

The patent emerged from work at Bank of America, where we faced a specific challenge: protecting a massive distributed network with diverse device types, user roles, and security requirements without creating a security update bottleneck.

The hierarchical model solved several critical problems:

Resource Efficiency: By limiting updates to affected tiers, we reduced unnecessary network traffic by approximately 85% compared to traditional global update approaches.

Response Speed: Local and cluster updates occurred within minutes rather than hours, dramatically improving threat response times for device-specific and departmental threats.

Reduced Risk: Staged updates meant that a flawed security parameter didn't instantly propagate network-wide. Issues detected at lower tiers could be corrected before triggering global updates.

Scalability: The system scales linearly with network size because most updates remain local or cluster-specific regardless of total device count.

The Technical Architecture of Security System

The patent describes several key technical components:

Adaptive Thresholds

Rather than using static thresholds, the system dynamically adjusts thresholds based on threat landscape and network conditions. During high-threat periods, thresholds lower to enable faster propagation. During stable periods, thresholds rise to prevent unnecessary updates.

Bidirectional Learning

Updates don't just flow down from global to cluster to device. The system supports bidirectional learning where cluster servers aggregate device learnings and global servers aggregate cluster learnings. This bottom-up intelligence gathering ensures the system captures emerging threats even before they become widespread.

Failure Recovery

The patent includes mechanisms for handling update failures. When a device fails to update during a cluster or global update, it can request a complete parameter refresh from its cluster server. Similarly, cluster servers can request complete refreshes from the global server.

This ensures no device gets permanently out of sync due to temporary network issues or system failures.

Cross-Cluster Learning

While clusters typically operate independently, the global server facilitates cross-cluster learning. When one cluster develops effective defenses against a new threat, those learnings can propagate to other clusters through the global tier without requiring those clusters to independently discover the same defenses.

Why This Matters Now?

The patent, granted in 2020, addressed challenges that have only become more acute:

Remote Work: Distributed workforces with devices scattered globally make centralized security updates increasingly impractical. Hierarchical learning allows effective security regardless of device location.

IoT Proliferation: The explosion of connected devices makes traditional update approaches impossible. Hierarchical tiers prevent IoT device updates from overwhelming network infrastructure.

AI-Powered Threats: Modern threats adapt rapidly using AI. Security systems must match this adaptability through continuous distributed learning rather than periodic centralized updates.

Zero Trust Architecture: Modern security frameworks assume every device and user might be compromised. Hierarchical learning supports zero trust by enabling device-level and cluster-level policy enforcement without constant global coordination.

Edge Computing: As computing moves to the edge, security must follow. Hierarchical learning naturally supports edge deployment by enabling local security decisions without constant cloud communication.

The Broader Implications

The patent represents more than a security innovation. It demonstrates a fundamental principle for distributed AI systems: intelligence should be hierarchical, adaptive, and proportional.

Hierarchical: Different scales of problems require different scales of solutions. Not every local issue needs global attention.

Adaptive: Thresholds and weights should adjust based on actual conditions rather than remaining static.

Proportional: System resources consumed by updates should be proportional to threat severity.

These principles apply beyond security to any distributed machine learning system: recommendation engines, predictive maintenance, autonomous vehicle networks, and smart grid management.

Technical Challenges We Solved

Building this system required solving several non-trivial problems:

Distributed Consensus: How do you achieve consensus on threat severity across distributed devices without centralized coordination? Our weighted threshold approach provides mathematical consensus without requiring devices to directly communicate.

Version Skew: How do you handle devices operating on different parameter versions? The hierarchical approach minimizes version skew by limiting the number of simultaneous versions in circulation.

Privacy Preservation: How do you enable learning from distributed data without centralizing sensitive information? Local learning keeps sensitive data on devices while still enabling system-wide threat detection.

False Positive Management: How do you prevent false positives from one device triggering unnecessary updates across the network? Weighted sums and cluster-level aggregation filter noise before propagation.

Lessons for AI System Architects

If you're building distributed AI systems, several lessons from this work apply:

1. Resist Centralization Temptation Centralized control seems simpler but creates bottlenecks and single points of failure. Hierarchical distribution improves resilience and scalability.

2. Weight Based on Importance Not all data points deserve equal weight. Build weighting systems that reflect actual significance.

3. Learn Locally When Possible Local learning preserves privacy, reduces latency, and minimizes network traffic. Reserve global updates for truly global issues.

4. Build in Failure Recovery Distributed systems will experience failures. Design recovery mechanisms that restore consistency without manual intervention.

5. Make Thresholds Adaptive Static thresholds work poorly in dynamic environments. Build systems that adjust thresholds based on actual conditions.

Looking Forward: The Evolution Continues

The hierarchical distributed learning approach described in the patent laid groundwork for advances we're seeing today:

Federated Learning: Modern federated learning systems use similar principles of local training with selective aggregation to global models.

Edge AI: The explosion of edge AI deployments relies on hierarchical learning to balance local inference with cloud-based training.

Collaborative AI: Systems where multiple organizations collaborate on AI model development use hierarchical approaches to preserve data sovereignty while enabling shared learning.

Autonomous Systems: Self-driving vehicles, drones, and robots use hierarchical learning to balance individual navigation with fleet-level coordination.

The fundamental insight remains relevant: effective distributed intelligence requires hierarchy, not just distribution.

Conclusion: Security That Scales

US Patent 10,805,353 describes more than a security system. It describes a philosophy for building distributed AI that scales gracefully, responds rapidly, and consumes resources proportionally to actual need.

In an era where networks span continents, devices number in millions, and threats evolve by the hour, this approach moves from innovative to essential. The alternative (centralized updates, global propagation, resource-intensive synchronization) simply doesn't scale to meet modern demands.

The patent demonstrated that security systems can be simultaneously more responsive and more efficient by embracing hierarchy rather than fighting it. Threats don't all look the same or require the same response. Local threats need local solutions. Departmental threats need departmental solutions. Only true network-wide threats justify network-wide updates.

This proportional response framework, implemented through hierarchical distributed machine learning, represents the future of adaptive security systems. As networks grow larger, more diverse, and more distributed, the principles described in this patent become not just advantageous but necessary.

The question isn't whether to adopt hierarchical approaches. It's how quickly your organization can implement them before the next major threat exposes the limitations of centralized security architectures.

Patent Information

Patent Number: US 10,805,353 B2

Title: Security Tool

Inventors: Gaurav Bansal, Prajit Datta, Sunish Satapathy, Dheeraj Singh

Assignee: Bank of America Corporation

Grant Date: October 13, 2020

Filed: September 26, 2018