## 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.** ![Screenshot of traces](./part2/Screenshot.png) ## 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.**