Predictive Integrity Monitor

Motivation

In IoT and robotic systems, hardware failure or slow software degradation creates security vulnerabilities before the device actually crashes. Traditional security tools are reactive — they detect known malware signatures after infection. This tool shifts security from reactive to predictive, fixing the vulnerability before it’s exploited.

A driver consuming excessive memory signals an exploitable leak. A sensor returning anomalous values may indicate tampering. These are security-relevant signals that current monitoring tools ignore.

Proposal

A kernel-level monitoring agent built with Rust and eBPF that continuously analyzes system vital signs and predicts exploitable conditions before they manifest as attacks.

Monitoring Channels

1. Kernel logs — dmesg patterns, driver warnings, OOM events
2. Memory allocation patterns — tracking per-subsystem allocation trends to detect leaks before they become exploitable
3. I/O wait times — detecting storage or network subsystem degradation that could indicate tampering or resource exhaustion attacks

Detection Model

• Time-series anomaly detection on system telemetry (not signature-based)
• Baseline profiling during healthy operation, with drift detection for each monitored metric
• Configurable thresholds and alerting — e.g., “driver X memory usage growing >5% per hour”

Why This Matters for Physical AI

In robotic and IoT systems, a compromised sensor or actuator has real-world consequences. A self-driving system with a degraded LiDAR driver is a safety hazard before it’s a security incident. This monitor treats integrity degradation as a first-class security signal.

Alternatives Considered

• Traditional HIDS (OSSEC, Wazuh) — signature-based, userspace-only, not designed for embedded/IoT constraints
• Prometheus + Grafana — good for observability but no predictive analysis or kernel-level visibility
• Custom watchdog timers — too narrow, only detect crashes, not gradual degradation

Decision

Pending — draft stage.