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Resilient Machine Learning Systems (RMLS) - Resilience for AI/ML at the Core

Resilient Machine Learning Systems (RMLS) Datasheet

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Key Benefits:

  • Self-Healing AI: Automatically retrains and recalibrates itself to maintain detection accuracy, even under adversarial conditions or attempts to poison data streams.
  • Adversarial Robustness: Built-in defenses safeguard against evasion techniques and model manipulation that could otherwise blind anomaly detection engines.
  • Continuous Adaptation: Learns from real-world operational data in near real-time to adapt to new threat vectors without manual intervention.
  • Trustworthy Autonomy: Maintains mission integrity with explainable AI that delivers transparent decisions, auditability, and policy compliance

Resilient Machine Learning Systems (RMLS) Videos

RMLS for IPv6
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Resilient ML for Electronic Warfare
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ICS/OT Environments
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Connect with LOCH in Houston at Maritime Security East – Booth #22

Connect with LOCH in Houston at Maritime Security East – Booth #22

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Advancing the Future of Cybersecurity with Resilient Machine Learning Systems (rMLS) to Protect Artificial Intelligence and Machine Learning from Cyberattacks

Advancing the Future of Cybersecurity with Resilient Machine Learning Systems (rMLS) to Protect Artificial Intelligence and Machine Learning from Cyberattacks

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Resilient Machine Learning Systems (RMLS) are a next-generation cybersecurity framework purpose-built to protect the integrity, availability, and trustworthiness of AI and ML systems.

Unlike traditional security tools that only monitor external threats, RMLS defends the algorithms themselves, ensuring they remain uncompromised even under persistent, sophisticated cyberattacks.

Where RMLS Delivers Resilient AI/ML Security

Industrial Control Systems (ICS)

Problem: Legacy ICS runs static configurations that are highly vulnerable to malware and unauthorized changes.
RMLS Solution: RMLS uses anomaly detection and MTD to continuously monitor and rotate control logic, stopping attackers from hijacking operations.

Smart Grids & Utility Networks

Problem: Predictive analytics for load balancing can be corrupted through false data injection, destabilizing the supply.
RMLS Solution: RMLS protects ML prediction engines by detecting abnormal data inputs and rotating models, blocking adversarial data poisoning attempts.

Utilities

Oil & Gas SCADA Systems

Problem: SCADA systems use ML for pipeline monitoring, and data poisoning can cause catastrophic misreadings.
RMLS Solution: RMLS ensures pipelines run safely by deploying resilient algorithms that adapt to changing conditions and resist tampered data streams.

Oil & Gas

Smart Manufacturing (Industry 4.0)

Problem: Automated factories rely on ML to coordinate robotics and production lines. If compromised, attackers can halt operations or produce defects.
RMLS Solution: RMLS defends embedded ML at the edge, continuously shifting robotics coordination logic and detecting abnormal behavior before damage is done.

Smart Manufacturing

Critical Infrastructure Data Lakes

Problem: Massive streams of sensor and operational data feed analytics engines — a prime target for corruption.
RMLS Solution: RMLS continuously verifies data integrity and dynamically adjusts analytics models to block data manipulation and maintain operational trust.

Autonomous Vehicles & Drones

Problem: AVs rely on real-time ML for navigation and perception. Adversarial inputs can mislead systems, risking collisions or mission failure.
RMLS Solution: RMLS rotates perception models and blocks adversarial attempts in real time, ensuring safe and reliable autonomous operations.

Electronic Warfare (EW)

Problem: EW systems rely on AI-driven signal intelligence to detect, classify, and respond to threats. These models are prime targets for spoofing and jamming.
RMLS Solution: RMLS dynamically shifts detection models and operational parameters, preventing attackers from mapping signal intelligence and preserving situational awareness.

Space & Satellite Networks

Problem: Satellites and ground stations depend on ML for mission planning and autonomous adjustments. A compromised model can disrupt communications and control.
RMLS Solution: RMLS protects satellite telemetry and mission-critical functions by applying resilient decision frameworks and MTD to make model behavior unpredictable.

Satellites

Financial Institutions

Problem: Banks and FinTech platforms rely on ML for fraud detection and risk scoring, making these models high-value targets for manipulation.
RMLS Solution: RMLS secures fraud detection engines by automatically mutating detection models, thwarting attempts to reverse-engineer or poison them.

Healthcare Delivery Organizations

Problem: Hospitals and healthcare providers use ML for diagnostics and monitoring. Compromised algorithms can lead to misdiagnosis or service disruption.
RMLS Solution: RMLS continuously monitors and rotates diagnostic models, ensuring patient safety and regulatory compliance.

Healthcare

Airports

Problem: Modern airports depend on AI for logistics, asset tracking, and threat detection. Static models can be fed malicious data and exploited.
RMLS Solution: RMLS shields critical gateways with autonomic, self-protecting AI that evolves in real time, blocking sabotage attempts and ensuring smooth operations.

Airports

Seaports

Problem: Modern ports depend on AI for logistics, asset tracking, and threat detection. Static models can be fed malicious data and exploited.
RMLS Solution: RMLS shields critical gateways with autonomic, self-protecting AI that evolves in real time, blocking sabotage attempts and ensuring smooth operations.

Seaports

How RMLS Works

 

Resilient Middleware Services

Containerized, redundant versions of core AI/ML services run concurrently. This ensures uninterrupted functionality, even if one instance is attacked.

Autonomic Management and Orchestration (AMO)

A built-in Observer module monitors system performance and anomalies. The Controller reacts instantly, reconfiguring services, deploying countermeasures, and maintaining uptime without human intervention.

Anomaly Behavior Analysis Units (BAUs)

RMLS uses two types of BAUs to detect manipulation:
  • Autoencoder-Based BAUs: Neural nets trained to identify "normal" behavior highlight outliers based on high reconstruction error.
  • Regression-Based BAUs: Compare live sensor data against predicted behavior from historical patterns to flag discrepancies in real time.

Simulation & Proof-of-Concept Environment

RMLS includes a safe virtual testbed that replicates live environments, satellites, sensors, and ICS, allowing organizations to safely test resilient configurations, fine-tune anomaly thresholds, and validate failover scenarios before live deployment.

Zero-Trust Architecture

RMLS enforces strict Zero-Trust Principles: No trust is granted by default, and every action is authenticated, authorized and validated in line with DoD and NIST standards.

Moving Target Defense (MTD)

Constantly rotates ML ensembles and virtual network functions, making it virtually impossible for adversaries to map and exploit system behavior.

Protect your wireless airspace with LOCH's RMLS today

Core Problems RMLS solves:

 

Static Predictability


Static models become easy for attackers to reverse-engineer and target. 
RMLS solves this by dynamically rotating models and configurations, creating an ever-shifting attack surface.

Data Poisoning & Adversarial Inputs


RMLS integrates anomaly detection that recognizes malicious or abnormal data streams in real-time and neutralizes manipulation attempts.

Single Point of Failure:


RMLS deploys redundant, virtualized models that can instantly take over if one instance is compromised, maintaining continuous operation.

Zero-Trust Gaps

RMLS enforces a Zero-Trust approach where every user, device, and transaction is authenticated, authorized, and continuously validated.

From critical infrastructure and healthcare to electronic warfare and autonomous transport, RMLS provides the foundational resilience organizations need to stay secure and operational, even in the face of the most advanced cyber threats.

It keeps AI and ML systems secure by defending the algorithms at their core. It ensures operational uptime by autonomously adapting to anomalies and disruptions without manual intervention.

RMLS helps organizations stay ahead of emerging cyber threats by continuously rotating system logic, preventing attackers from gaining a predictable foothold.

Defend the algorithms driving your digital transformation. Secure your mission at the core.

LOCH Technologies Wireless Cybersecurity Solutions

Protect your wireless airspace, connected assets, and AI/ML with LOCH's RMLS