90 lines
2.2 KiB
Markdown
90 lines
2.2 KiB
Markdown
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## Optional
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**Report your browser version, CPU type, cache size, RAM amount, and OS. We use this information to learn about the attack’s behavior on different machines.**
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- Browser:
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- CPU:
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- Cache sizes:
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- RAM:
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- OS:
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## 1-2
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**Use the values printed on the webpage to find the median access time and report your results as follows.**
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| Number of Cache Lines | Median Access Latency (ms) |
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| --------------------- | -------------------------- |
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| 1 | |
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| 10 | |
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| 100 | |
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| 1,000 | |
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| 10,000 | |
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| 100,000 | |
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| 1,000,000 | |
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| 10,000,000 | |
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## 1-3
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**According to your measurement results, what is the resolution of your `performance.now()`? In order to measure differences in time with `performance.now()``, approximately how many cache accesses need to be performed?**
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## 2-2
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**Report important parameters used in your attack. For each sweep operation, you access N addresses, and you count the number of sweep operations within a time interval P ms. What values of N and P do you use? How do you choose N? Why do not you choose P to be larger or smaller?**
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## 2-3
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**Take screenshots of the three traces generated by your attack code and include them in the lab report.**
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## 2-4
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**Use the Python code we provided in Part 2.1 to analyze simple statistics (mean, median, etc.) on the traces from google.com and nytimes.com. Report the statistic numbers.**
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## 2-6
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**Include your classification results in your report.**
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```
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```
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## 3-2
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**Include your new accuracy results for the modified attack code in your report.**
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```
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```
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## 3-3
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**Compare your accuracy numbers between Part 2 and 3. Does the accuracy decrease in Part 3? Do you think that our “cache-occupancy” attack actually exploits a cache side channel? If not, take a guess as to possible root causes of the modified attack.**
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