Platform-Invariant Detection Methods

• Platform-Invariant Detection is defined as systems that achieve consistent outcomes across varied platforms by mapping inputs to a unified, platform-agnostic space.
• Techniques such as canonicalization, feature disentanglement, and cross-platform alignment block platform-specific artifacts to ensure robust, generalizable detection.
• Applications span security, malware and quantum bug detection, and sensor fusion, with empirical studies showing significant performance improvements over baseline methods.

Platform-invariant detection refers to algorithmic and systems architectures that enable accurate detection of target phenomena—malicious behavior, semantic content, physical events, or software flaws—under conditions in which the underlying computing, sensing, or data-generation platforms may differ widely in architecture, data schema, modality, interface, or other distributional properties. Unlike standard domain adaptation or naive multi-platform evaluation, platform-invariant detection solutions construct shared intermediate representations, employ invariant feature or causal modeling, or align distributions in a manner that fundamentally blocks platform-specific artifacts from disrupting detection, thus achieving robust generalization and resilience to cross-platform shift.

1. Core Principles and Definitions

Platform invariance is defined as the capacity of a detection system $F$ to achieve stable, high-accuracy outputs $y = F(x)$ or $y = F(x; S)$ for all $x$ drawn from possibly platform-varying distributions $P(X|S)$, where $S$ indexes the set of platforms (hardware, OS, sensor array, language domain, etc.). Formally, for a task distribution $P_S(X, Y)$ with $S \in \mathcal{S}$, a detector $F$ is platform-invariant if

$$\mathbb{E}_{(X, Y)\sim P_{S_i}}[\ell(F(X), Y)] \approx \mathbb{E}_{(X, Y)\sim P_{S_j}}[\ell(F(X), Y)] \qquad \forall S_i, S_j \in \mathcal{S}$$

where $\ell$ is a relevant loss or error metric. Platform invariance is achieved either via:

• Canonicalization: mapping $X$ to a platform-independent space through tokenization, embedding, or intermediate representations.
• Disentanglement: explicit factoring of features into platform-dependent and invariant components.
• Cross-platform differential analysis/detection: comparing outputs or behaviors across platforms to identify divergence directly.

2. System Architectures for Platform-Invariant Detection

2.1 Distributed Heterogeneous N-Variant Execution

The DMON system exemplifies execution-level platform invariance for security. Multiple program variants, each compiled for a different ISA/ABI, are deployed on separate hosts. The system coordinates their lock-step execution, intercepting and canonicalizing all security-sensitive system calls. Each syscall and argument is mapped to a platform-independent identifier and marshaled into a normalized struct, eliminating differences due to instruction encoding, endianness, or calling convention. Detection occurs if any pair of variants diverges beyond a formal threshold in their execution trace after canonicalization: $\Delta(\text{Trace}_i, \text{Trace}_j) > \tau$ where $\Delta$ counts the number of mismatched canonicalized syscall events. This design robustly blocks code-reuse and data-only attacks that require cross-variant alignment, and forces attackers to produce exploits functional across heterogeneous architectures—a task shown to be infeasible in practice (Voulimeneas et al., 2019).

2.2 Cross-Platform Feature Disentanglement

In natural language tasks, platform invariance often requires learning a representation that eliminates spurious platform-specific lexical or semantic cues. The CATCH framework formalizes this by encoding each input via a pre-trained transformer and then disentangling its latent space into a continuous, causal subspace $X_c$ (platform-invariant) and a discrete, target subspace $X_t$ (platform-dependent), using variational auto-encoding. Hate speech presence is predicted only using $X_c$, which empirical t-SNE projections show remains stable across domains, while $X_t$ can vary arbitrarily (Sheth et al., 2023, Sheth et al., 17 Apr 2024).

PEACE leverages two causally-motivated "invariant cues"—sentiment and aggression—across platforms, proving that architectures depending solely on these cues achieve macro-F1 gains up to +6% in cross-platform generalization (Sheth et al., 2023). HATE-WATCH extends this to weakly supervised settings by combining confidence-based reweighting, contrastive loss, and VAE-style reconstruction to disentangle invariant features even in the absence of labeled data on new platforms (Sheth et al., 17 Apr 2024).

2.3 Shared Intermediate Representations for Code and Circuits

Platform-invariant detection in compiled code and quantum software is achieved by lifting source artifacts to a common platform-independent intermediate form:

• DroidNative translates Dalvik, ARM, and x86 binaries to a unified intermediate language (MAIL), with only 21 abstract patterns capturing all semantic operands (assignments, control transfers, calls, etc.). Malware detection is then performed via matching graph structures (annotated CFGs) extracted from MAIL, ensuring resilience to opcode-level, structural, or platform-level obfuscations (Alam et al., 2016).
• QITE defines OpenQASM as a shared assembly representation for quantum circuits. It iteratively runs candidate circuits through multiple real backends, applies platform-specific transformations and optimizations, and then exports to QASM. Divergence after re-import (crash oracle) or semantic non-equivalence (equivalence oracle) indicates platform-specific bugs, even in subtle semantic translations (Paltenghi et al., 21 Mar 2025).

2.4 Cross-Platform Alignment in Sensory and Perception Tasks

Pi3DET-Net achieves perspective and platform-invariant object detection across LiDAR-equipped vehicles, quadrupeds, and drones by:

• Applying random platform jitters to simulate deployment noise.
• Mapping all point clouds to a virtual canonical pose.
• Augmenting features above the backbone RoI-head with geometry-aware descriptors and aligning class-conditional latent distributions via KL divergence. This results in consistent $AP_{[email protected]}$ improvements up to +15 points over strong domain adaptation baselines under severe cross-platform shifts (Liang et al., 23 Jul 2025).

In time-series and dynamic control, touch-based human-UAV interaction detection normalizes all raw sensor streams with parameterized digital IIR prefilters derived from automatically identified inner-loop UAV dynamics. This transformation brings all platforms to a common training domain for a single LSTM detector, enabling 96% cross-platform accuracy (Peringal et al., 2022).

3. Methodologies for Achieving Invariance

Methodologies vary by data type and detection target, but common approaches include:

• Intermediate Language (IL) Lifting: Normalize code to a common instruction set (e.g., MAIL in DroidNative, QASM in QITE), enabling platform-agnostic matching.
• Canonicalization: Systematically map all relevant inputs (syscalls, arguments, struct fields, flag values) to a canonical representation independent of their platform source (e.g., DMON's syscall marshaling).
• Disentanglement via Variational Methods: Use VAEs or contrastive self-supervision to split latent representations into invariant (causal) and variant (spurious/target) subspaces (Sheth et al., 2023, Sheth et al., 17 Apr 2024).
• Causal Feature Anchoring: Explicitly model only those features with theoretical or empirical support for invariance (e.g., sentiment/aggression for hate speech; event frequency for cross-platform diffusion) (Sheth et al., 2023, Gerard et al., 10 Oct 2025).
• Feature/Distribution Alignment: Minimize divergence (e.g., via KL or adversarial loss) between target and source feature distributions, either by direct metric (e.g., Pi3DET's KL-regularized feature head) or via generative mapping (e.g., CycleGAN for multi-platform remote sensing) (Mancoridis et al., 2 Jun 2025).

4. Platforms, Evaluation, and Empirical Findings

Platform-invariance is demonstrated across a variety of detection domains:

System/Domain	Platforms	Representation	Detection Target	Cross-Platform Metric/Result
DMON (Voulimeneas et al., 2019)	x86-64, ARMv8	Canonicalized syscall traces	Malicious behavior	99%+ diversity, robust to code/data ROP
DroidNative (Alam et al., 2016)	Dalvik, ARM, x86, MIPS	MAIL, ACFG/SWOD	Malware	DR 99.2–99.5%, FPR <1.3%, platform-agnostic
CATCH (Sheth et al., 2023)	GAB, Reddit, Twitter, YT	Causal/discrete latent VAE	Hate speech	Macro-F1 +3–5% over baselines, invariant
QITE (Paltenghi et al., 21 Mar 2025)	Qiskit, PennyLane, Pytket, BQSKit	QASM (OpenQASM 2.0)	Quantum bugs	17 bugs (14 fixed), joint coverage >34k lines
Pi3DET-Net (Liang et al., 23 Jul 2025)	Vehicle, Drone, Quad	Vehicle-pose-aligned LiDAR, RoI KL	3D detection	+15 AP$_{3D}$ over best prior
HATE-WATCH (Sheth et al., 17 Apr 2024)	GAB, YT, Reddit, X	Weak-/unlabeled disentangled VAE	Hate speech	Macro-F1 $\sim$0.66 (no label drop)
HeteroBugDetect (Davis et al., 16 Jan 2025)	CPU, GPU (LAMMPS)	Numerical outputs, kernel metrics	HPC bugs	8/20 bugs in 7h, coverage 13.7% (4x baseline)

Empirically, the main reported gains are superior robustness to distributional shifts, strong improvements on unseen platforms, and the surfacing of error cases not exposed by single-platform or non-invariant baselines.

5. Security, Robustness, and Threat Models

Platform-invariant detection fundamentally expands the attack surface an adversary must simultaneously compromise. In DMON, code reuse or data-only attacks that succeed on uniform hosts fail due to differences in gadget offsets and struct layouts. Cross-platform ensemble methods (e.g., superlearner stacking (Gallacher, 2021)) avoid destructive interference while enabling plug-and-play adaptation to new data sources. Disentanglement approaches guarantee, theoretically and empirically, that invariants generalize when platform-specific artifacts drift.

Threat models are typically strengthened over single-platform detection, assuming remote adversaries with binary knowledge but not internal compromise. Analytical diversity metrics quantify the fraction of shared surface an attacker could exploit ($D = 1 - |G_\mathrm{common}|/|G_\mathrm{total}|$, empirically >97% for DMON (Voulimeneas et al., 2019)).

6. Practical Guidelines and Limitations

Best practices extracted across domains include:

• Precisely calibrate and canonicalize all measurement axes prior to detection.
• Parameterize transformations and alignments from data or theoretical models rather than relying on hand-tuned rules.
• When feasible, favor feature-level invariance via disentanglement or causal modeling for interpretability and transfer.
• When unifying at the code or assembly level, ensure IR constructs are expressive enough to encode all platform semantics but minimal to suppress spurious diversity.
• Comprehensive evaluation requires diversity of sources, coverage measurement, and ablation against both in-domain and cross-platform tasks, as shown in Pi3DET-Net, CATCH, and QITE. Limitations include additional runtime, overhead for cross-monitoring or canonicalization (e.g., syscall latency in DMON), dependency on the completeness of intermediate mappings, and residual domain shift if unmodeled platform factors remain.

7. Broader Implications and Future Directions

Platform-invariant detection frameworks establish a stable cross-domain operational envelope not only for security and content moderation but also for software QA (quantum/HPC), aerial and ground sensor fusion, and future ubiquitous multimodal AI deployments. Notable future extensions include:

• Dynamic/interactive adaptation as unseen platforms emerge.
• Transfer of techniques to other high-variance domains (medical imaging, embedded platforms, conversational agents).
• Richer equivalence oracles and mixed-modality representations (QIR, ONNX).
• Signal-processing generalizations of dynamic-invariance transforms as developed in UAV interaction detection and environmental sensing.

Overall, platform-invariant detection synthesizes invariance principles from statistics, causal inference, program analysis, and deep representation learning into a unified paradigm for generalizable, robust, and scalable detection systems across heterogeneous and evolving environments (Voulimeneas et al., 2019, Sheth et al., 2023, Paltenghi et al., 21 Mar 2025, Liang et al., 23 Jul 2025, Sheth et al., 2023, Sheth et al., 17 Apr 2024, Alam et al., 2016, Davis et al., 16 Jan 2025, Gallacher, 2021).