9e38f78379
Two changes per the user's feedback that the slide had unused horizontal space and needed per-PDF context. Layout - The reference scene is now a 2-column grid inside the metric-stack: PDF iframe at ~1.7fr on the left, description panel at ~0.55fr on the right (min 280px). On narrow viewports (<1100px) it falls back to a vertical stack with the description capped to 240px. - Added #zoom=page-width to the iframe URL so the PDF's page fits its column width instead of leaving margins beside an 8.5x11 page rendered in a wider iframe. - Hide the prose card on the references scene — the description panel inside the stack covers what the prose was saying, and freeing the right edge gives the description proper room. Description content - Backend reads <stem>.md sidecar files alongside each PDF and returns the contents in the /api/references payload. - Frontend renders them with a tiny built-in markdown subset (headings, bold/italic, lists, inline code, paragraphs) — no third-party renderer dependency. - Initial draft sidecar .md files committed for the four PDFs currently in references/. Each describes how the paper informs a specific scene of the deck (which model row, which eval protocol, which channel selection). Edit them in place and the panel updates on the next reload.
26 lines
1.2 KiB
Markdown
26 lines
1.2 KiB
Markdown
# Closest direct precedent
|
|
|
|
This paper applies deep learning to **time-series system-call traces
|
|
inside virtual machines** for malware detection — almost exactly the
|
|
framing of this project, just one layer deeper in the stack
|
|
(syscall traces vs `/proc` samples).
|
|
|
|
## What we borrowed
|
|
|
|
- **Windowing strategy.** The paper's fixed-length sliding-window
|
|
formulation over a sequential telemetry stream is the same shape
|
|
we use for our 10-second `/proc` windows fed to LSTM/GRU/RNN.
|
|
- **Recurrent architecture as the simple-but-strong baseline.**
|
|
Their result that an LSTM on raw sequences beats hand-crafted
|
|
feature classifiers on the same data is the cited justification
|
|
for our LSTM/GRU/RNN row of the model comparison.
|
|
- **Per-VM containment posture.** Confirms our choice to run each
|
|
episode in its own throwaway Alpine guest rather than instrumenting
|
|
the host process directly.
|
|
|
|
## Where it differs
|
|
|
|
- Their telemetry is full **syscall traces** (much richer than
|
|
`/proc` resource counters), which is why their numbers don't
|
|
transfer 1-to-1 to our setup. They establish *that* this works;
|
|
we measure how well it works on a thinner, more deployable signal.
|