explain log verification part
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1 changed files with 33 additions and 2 deletions
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@ -183,12 +183,14 @@ supplement: none,
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columns: (auto,auto),
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columns: (auto,auto),
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gutter: 3pt,
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gutter: 3pt,
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[#image("images/xpsu_illustration.svg", height:90%)],
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[#image("images/xpsu_illustration.svg", height:90%)],
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[Points of measure:
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[Points of Measure:
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- CPU
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- CPU
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- 3x Motherboard 3.3V, 5V and 12V
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- 3x Motherboard 3.3V, 5V and 12V
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- GPU
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- GPU
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- 3x Storage (MOLEX) 3.3V ,5V, and 12V
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- 3x Storage (MOLEX) 3.3V ,5V, and 12V
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- Fans?]
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- Fans?
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Not Points of Measure:
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- Motherboard-Powered Components]
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)
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)
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]
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]
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@ -198,4 +200,33 @@ supplement: none,
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Log Verification
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Log Verification
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]
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#slide(title: "Problem Statement")[
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#align(center)[
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Given a journal of event $J$ and a multivariate time series $t_s$ covering the same time periodand machine, verify that no log was added or removed from the journal.
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]
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]
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#slide(title: "Approaches")[
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- Approach 1:
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#list([Mine patterns from training journal], [apply DSD to each dimension], [Compare])
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#pause
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- Approach 2:
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#list([Extract patterns from training journal],[Train time-series classification model on multivariate data], [Classify power patterns for each event journal entry])
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]
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#slide(title: "Experiment Design / Data Collection")[
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- What OS to consider?
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- What log journal to consider? Linux is easier to collect, windows is more realistic.
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#pause
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- What activity to simulate?
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- Program to fake activity -> Reproducible, Easy
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- Real user -> Realistic, Expensive
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#pause
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- What logs to verify?
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- Previous work on merging similar logs into meta-events.
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- Should consider all logs or limit to verifyable ones?
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#pause
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- Whould dataset present real attacks?
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- Real attacks faking logs are difficult to perform.
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- Faking attack is easy (tamper with $J$) but less realistic.
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]
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