1. Peeping Tom in the Neighborhood Keystroke Eavesdropping on Multi-User Systems USENIX 2009 Kehuan Zhang, Indiana University, Bloomington XiaoFeng Wang, Indiana University, Bloomington

2. Agenda 2  Overview  Assumption  Implementation  Experiment  Conclusion

3. Overview  For some command such as ps or top, they need some information about the process  The virtual file system procfs, which discloses such information, locates at /proc/ /stat  Our attack take advantage of the stack information of a process to infer keystrokes Specially ESP 、 EIP 3

4. Overview (cont.) 4  For some command such as ps or top, they need some information about the process  The virtual file system procfs, which discloses such information, locates at /proc/ /stat  Our attack take advantage of the stack information of a process to infer keystrokes Specially ESP 、 EIP Fig. 1: The sketch of keystroke extraction and recognition

5. Assumption  Capability to execute program  Multi-core system  Access to the victim’s information  Attacker can obtain some victim’s typing sample as training data 5

6. Implementation 6  Pattern extraction  Trace logging  Get inter-timing  Keystroke analysis Fig. 1: The sketch of keystroke extraction and recognition

7. Implementation 7  Pattern extraction  Trace logging  Get inter-timing  Keystroke analysis Fig. 2: Steps about keystroke pattern extraction

8. Implementation (cont.) 8  Pattern extraction  Trace logging  Get inter-timing  Keystroke analysis Fig. 3: Steps about trace logging and getting inter-timing

9. Implementation (cont.) 9  Pattern extraction  Trace logging  Get inter-timing  Keystroke analysis Fig. 4: Steps about keystroke analysis

10. Pattern extraction  Deterministic program Same input cause the same output, such as vim Use strace to get all system call sequences, then extract the difference False positive check  Non-deterministic program Same input could cause different outputs, almost all GUI programs are non-deterministic An instruction level analysis tool to the function gtk_main_do_event(event) to get it’s event 10

11. Trace logging 11  Attacker’s shadow program keep monitor on /proc/ /stat That’s why we need multi-core system However, the log won’t be complete  Avoid detection Decrease the sample rate Hide CPU usage Fig. 3: Steps about trace logging and getting inter-timing

12. Get inter-timing 12  Use Longest Common Subsequence (LCS) algorithm to compare log with pattern Ignore ASLR by normalize ESP pattern  Use a time duration to get only consecutive keystroke pattern Fig. 5: Pattern matchingFig. 6: Using time duration

13. Keystroke analysis 13  Now, we have got inter-timing sequences  We use Hidden Markov Model (HMM) to guess what victim input and list 4500 candidates N-Viterbi algorithm: use conditional probability Average all probabilities M-N-Viterbi algorithm: use conditional probability Fig. 4: Steps about keystroke analysis

14. Experiment  Environment Intel Core 2 Duo E6700, 3GB RAM Red Hat Linux Enterprise 4.0, Debian 4.0, and Ubuntu 8.04  Evaluation on three public server A Linux workstation in a public machine room (Server 1) A web server of Indiana University that allows SSH connections from its users (Server 2) A server for students’ course projects (Server 3) 72-hour monitoring on these servers that user number range from 1 to 24 14

15. Experiment (cont.) 15 Fig. 11: CPU usage of three real world server during 72 hours Fig. 10: Percentage of keystroke detected versus CPU usage

16. Experiment (cont.) 16  Speculating passwords Training: 15 training keys, each has 13 letters and 2 digits, totally 225 key pairs. We detect 45 inter- timings for each of these pairs from a user Evaluation: select 3 passwords from the space of all possible 8-bytes sequences formed by 15 characters. Our HMM output 4500 candidates

17. Experiment (cont.) 17  Speculating passwords Training: 15 training keys, each has 13 letters and 2 digits, totally 225 key pairs. We detect 45 inter-timings for each of these pairs from a user Evaluation: select 3 passwords from the space of all possible 8-bytes sequences formed by 15 characters. Our HMM output 4500 candidates Fig. 7: Percentage of space to search before find the right password

18. Experiment (cont.) 18  Guess English words Training: use the word frequency of British national corpus to compute transition probabilities Evaluation: random draw a word from 2103 known words with length 3 to 5, then type them Fig. 8: Time distribution of letter pairs

19. Experiment (cont.) 19  Guess English words Training: use the word frequency of British national corpus to compute transition probabilities Evaluation: random draw a word from 2103 known words with length 3 to 5, then type them Fig. 8: Time distribution of letter pairs Fig. 9: Success rate on English word

20. Conclusion  Information leak: one can get others’ keystrokes without any special permission  Trade-off between convenience and security  Contribute for keystrokes detection and extraction method on almost all distributions of Linux 20

21. Future work  More precise detection method for non- deterministic programs  Way to detect keystrokes when system calls are not immediately triggered by keystrokes  Better algorithm to identify English words  Utilize more information to infer other events, such as mouse moving 21

22. The End