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WADE: An LSTM-Based Anomaly Detection Engine for WebAssembly Applications — arXiv:2610.31984

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.31984
License (as archived): CC0-1.0
Archived: 2026-10-26T20:07:11.853Z
SHA-256 of the archived text: dba84c4ff1c89019ebca5eddf1834b013b32935a630497ad9c06bd2ad9f1534f
Stable URL of this page: https://metraipolis.com/documento/dc_bfc43bb1ff2f

How to cite

WADE: An LSTM-Based Anomaly Detection Engine for WebAssembly Applications — arXiv:2610.31984 — arXiv (en), https://arxiv.org/abs/2610.31984 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bfc43bb1ff2f (Metraipolis archive dc_bfc43bb1ff2f, sha256 dba84c4ff1c89019ebca5eddf1834b013b32935a630497ad9c06bd2ad9f1534f)

BibTeX

@misc{dcbfc43bb1ff2f,
  title = {WADE: An LSTM-Based Anomaly Detection Engine for WebAssembly Applications — arXiv:2610.31984},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.31984},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bfc43bb1ff2f; sha256 dba84c4ff1c89019ebca5eddf1834b013b32935a630497ad9c06bd2ad9f1534f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.31984. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

WebAssembly (Wasm) enables near-native performance for web applications through a portable and sandboxed execution model. However, its limited runtime protection mechanisms (e.g., the lack of stack canaries and address space layout randomization (ASLR)) leave Wasm applications vulnerable to traditional attacks. We present WADE (WebAssembly Anomaly Detection Engine), a system that detects anomalous runtime behaviors in browser-executed WebAssembly applications. WADE monitors function-level execution traces collected under benign conditions and trains a Long Short-Term Memory (LSTM) network to learn the normal execution behavior of each application. At runtime, the model flags deviations from these learned patterns as potential attacks. Unlike signature-based approaches, WADE builds application-specific behavioral models, enabling detection of both known and previously unseen attacks without per-vulnerability signatures. We evaluate WADE on a dataset of 67 WebAssembly applications spanning ten vulnerability classes, achieving zero false negatives and a 0.42% false positive rate.

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.31984 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-26T19:48:33.253Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).