Process Fingerprinting Side-Channel

• Process fingerprinting side-channels are techniques that leverage physical and microarchitectural signals to infer process identity, structure, or activity.
• The methodologies involve precise measurements and feature extraction from cache, power, interconnect, and other system signals to achieve high classification accuracy.
• Practical defenses include noise injection, resource isolation, and constant-time operations, though they must balance security with performance trade-offs.

Process Fingerprinting Side-Channel

Process fingerprinting side-channels encompass a class of attacks in which an adversary exploits low-level, often unintended, physical or microarchitectural dependencies to infer the identity, structure, or activity patterns of processes running on a system. Such channels arise from shared resources—including cache hierarchy, power consumption, bus contention, timing signals, and even file system mechanics—providing attackers with rich “fingerprints” specific to the execution profile of target processes. These attacks are applicable across diverse domains, such as deep learning workloads, web browsers, operating systems, cloud accelerators, cryptographic primitives, and even cyber-physical and AR/VR systems.

1. Taxonomy and Mechanisms

Process fingerprinting side-channels can be classified by the type of physical or logical leakage exploited:

• Microarchitectural channels: Cache state (Prime+Probe, Flush+Reload, LLC occupancy), instruction- or function-level cache mappings, and transient-execution artifacts (Hong et al., 2018, Shusterman et al., 2018, Weber et al., 2023, Joshi et al., 28 Jan 2025, Ferguson et al., 9 Jan 2024).
• Power and frequency channels: External power draw (including wireless charging (Cour et al., 2021)), on-die frequency sensors (Dipta et al., 2022, Taneja et al., 2023, Dipta et al., 16 Apr 2024), and system temperature.
• Interconnect channels: PCIe usage patterns in FPGAs (Fang et al., 2023) and NVLink performance counters/timing in multi-GPU setups (Zhang et al., 22 Mar 2025).
• DRAM/fence timing: Latency shifts due to RowHammer mitigations (Bostancı et al., 23 Mar 2025).
• File-system IO timing: Execution latency of system calls like syncfs reflecting I/O patterns of other domains (Gu et al., 16 Nov 2024).
• Acoustic/electromagnetic: Robot or device-generated acoustic signatures (Shah et al., 2022).
• GPU activity profiling in AR/VR: Low-frequency GPU usage patterns correlated with user and app actions (Son et al., 12 Sep 2025).

In all cases, the attacker typically measures a side observable (timing, power, occupancy, contention, etc.) exposed due to resource sharing or weak abstraction boundaries.

2. Threat Models and Observability

Process fingerprinting side-channels generally presume the following adversarial models:

• Co-located attacker: The attacker shares physical or logical resources (cache, NVLink, DRAM row/bank, etc.) with the victim. This applies to cloud or multi-user systems (Hong et al., 2018, Fang et al., 2023, Zhang et al., 22 Mar 2025, Dipta et al., 16 Apr 2024).
• Cross-domain observer: The attacker occupies a sandboxed or containerized domain but exploits shared system components or sensors, including cloud sandboxes and VMs (Dipta et al., 16 Apr 2024, Gu et al., 16 Nov 2024).
• Passive external observer: The attacker leverages externally measurable phenomena such as power draw (via wireless charging or USB (Cour et al., 2021)), acoustic emanations (Shah et al., 2022), or exposed sensor data (Taneja et al., 2023, Son et al., 12 Sep 2025).
• Web attacker: The adversary runs (possibly obfuscated) JavaScript or WebGPU code in the browser or WebXR, and infers activity in other tabs or contexts (Shusterman et al., 2018, Ferguson et al., 9 Jan 2024, Son et al., 12 Sep 2025, Taneja et al., 2023).
• No direct victim interaction: Often, the attacker does not require query or probe access to the victim process, relying solely on passive measurement (Hong et al., 2018, Shusterman et al., 2018).

Observability is typically enabled by accessible hardware sensors, OS-provided APIs, shared libraries, high-resolution (or even low-resolution) timers, or direct physical connections.

3. Methodologies for Fingerprinting

The core steps in process fingerprinting via side-channels are:

1. Measurement: Collect raw side-channel signals (e.g., cache probe times, power/current traces, GPU performance metrics, syncfs latencies, acoustic features).
   • Cache fingerprinting: Repeated cycles of flush/access/time (Hong et al., 2018, Shusterman et al., 2018, Joshi et al., 28 Jan 2025) or overall occupancy measurements (Shusterman et al., 2018, Ferguson et al., 9 Jan 2024).
   • Power/frequency: Current traces sampled at sub-kHz frequency (Cour et al., 2021), on-die frequency readouts (Dipta et al., 2022, Dipta et al., 16 Apr 2024), or continuous GPU/CPU sensor polling (Taneja et al., 2023, Son et al., 12 Sep 2025).
   • Interconnect: PCIe/NVLink throughput measured via counters or communication timings (Fang et al., 2023, Zhang et al., 22 Mar 2025).
   • File system: High-precision timing of syncfs or similar calls (Gu et al., 16 Nov 2024).
   • Acoustic: Microphone-recorded robot movement; feature extraction via STFT, MFCC, etc. (Shah et al., 2022).
2. Feature extraction: Distillation of discriminating features—such as function call counts, occupancy traces, time series statistics, or frequency transforms—from raw measurements. Machine learning models (decision trees, CNNs, LSTMs, XGBoost, etc.) are often used.
3. Fingerprint reconstruction/classification:
   • Direct attribute reconstruction: e.g., mapping function call traces to DNN layer counts (Hong et al., 2018).
   • Behavioral classification: e.g., CNNs/LSTMs that classify memorygrams or current traces to application type or website (Shusterman et al., 2018, Cour et al., 2021, Joshi et al., 28 Jan 2025, Zhang et al., 22 Mar 2025).
   • Meta-modeling for architectural family or teacher network inference (Hong et al., 2018).
   • Temporal/spatial correlation: e.g., using inter-keystroke timings (Weber et al., 2023, Dipta et al., 2022) or object-level inference in AR/VR (Son et al., 12 Sep 2025).
4. Evaluation and benchmarking: Cross-validation and confusion-matrix analyses with accuracies commonly exceeding 70–90% for top-choice and up to 99% for closed sets (Hong et al., 2018, Shusterman et al., 2018, Joshi et al., 28 Jan 2025, Son et al., 12 Sep 2025, Zhang et al., 22 Mar 2025).

4. Practical Impact and Attacks

These fingerprinting channels yield powerful attacks across multiple system contexts:

• DNN and ML model extraction: DeepRecon recovers full DNN architectures given only passive function call observations, with meta-models distinguishing network families at 100% accuracy (Hong et al., 2018).
• Website fingerprinting: Cache occupancy, NVLink throughput, current traces, DRAM latency (via RowHammer mitigations), and syncfs delays all enable statistically robust fingerprinting of visited websites in mainstream OSes and browsers, including Tor (Shusterman et al., 2018, Cour et al., 2021, Zhang et al., 22 Mar 2025, Bostancı et al., 23 Mar 2025, Gu et al., 16 Nov 2024, Taneja et al., 2023, Ferguson et al., 9 Jan 2024).
• Inter-keystroke or behavioral profiling: Attacks exploiting IDT cache states, on-die CPU or GPU frequency, or cache occupancy have achieved ≥88% accuracy for activity inference (Weber et al., 2023, Dipta et al., 2022, Taneja et al., 2023).
• Cross-container and cloud attacks: Fingerprint-based leakage shown across VMs (NVLink), across containers (syncfs), and in cloud FPGA multi-tenancy (Fang et al., 2023, Zhang et al., 22 Mar 2025, Gu et al., 16 Nov 2024, Dipta et al., 16 Apr 2024).
• Cyber-physical and AR/VR systems: Robot movement and AR/VR user/app interactions reliably inferred via acoustic and GPU activity profiling (Shah et al., 2022, Son et al., 12 Sep 2025).
• Power side-channels in charging and app usage: Wireless and wired charging current leaks enable website fingerprinting (up to 95% accuracy) and are robust to battery level, device, and time drift (Cour et al., 2021).

5. Countermeasures and Defenses

The surveyed literature discusses a variety of defenses, each with distinct trade-offs:

• Noise and randomization: Injecting random tasks in the same resource pool (e.g., TinyNet decoy models (Hong et al., 2018), cache/memory masking (Shusterman et al., 2018), frequency noise (Dipta et al., 2022, Dipta et al., 16 Apr 2024), random activation counters (Bostancı et al., 23 Mar 2025)) reduces fingerprint accuracy but imposes performance overhead.
• Isolation and resource partitioning: Partitioning cache or memory regions among security domains (static/dynamic cache partitioning (Ferguson et al., 9 Jan 2024), DRAM bank/rank partitioning (Bostancı et al., 23 Mar 2025), NVLink counter and channel isolation (Zhang et al., 22 Mar 2025), PCIe delay obfuscation (Fang et al., 2023)) can prevent inter-domain contamination—though at the possible cost of underutilization or complexity.
• Obfuscation and masking: Dynamic modification of execution paths (oblivious computations (Hong et al., 2018)), dummy memory accesses, or “flattening” scheduling in CPUs and GPUs (Taneja et al., 2023, Son et al., 12 Sep 2025).
• Restriction and system call redesign: Limiting access to high-resolution sensors/counters (e.g., GPU, CPU, or performance counters; (Taneja et al., 2023, Dipta et al., 16 Apr 2024, Zhang et al., 22 Mar 2025)), or restricting filesystem flushes to per-process dirty pages (Gu et al., 16 Nov 2024).
• Constant-time operations: For DRAM, enforcing constant-latency responses can block timing channels (Bostancı et al., 23 Mar 2025), though such approaches may severely penalize memory throughput.
• Anomaly detection: Flagging or restricting abnormal monitoring rates or probing patterns (Zhang et al., 22 Mar 2025, Shusterman et al., 2018).
• Software and hardware integration: Secure HDL practices, obfuscation logic in processor datapaths (F et al., 2019), or architectural changes to mitigate systematic side-channel exposure.

6. Evaluation and Limitations

• Experiments consistently show that process fingerprinting channels are resilient to many classical and first-generation mitigations (such as timer reduction, traffic shaping, or browser site isolation).
• Sophisticated fingerprinting is possible even at low sampling rates (e.g., 1 Hz GPU counters (Son et al., 12 Sep 2025), coarse timers in Tor (Shusterman et al., 2018), slow filesystem calls (Gu et al., 16 Nov 2024)).
• Defenses introduce trade-offs between performance, usability, and the degree of privacy protection.
• Effective countermeasures often require hardware or architectural changes, which may not be retrofittable.
• Open questions persist regarding the generalizability across hardware generations, cross-device transferability of fingerprints, optimal defense deployment in cloud environments, and the composability of multiple side channels.
• The attacker, in many cases, remains untargetable by conventional access policies, illustrating the need for co-design at the intersection of OS, hardware, and application domains.

7. Future Directions and Research Challenges

The field is evolving with the increasing heterogeneity of systems (SoCs, GPUs, AR/VR hardware, cloud accelerators), further exposing the risk landscape:

• Broader hardware and dataset testing: As seen in (Shusterman et al., 2018), larger and more diverse datasets, and hardware-agnostic attacks, will clarify robustness and real-world exploitability.
• New domains of leakage: Application of side-channel fingerprinting in cyber-physical, IoT, and immersive computing presents both technical and privacy challenges (Shah et al., 2022, Son et al., 12 Sep 2025).
• Multi-modal and cross-channel attacks: Future attacks may combine multiple side channels (power, timing, cache, interconnect) to evade defense-in-depth.
• Machine learning for defense: Hybrid deep learning models that fuse spatial and temporal profiling can detect or disrupt side-channel attacks at high accuracy, but their generalization and efficiency remain active research topics (Joshi et al., 28 Jan 2025).
• Dynamic adaptation: As attackers adapt to countermeasures, real-time detection and dynamic system reconfiguration will likely become essential components of secure architectures.

Process fingerprinting side-channels, leveraging a diverse range of hardware and OS artifacts, represent a persistent threat to system confidentiality across classic, modern, and emerging computing platforms. The ongoing research into both attack methodology and defensive countermeasures will require close integration of hardware, software, and security engineering to mitigate their impact effectively.