2.2 KiB
Optional
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.
- Browser:
- CPU:
- Cache sizes:
- RAM:
- OS:
1-2
Use the values printed on the webpage to find the median access time and report your results as follows.
| Number of Cache Lines | Median Access Latency (ms) |
|---|---|
| 1 | |
| 10 | |
| 100 | |
| 1,000 | |
| 10,000 | |
| 100,000 | |
| 1,000,000 | |
| 10,000,000 |
1-3
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?
2-2
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?
2-3
Take screenshots of the three traces generated by your attack code and include them in the lab report.
2-4
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.
2-6
Include your classification results in your report.
3-2
Include your new accuracy results for the modified attack code in your report.
3-3
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.