System Integrity Monitoring Overview

• System Integrity Monitoring is a discipline that continuously verifies a system's state against a known-good configuration using methods like hash comparisons and real-time event checks.
• It employs diverse methodologies including machine learning, formal modeling, and multi-sensor fusion to detect anomalies and ensure timely fault localization.
• Deployments balance detection latency and performance overhead by integrating multi-layer checks, cryptographic attestation, and policy-driven change management.

System integrity monitoring refers to the technical discipline and operational process of continuously verifying that the computational state and operational posture of a system align with a specified "known-good" or policy-compliant configuration, typically to detect, localize, and provide actionable forensics against malicious subversion, unauthorized modification, malfunctions, or faults—at any abstraction layer, from hardware to application logic. This encompasses ongoing assessments of the fidelity, trustworthiness, and availability of critical system components, with the overarching objective of rapid anomaly detection, accurate fault attribution, and mitigation or containment before security, safety, or reliability guarantees are violated.

1. Foundations, Definitions, and Key Metrics

System integrity monitoring frameworks universally enforce regular, structured verification to detect deviation from a defined baseline—variously established as a set of digital fingerprints, configuration manifests, or real-time behavioral models (Mao et al., 6 Dec 2025, Paliwal et al., 21 Dec 2024, R et al., 11 Nov 2025). The foundational pillars are:

• Consistency Check: Compare the current system state (e.g., file hashes, kernel module lists, hardware IDs, configuration registers) to precomputed reference values or dynamic thresholds.
• Runtime Detection: Integrate real-time event monitoring (trapping process, registry, or network events; SMM/TEE trapping; out-of-band side-channel acquisition) to detect live faults.
• Protection Level (PL), Integrity Risk (IR), and Alert Limit (AL): Particularly in safety-critical domains, integrity is quantified as a probability or bound that the system will not enter "hazardously misleading information" states without an alert (Nayak et al., 7 Feb 2025, Tian et al., 30 Oct 2024).
• Key Metrics: Availability $A = \frac{MTBF}{MTBF + MTTR}$, failure rate $\lambda$, false-alarm rate (FAR), and detection latency $L$ are standard (Keyes et al., 9 Dec 2025).

This multi-level, metric-driven view enables integrity monitoring to support both immediate operational alerting (for e.g., rootkit infection (Delgado et al., 2018), registry persistence tampering (R et al., 11 Nov 2025)) and longer-term forensic comparison.

2. Methodological Approaches

Integrity monitoring spans a spectrum of technical methods:

• Hash-Based Comparison and Fingerprinting: Hashes of critical files, binaries, configuration, and kernel data (e.g., IMA, TPM PCRs) are collected in clean states and compared at interval or on-demand (Paliwal et al., 21 Dec 2024, Ozga et al., 2021, Faisal et al., 16 May 2024). Modern approaches chain these using Merkle trees for tamper evidence and sign the root via hardware security modules.
• Runtime Monitoring and Decomposition: Techniques such as EPA-RIMM leverage decomposed, performance-aware, SMM-based checks running in isolated environments, minimizing system perturbation through task/batch scheduling and bounded-latency SMM residency (Delgado et al., 2018).
• Co-Monitoring and Formal Modeling: HW/SW co-monitoring shadows every device/driver transaction with a formally verified device- and protocol-level reference model, using symbolic execution and property-checking to enforce runtime conformance and temporal logic invariants (Lei et al., 2019).
• Machine Learning and Anomaly Detection: ML-based engines (unsupervised Isolation Forest, fine-tuned LLM classifiers) consume multivariate event streams (process executions, file changes, registry edits) and assign anomaly/risk scores; ensemble decision logic mitigates FP/FN rates (R et al., 11 Nov 2025, Khorrami et al., 22 Jan 2025).
• Hybrid and Defense-in-Depth (Multi-Modal): Subcomponent-level monitoring (direct NIC telemetry, GPU/CPU side-channel, keyboard MCU) achieves anomaly detection even under main-CPU compromise, with multi-channel fusion for robust, low-latency, high-confidence alerts (Khorrami et al., 22 Jan 2025).

In heterogeneous ecosystems and regulated environments (e.g., health systems, critical infrastructure), policies may directly encode change-management workflows, update whitelists, and forensic logging for chain-of-custody assurance (Mao et al., 6 Dec 2025, Paliwal et al., 21 Dec 2024).

3. Domain-Specific Implementations and Technical Platforms

System integrity monitoring is realized at all system tiers, each with domain-specific protocols and trust anchors:

Application Domain	Trust Anchor / Technique	Key Reference
OS, Kernel & Hypervisor	TPM/IMA, SMM, hardware permission tbl	(Yitbarek et al., 2019, Delgado et al., 2018)
Endpoint Security	WMI/ETW, ML event detection, SIEM	(R et al., 11 Nov 2025)
Critical Systems / IIoT	eUICC/iSIM, DLT-backed attestation	(Faisal et al., 16 May 2024)
Multi-Modal Sensing	NIC/GPU/CPU/keyboard side-channels	(Khorrami et al., 22 Jan 2025)
Vehicle Navigation	RAIM, GNSS/INS/Perception fusion	(Nayak et al., 7 Feb 2025, Tian et al., 30 Oct 2024, Yan et al., 6 Jul 2025)
TEE/Embedded	TrustZone, in-TEE policy, attestation	(Mao et al., 6 Dec 2025)
Health Care AI Systems	Pipeline uptime, automated error metrics	(Keyes et al., 9 Dec 2025)

Deployments such as EPA-RIMM (SMM-based) measure and hash privileged memory state, kernel code, and configuration registers, with measured SMM entry/exit times and chunked measurement windowing to limit performance impact (e.g., $T_{max} = 150\,\mu$s, per-bin work <280 $\mu$s for 1KB, detection overhead <5% throughput loss) (Delgado et al., 2018). Neverland achieves zero-runtime-overhead integrity protection for kernel code via immutable, in-hardware permission tables and lockable registers, incurring <1.2% silicon area and no page-table dependencies (Yitbarek et al., 2019). Out-of-band, external power-based attestation (PowerAlert) utilizes random subset kernel checks and power profile validation, achieving probabilistic coverage unforgeable by kernel compromise (Fawaz et al., 2017).

4. Multi-Sensor and Multi-Layer Integrity: Protection, Detection, and Fault Localization

Integrity monitoring extends to cyber-physical and sensor-fusion scenarios, where measurements are fused from GNSS, IMU, vision, and odometry:

• Statistical Consistency and Covariance Propagation: Outlier rejection via $\chi^2$ innovation tests in Kalman filters ensures only statistically consistent updates are fused, raising alarms and vetoing updates for grossly inconsistent readings; empirical evaluation shows error bounds shrink by up to 40% with integrity monitoring (Harr et al., 2018).
• Factor-Graph-Based and Nonlinear IM: Modern navigation stacks (e.g., IM-GIV) derive closed-form protection levels from factor graph Jacobians and covariance, supporting nonlinear batch processing and multi-fault detection (6 fault modes), achieving 100% integrity availability post-fault-exclusion (Tian et al., 30 Oct 2024).
• Particle Filtering and Risk-Bounded Fusion: For vision-GNSS fusion, particle filters jointly estimate state, adaptively down-weight faulty measurements using EM and KL-divergence metrics, and derive PAC-Bayes risk bounds on hazardous misleading information (HMI) (Mohanty et al., 2021).
• Range-Domain Jackknife Detection: High-availability integrity in multi-constellation GNSS leverages non-Gaussian error models, jackknife residuals, and Bonferroni-adjusted thresholds to bound vertical protection level (VPL), supporting fault detection up to $k_\text{max}$ simultaneous faults with tight integrity-risk bounds (Yan et al., 6 Jul 2025).

Fault detection, exclusion, and robust error bounding are foundational: when suspicious behavior is statistically confirmed, the faulty sensor/module is isolated, excluded from the estimate, and an integrity alert is raised, with subsequent automatic system re-initialization or human triage depending on criticality (Harr et al., 2018, Tian et al., 30 Oct 2024, Nayak et al., 7 Feb 2025).

5. Security, Threat Models, and Attestation

Integrity monitoring systems are architected to address diverse threat models:

• Software Supply Chain and Update Integrity: Trusted Software Repositories (TSR) mediate package installation and updates via in-SGX enclaves, digitally sanitize scripts, and inject deterministic signatures—ensuring file/system update does not break remote attestation and supporting ~99.76% of Alpine Linux packages at 1.18x median overhead on package insertion (Ozga et al., 2021).
• Remote Attestation and Cryptographic Evidence: Hardware-bound attestation (e.g., TPM+IMA, eUICC/iSIM applets, SMM) collects and signs measurements, maintaining a tamper-evident log for auditability and proof to remote verifiers, leveraging consensus or quorum for update approval and software roll-back (Faisal et al., 16 May 2024, Paliwal et al., 21 Dec 2024, Mao et al., 6 Dec 2025).
• Runtime Control-Flow and Device Conformance: HW/SW co-monitoring formally shadows every device action, detecting even transient protocol violations or unauthorized state transitions that evade pure signature or hash-based approaches; all practical bugs discovered by this method have corresponded to real vulnerabilities (Lei et al., 2019).
• Cross-Layer Monitoring and Tamper Resistance: Multimodal defense-in-depth architectures obtain measurement from subcomponents inaccessible to rootkits (SmartNIC, keyboard MCU, hardware energy meters) to preserve visibility even under main-CPU compromise (Khorrami et al., 22 Jan 2025).

Policy-driven frameworks (PDRIMA) for TEEs (e.g., ARM TrustZone) implement runtime hash chain updates, time-based measurement triggers, and in-TEE attestation agents, closing the boot-to-runtime visibility gap and enabling robust remote validation with fine-grained control over measurement intervals and targets (Mao et al., 6 Dec 2025). Performance overhead in prototyped implementations (e.g., OP-TEE, Raspberry Pi 3 B+) is measured at 22–28% for measurement, 35–40% with in-line appraisal, across user applications of 51–248 KB.

6. Practical Considerations, Limitations, and Deployment Trade-offs

System integrity monitoring frameworks must reconcile detection coverage, latency, and system/resource impact:

• Performance-Critical Tuning: Architectural decisions—such as SMM chunk size, SMI frequency, or hash windowing—must maintain detection latency and throughput within specified thresholds (Delgado et al., 2018, R et al., 11 Nov 2025). For large fleets or time-sensitive systems (CAVs, health AI), checks should be de-synchronized to avoid resource spikes (Paliwal et al., 21 Dec 2024).
• Update Handling and Change Management: False positive alerts may arise from legitimate updates, hardware changes, or benign file churn (e.g., logs, temp files). Mitigation includes adaptive whitelisting, change request workflows, auto-baselining, and explicit policy exceptions (Paliwal et al., 21 Dec 2024, Ozga et al., 2021).
• Security Limitations: Some techniques do not detect semantic data corruption (e.g., pointer attacks in unlocked kernel structures), transient fault states (e.g., SMM-based gaps between chunk hashes), or require architectural assumptions (e.g., trust in secure boot and hardware chain) (Yitbarek et al., 2019, Delgado et al., 2018).
• Hardware/Software Complexity: Multimodal side-channel approaches require additional instrumentation or specialized hardware; SMM/TEE-based strategies must balance memory footprint and measurement overhead; co-monitoring approaches can face symbolic execution bottlenecks under high I/O rates (Lei et al., 2019, Khorrami et al., 22 Jan 2025).
• Standards and Interoperability: For vehicle and AI health systems, alignment with ISO/SAE standards mandates that protection levels, integrity risk quantification, and review cadences be formally defined, with embedded review, audit, and escalation into governance processes (Keyes et al., 9 Dec 2025, Nayak et al., 7 Feb 2025).

7. Outlook and Future Research Directions

Open problems center on formalizing and unifying integrity quantification across domains, especially in multi-sensor, AI-enabled, and cooperative/CPS contexts:

• Unified Integrity Metrics for Perception/AI: There is a substantive gap in deriving operational protection levels and risk metrics from black-box neural components or fused perception systems; research into adversarial robustness and uncertainty calibration remains ongoing (Nayak et al., 7 Feb 2025, Keyes et al., 9 Dec 2025).
• Cooperative and Distributed Monitoring: V2X and distributed device networks demand methods for cross-system fusion of integrity risk, consensus-driven attestation, and federated detection while managing trust relationships and propagation of reputation scores (Faisal et al., 16 May 2024, Nayak et al., 7 Feb 2025).
• Adaptive and Policy-Driven Strategies: Policy-driven frameworks enabling live-updated measurement targets, granular re-measurement intervals, and automated remediation (rollback, device isolation) are required in dynamic or resource-constrained environments (Mao et al., 6 Dec 2025, Paliwal et al., 21 Dec 2024).
• Benchmarking and Standardization: Expansion of open datasets (e.g., public V2X, multi-modal malware) and simulation frameworks to support reproducible evaluation and formal benchmarking of integrity-monitoring techniques is an ongoing need (Nayak et al., 7 Feb 2025, Khorrami et al., 22 Jan 2025).

System integrity monitoring is evolving into a highly technical, multi-layer field synthesizing measurement, statistical learning, cryptographic proofs, formal models, and systems engineering, with its rigor increasingly enforced by regulatory, safety, and security standards across domains.