tor-commits October 2012

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1288 discussions

[tor/master] Use channel_is_bad_for_new_circs(), connection_or_get_num_circs() in main.c
by andrea＠torproject.org 11 Oct '12

11 Oct '12

commit cb62a0b69a7d67b427224ca4c3075b49853a3a1f Author: Andrea Shepard <andrea(a)persephoneslair.org> Date: Fri Sep 7 04:42:13 2012 -0700 Use channel_is_bad_for_new_circs(), connection_or_get_num_circs() in main.c --- src/or/main.c | 10 +++++++--- 1 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/or/main.c b/src/or/main.c index a1b1502..f53bcf3 100644 --- a/src/or/main.c +++ b/src/or/main.c @@ -13,6 +13,8 @@ #define MAIN_PRIVATE #include "or.h" #include "buffers.h" +#include "channel.h" +#include "channeltls.h" #include "circuitbuild.h" #include "circuitlist.h" #include "circuituse.h" @@ -1046,7 +1048,8 @@ run_connection_housekeeping(int i, time_t now) tor_assert(conn->outbuf); #endif - if (or_conn->is_bad_for_new_circs && !or_conn->n_circuits) { + if (channel_is_bad_for_new_circs(TLS_CHAN_TO_BASE(or_conn->chan)) && + !connection_or_get_num_circuits(or_conn)) { /* It's bad for new circuits, and has no unmarked circuits on it: * mark it now. */ log_info(LD_OR, @@ -1064,14 +1067,15 @@ run_connection_housekeeping(int i, time_t now) (int)conn->s,conn->address, conn->port); connection_mark_for_close(conn); } - } else if (we_are_hibernating() && !or_conn->n_circuits && + } else if (we_are_hibernating() && + !connection_or_get_num_circuits(or_conn) && !connection_get_outbuf_len(conn)) { /* We're hibernating, there's no circuits, and nothing to flush.*/ log_info(LD_OR,"Expiring non-used OR connection to fd %d (%s:%d) " "[Hibernating or exiting].", (int)conn->s,conn->address, conn->port); connection_mark_and_flush(conn); - } else if (!or_conn->n_circuits && + } else if (!connection_or_get_num_circuits(or_conn) && now >= or_conn->timestamp_last_added_nonpadding + IDLE_OR_CONN_TIMEOUT) { log_info(LD_OR,"Expiring non-used OR connection to fd %d (%s:%d) "

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[tor/master] Add LD_CHANNEL log domain in log.c
by andrea＠torproject.org 11 Oct '12

11 Oct '12

commit f06880c855a6ac4dd824bed7efcd0bd0436bda32 Author: Andrea Shepard <andrea(a)persephoneslair.org> Date: Fri Sep 7 14:38:16 2012 -0700 Add LD_CHANNEL log domain in log.c --- src/common/log.c | 2 +- 1 files changed, 1 insertions(+), 1 deletions(-) diff --git a/src/common/log.c b/src/common/log.c index 5e2e6b5..ef2e69d 100644 --- a/src/common/log.c +++ b/src/common/log.c @@ -907,7 +907,7 @@ log_level_to_string(int level) static const char *domain_list[] = { "GENERAL", "CRYPTO", "NET", "CONFIG", "FS", "PROTOCOL", "MM", "HTTP", "APP", "CONTROL", "CIRC", "REND", "BUG", "DIR", "DIRSERV", - "OR", "EDGE", "ACCT", "HIST", "HANDSHAKE", "HEARTBEAT", NULL + "OR", "EDGE", "ACCT", "HIST", "HANDSHAKE", "HEARTBEAT", "CHANNEL", NULL }; /** Return a bitmask for the log domain for which <b>domain</b> is the name,

1 0

[tor/master] Call channel_run_cleanup() in main.c, and include a comment explaining how closing or_connections related to channels
by andrea＠torproject.org 11 Oct '12

11 Oct '12

commit 07f9e8fc7d5de57b878118a5179c26eee8466a39 Author: Andrea Shepard <andrea(a)persephoneslair.org> Date: Sat Sep 8 18:58:03 2012 -0700 Call channel_run_cleanup() in main.c, and include a comment explaining how closing or_connections related to channels --- src/or/main.c | 15 +++++++++++++++ 1 files changed, 15 insertions(+), 0 deletions(-) diff --git a/src/or/main.c b/src/or/main.c index f53bcf3..491c48e 100644 --- a/src/or/main.c +++ b/src/or/main.c @@ -400,6 +400,18 @@ connection_unlink(connection_t *conn) if (conn->type == CONN_TYPE_OR) { if (!tor_digest_is_zero(TO_OR_CONN(conn)->identity_digest)) connection_or_remove_from_identity_map(TO_OR_CONN(conn)); + /* connection_unlink() can only get called if the connection + * was already on the closeable list, and it got there by + * connection_mark_for_close(), which was called from + * connection_or_close_normally() or + * connection_or_close_for_error(), so the channel should + * already be in CHANNEL_STATE_CLOSING, and then the + * connection_about_to_close_connection() goes to + * connection_or_about_to_close(), which calls channel_closed() + * to notify the channel_t layer, and closed the channel, so + * nothing more to do here to deal with the channel associated + * with an orconn. + */ } connection_free(conn); } @@ -1525,6 +1537,9 @@ run_scheduled_events(time_t now) * flush it. */ or_state_save(now); + /** 8c. Do channel cleanup just like for connections */ + channel_run_cleanup(); + /** 9. and if we're a server, check whether our DNS is telling stories to * us. */ if (!net_is_disabled() &&

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[tor/master] Call connection_or_close_normally() rather than using connection_mark_for_close()/connection_mark_and_flush() in run_connection_housekeeping() of main.c so that channels get sent to the CLOSING state correctly (avoids an assert otherwise)
by andrea＠torproject.org 11 Oct '12

11 Oct '12

commit 72251385b07a4d6ed2f5d7d972d57a8deb899c4d Author: Andrea Shepard <andrea(a)persephoneslair.org> Date: Mon Sep 10 01:31:08 2012 -0700 Call connection_or_close_normally() rather than using connection_mark_for_close()/connection_mark_and_flush() in run_connection_housekeeping() of main.c so that channels get sent to the CLOSING state correctly (avoids an assert otherwise) --- src/or/main.c | 10 +++++----- 1 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/or/main.c b/src/or/main.c index 491c48e..b87cf52 100644 --- a/src/or/main.c +++ b/src/or/main.c @@ -1071,13 +1071,13 @@ run_connection_housekeeping(int i, time_t now) connection_or_connect_failed(TO_OR_CONN(conn), END_OR_CONN_REASON_TIMEOUT, "Tor gave up on the connection"); - connection_mark_and_flush(conn); + connection_or_close_normally(TO_OR_CONN(conn), 1); } else if (!connection_state_is_open(conn)) { if (past_keepalive) { /* We never managed to actually get this connection open and happy. */ log_info(LD_OR,"Expiring non-open OR connection to fd %d (%s:%d).", (int)conn->s,conn->address, conn->port); - connection_mark_for_close(conn); + connection_or_close_normally(TO_OR_CONN(conn), 0); } } else if (we_are_hibernating() && !connection_or_get_num_circuits(or_conn) && @@ -1086,14 +1086,14 @@ run_connection_housekeeping(int i, time_t now) log_info(LD_OR,"Expiring non-used OR connection to fd %d (%s:%d) " "[Hibernating or exiting].", (int)conn->s,conn->address, conn->port); - connection_mark_and_flush(conn); + connection_or_close_normally(TO_OR_CONN(conn), 1); } else if (!connection_or_get_num_circuits(or_conn) && now >= or_conn->timestamp_last_added_nonpadding + IDLE_OR_CONN_TIMEOUT) { log_info(LD_OR,"Expiring non-used OR connection to fd %d (%s:%d) " "[idle %d].", (int)conn->s,conn->address, conn->port, (int)(now - or_conn->timestamp_last_added_nonpadding)); - connection_mark_for_close(conn); + connection_or_close_normally(TO_OR_CONN(conn), 0); } else if ( now >= or_conn->timestamp_lastempty + options->KeepalivePeriod*10 && now >= conn->timestamp_lastwritten + options->KeepalivePeriod*10) { @@ -1103,7 +1103,7 @@ run_connection_housekeeping(int i, time_t now) (int)conn->s, conn->address, conn->port, (int)connection_get_outbuf_len(conn), (int)(now-conn->timestamp_lastwritten)); - connection_mark_for_close(conn); + connection_or_close_normally(TO_OR_CONN(conn), 0); } else if (past_keepalive && !connection_get_outbuf_len(conn)) { /* send a padding cell */ log_fn(LOG_DEBUG,LD_OR,"Sending keepalive to (%s:%d)",

1 0

[tor/master] Call channel_tls_free_all() and channel_free_all() from tor_free_all in main.c
by andrea＠torproject.org 11 Oct '12

11 Oct '12

commit a9a75ee59a719f938b02d48c6df3db649cf32cb9 Author: Andrea Shepard <andrea(a)persephoneslair.org> Date: Wed Sep 12 16:31:08 2012 -0700 Call channel_tls_free_all() and channel_free_all() from tor_free_all in main.c --- src/or/main.c | 2 ++ 1 files changed, 2 insertions(+), 0 deletions(-) diff --git a/src/or/main.c b/src/or/main.c index b87cf52..908e490 100644 --- a/src/or/main.c +++ b/src/or/main.c @@ -2476,6 +2476,8 @@ tor_free_all(int postfork) circuit_free_all(); entry_guards_free_all(); pt_free_all(); + channel_tls_free_all(); + channel_free_all(); connection_free_all(); buf_shrink_freelists(1); memarea_clear_freelist();

1 0

[torflow/master] Clarify that trials are circuit counts in output.
by mikeperry＠torproject.org 11 Oct '12

11 Oct '12

commit 04ed8e5dc3b357f6d7c012b6192f7c841b04aaed Author: Mike Perry <mikeperry-git(a)fscked.org> Date: Wed Oct 10 18:56:22 2012 -0700 Clarify that trials are circuit counts in output. --- CircuitAnalysis/PathBias/path_bias.py | 48 ++++++------ CircuitAnalysis/PathBias/results.txt | 145 +++++++++++++++++---------------- 2 files changed, 97 insertions(+), 96 deletions(-) diff --git a/CircuitAnalysis/PathBias/path_bias.py b/CircuitAnalysis/PathBias/path_bias.py index 54e8581..f3fc1bd 100755 --- a/CircuitAnalysis/PathBias/path_bias.py +++ b/CircuitAnalysis/PathBias/path_bias.py @@ -517,68 +517,68 @@ def main(): if True: print "\n\n===================== False Positives ============================" - print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" + print "\nStartup false positive counts at [num_circs, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" print brute_force(lambda x,y: x<y, startup_false_positive_test, - #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.80, 0.80), (20,200), (70, 70)], - [0, -0.1, 20, 5]) + #false_positive_test(num_circs, success_rate, min_circs, path_bias_pct): + [(1000000,1000000), (0.80, 0.80), (25,250), (70, 70)], + [0, -0.1, 25, 5]) - print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" + print "\nStartup false positive counts at [num_circs, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" print brute_force(lambda x,y: x<y, startup_false_positive_test, - #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.45, 0.45), (20,200), (30, 30)], - [0, -0.1, 20, 5]) + #false_positive_test(num_circs, success_rate, min_circs, path_bias_pct): + [(1000000,1000000), (0.45, 0.45), (25,250), (30, 30)], + [0, -0.1, 25, 5]) - print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(1000000,1000000), (0.70, 0.70), (100,500), (70, 70)], [0, -0.1, 50, 5]) - print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(1000000,1000000), (0.75, 0.75), (100,500), (70, 70)], [0, -0.1, 50, 5]) - print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(1000000,1000000), (0.80, 0.80), (100,500), (70, 70)], [0, -0.1, 50, 5]) - print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(1000000,1000000), (0.55, 0.55), (100,500), (50, 50)], [0, -0.1, 50, 5]) - print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(1000000,1000000), (0.60, 0.60), (100,500), (50, 50)], [0, -0.1, 50, 5]) - print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(1000000,1000000), (0.45, 0.45), (100,500), (30, 30)], [0, -0.1, 50, 5]) @@ -587,16 +587,16 @@ def main(): print "\nDoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]:" print brute_force(lambda x,y: x>y, dos_attack_test, - #dos_attack_test(g, trials, success_rate, dos_success_rate, path_bias_pct): - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #dos_attack_test(g, num_circs, success_rate, dos_success_rate, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(0.80, 0.80), (0.05,0.05), (30, 30), (200, 1000)], [-0.1, -0.1, 5, 100]) print "\nDoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]:" print brute_force(lambda x,y: x<y, dos_attack_test, - #dos_attack_test(g, trials, success_rate, dos_success_rate, path_bias_pct): - #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + #dos_attack_test(g, num_circs, success_rate, dos_success_rate, path_bias_pct): + #false_positive_test(num_circs, success_rate, scale_circs, path_bias_pct): [(0.80, 0.80), (0.25,0.05), (30, 30), (500, 500)], [-0.1, -0.1, 5, 100]) diff --git a/CircuitAnalysis/PathBias/results.txt b/CircuitAnalysis/PathBias/results.txt index d8e1c97..094b495 100644 --- a/CircuitAnalysis/PathBias/results.txt +++ b/CircuitAnalysis/PathBias/results.txt @@ -81,87 +81,88 @@ New extrema at [100000, 0.75, 0.8500000000000001, 50]: 85085.0 ===================== False Positives ============================ -Startup false positive counts at [trials, success_rate, min_circs, path_bias_pct]: +Startup false positive counts at [num_circs, success_rate, min_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs min_circs) -New extrema at [100000, 0.8, 20, 70]: 1423 -New extrema at [100000, 0.8, 40, 70]: 301 -New extrema at [100000, 0.8, 60, 70]: 93 -New extrema at [100000, 0.8, 80, 70]: 38 -New extrema at [100000, 0.8, 100, 70]: 18 -New extrema at [100000, 0.8, 120, 70]: 5 -New extrema at [100000, 0.8, 160, 70]: 1 -New extrema at [100000, 0.8, 180, 70]: 0 -[100000, 0.8, 200, 70] - -Startup false positive counts at [trials, success_rate, min_circs, path_bias_pct]: +New extrema at [1000000, 0.8, 25, 70]: 9704 +New extrema at [1000000, 0.8, 50, 70]: 1571 +New extrema at [1000000, 0.8, 75, 70]: 469 +New extrema at [1000000, 0.8, 100, 70]: 143 +New extrema at [1000000, 0.8, 125, 70]: 54 +New extrema at [1000000, 0.8, 150, 70]: 31 +New extrema at [1000000, 0.8, 175, 70]: 6 +New extrema at [1000000, 0.8, 225, 70]: 3 +New extrema at [1000000, 0.8, 250, 70]: 0 +[1000000, 0.8, 250, 70] + +Startup false positive counts at [num_circs, success_rate, min_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs min_circs) -New extrema at [100000, 0.45, 20, 30]: 811 -New extrema at [100000, 0.45, 40, 30]: 123 -New extrema at [100000, 0.45, 60, 30]: 25 -New extrema at [100000, 0.45, 80, 30]: 3 -New extrema at [100000, 0.45, 100, 30]: 0 -[100000, 0.45, 200, 30] - -False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +New extrema at [1000000, 0.45, 25, 30]: 4893 +New extrema at [1000000, 0.45, 50, 30]: 497 +New extrema at [1000000, 0.45, 75, 30]: 96 +New extrema at [1000000, 0.45, 100, 30]: 15 +New extrema at [1000000, 0.45, 125, 30]: 8 +New extrema at [1000000, 0.45, 150, 30]: 2 +New extrema at [1000000, 0.45, 175, 30]: 0 +[1000000, 0.45, 250, 30] + +False positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.7, 100, 70]: 17156 -New extrema at [1000000, 0.7, 150, 70]: 13887 -New extrema at [1000000, 0.7, 200, 70]: 12295 -New extrema at [1000000, 0.7, 250, 70]: 11436 -New extrema at [1000000, 0.7, 300, 70]: 10390 -New extrema at [1000000, 0.7, 350, 70]: 9703 -New extrema at [1000000, 0.7, 400, 70]: 8697 -New extrema at [1000000, 0.7, 500, 70]: 8271 +New extrema at [1000000, 0.7, 100, 70]: 16805 +New extrema at [1000000, 0.7, 150, 70]: 13963 +New extrema at [1000000, 0.7, 200, 70]: 11911 +New extrema at [1000000, 0.7, 250, 70]: 11067 +New extrema at [1000000, 0.7, 300, 70]: 10310 +New extrema at [1000000, 0.7, 350, 70]: 9828 +New extrema at [1000000, 0.7, 400, 70]: 9273 +New extrema at [1000000, 0.7, 450, 70]: 8294 [1000000, 0.7, 500, 70] -False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +False positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.75, 100, 70]: 3679 -New extrema at [1000000, 0.75, 150, 70]: 1826 -New extrema at [1000000, 0.75, 200, 70]: 1134 -New extrema at [1000000, 0.75, 250, 70]: 577 -New extrema at [1000000, 0.75, 300, 70]: 365 -New extrema at [1000000, 0.75, 350, 70]: 228 -New extrema at [1000000, 0.75, 400, 70]: 117 -New extrema at [1000000, 0.75, 450, 70]: 77 +New extrema at [1000000, 0.75, 100, 70]: 3554 +New extrema at [1000000, 0.75, 150, 70]: 1833 +New extrema at [1000000, 0.75, 200, 70]: 1126 +New extrema at [1000000, 0.75, 250, 70]: 544 +New extrema at [1000000, 0.75, 300, 70]: 412 +New extrema at [1000000, 0.75, 350, 70]: 237 +New extrema at [1000000, 0.75, 400, 70]: 132 +New extrema at [1000000, 0.75, 450, 70]: 54 +New extrema at [1000000, 0.75, 500, 70]: 33 [1000000, 0.75, 500, 70] -False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +False positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.8, 100, 70]: 123 -New extrema at [1000000, 0.8, 150, 70]: 29 -New extrema at [1000000, 0.8, 200, 70]: 9 -New extrema at [1000000, 0.8, 250, 70]: 3 -New extrema at [1000000, 0.8, 300, 70]: 0 +New extrema at [1000000, 0.8, 100, 70]: 147 +New extrema at [1000000, 0.8, 150, 70]: 17 +New extrema at [1000000, 0.8, 200, 70]: 2 +New extrema at [1000000, 0.8, 250, 70]: 0 [1000000, 0.8, 500, 70] -False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +False positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.55, 100, 50]: 4739 -New extrema at [1000000, 0.55, 150, 50]: 2754 -New extrema at [1000000, 0.55, 200, 50]: 1618 -New extrema at [1000000, 0.55, 250, 50]: 1123 -New extrema at [1000000, 0.55, 300, 50]: 682 -New extrema at [1000000, 0.55, 350, 50]: 326 -New extrema at [1000000, 0.55, 400, 50]: 322 -New extrema at [1000000, 0.55, 450, 50]: 189 -New extrema at [1000000, 0.55, 500, 50]: 152 +New extrema at [1000000, 0.55, 100, 50]: 4878 +New extrema at [1000000, 0.55, 150, 50]: 2765 +New extrema at [1000000, 0.55, 200, 50]: 1619 +New extrema at [1000000, 0.55, 250, 50]: 1136 +New extrema at [1000000, 0.55, 300, 50]: 681 +New extrema at [1000000, 0.55, 350, 50]: 462 +New extrema at [1000000, 0.55, 400, 50]: 292 +New extrema at [1000000, 0.55, 450, 50]: 232 +New extrema at [1000000, 0.55, 500, 50]: 105 [1000000, 0.55, 500, 50] -False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +False positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.6, 100, 50]: 519 -New extrema at [1000000, 0.6, 150, 50]: 103 -New extrema at [1000000, 0.6, 200, 50]: 17 -New extrema at [1000000, 0.6, 250, 50]: 5 -New extrema at [1000000, 0.6, 300, 50]: 2 -New extrema at [1000000, 0.6, 350, 50]: 1 -New extrema at [1000000, 0.6, 450, 50]: 0 +New extrema at [1000000, 0.6, 100, 50]: 545 +New extrema at [1000000, 0.6, 150, 50]: 108 +New extrema at [1000000, 0.6, 200, 50]: 25 +New extrema at [1000000, 0.6, 250, 50]: 7 +New extrema at [1000000, 0.6, 300, 50]: 0 [1000000, 0.6, 500, 50] -False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +False positive counts at [num_circs, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.45, 100, 30]: 17 +New extrema at [1000000, 0.45, 100, 30]: 16 New extrema at [1000000, 0.45, 150, 30]: 0 [1000000, 0.45, 500, 30] @@ -171,17 +172,17 @@ New extrema at [1000000, 0.45, 150, 30]: 0 DoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]: New extrema at [0.8, 0.05, 30, 200]: 150 New extrema at [0.8, 0.05, 30, 300]: 215 -New extrema at [0.8, 0.05, 30, 400]: 330 -New extrema at [0.8, 0.05, 30, 500]: 368 -New extrema at [0.8, 0.05, 30, 600]: 460 -New extrema at [0.8, 0.05, 30, 700]: 539 -New extrema at [0.8, 0.05, 30, 800]: 593 -New extrema at [0.8, 0.05, 30, 900]: 660 -New extrema at [0.8, 0.05, 30, 1000]: 760 +New extrema at [0.8, 0.05, 30, 400]: 310 +New extrema at [0.8, 0.05, 30, 500]: 380 +New extrema at [0.8, 0.05, 30, 600]: 465 +New extrema at [0.8, 0.05, 30, 700]: 510 +New extrema at [0.8, 0.05, 30, 800]: 658 +New extrema at [0.8, 0.05, 30, 900]: 675 +New extrema at [0.8, 0.05, 30, 1000]: 755 [0.8, 0.05, 30, 1000] DoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]: -New extrema at [0.8, 0.25, 30, 500]: 828 +New extrema at [0.8, 0.25, 30, 500]: 805 New extrema at [0.8, 0.15, 30, 500]: 503 -New extrema at [0.8, 0.04999999999999999, 30, 500]: 355 +New extrema at [0.8, 0.04999999999999999, 30, 500]: 362 [0.8, 0.04999999999999999, 30, 500]

1 0

[torflow/master] Update results file with new output.
by mikeperry＠torproject.org 10 Oct '12

10 Oct '12

commit 79f1671ab244858429484f2d0b3da64baf732e55 Author: Mike Perry <mikeperry-git(a)fscked.org> Date: Wed Oct 10 16:49:28 2012 -0700 Update results file with new output. --- CircuitAnalysis/PathBias/results.txt | 233 +++++++++++++++++++-------------- 1 files changed, 134 insertions(+), 99 deletions(-) diff --git a/CircuitAnalysis/PathBias/results.txt b/CircuitAnalysis/PathBias/results.txt index 4293851..d8e1c97 100644 --- a/CircuitAnalysis/PathBias/results.txt +++ b/CircuitAnalysis/PathBias/results.txt @@ -2,11 +2,11 @@ PassiveEvilGuard compromise rate at [success_rate, adversary_capacity, path_bias_pct]: (As expected, P(CompromisedExit|PassiveEvilGuard) ~= c/n) -New extrema at [10000, 0.75, 0.05, 70]: 0.052610548748 -New extrema at [10000, 0.75, 0.25, 70]: 0.250574712644 -New extrema at [10000, 0.75, 0.45, 70]: 0.455518836748 -New extrema at [10000, 0.75, 0.65, 70]: 0.64692513369 -New extrema at [10000, 0.75, 0.8500000000000001, 70]: 0.851249664069 +New extrema at [10000, 0.75, 0.05, 70]: 0.0489855846236 +New extrema at [10000, 0.75, 0.25, 70]: 0.252824098978 +New extrema at [10000, 0.75, 0.45, 70]: 0.453292291221 +New extrema at [10000, 0.75, 0.65, 70]: 0.651239888609 +New extrema at [10000, 0.75, 0.8500000000000001, 70]: 0.845948471499 [10000, 0.75, 0.8500000000000001, 70] UnrepentantEvilGuard compromise rate at [success_rate, adversary_capacity, path_bias_pct]: @@ -16,28 +16,28 @@ New extrema at [10000, 0.75, 0.05, 70]: 1.0 ProbabalisticEvilGuard compromise rate at [success_rate, adversary_capacity, path_bias_pct]: P(CompromisedExit|ProbabalisticEvilGuard) <= (c/n)*(100/PATH_BIAS_PCT) -New extrema at [10000, 0.75, 0.05, 70]: 0.0615289955391 -New extrema at [10000, 0.75, 0.25, 70]: 0.381941239809 -New extrema at [10000, 0.75, 0.45, 70]: 0.686913732124 -New extrema at [10000, 0.75, 0.65, 70]: 0.993549377976 +New extrema at [10000, 0.75, 0.05, 70]: 0.0641538461538 +New extrema at [10000, 0.75, 0.25, 70]: 0.386709736964 +New extrema at [10000, 0.75, 0.45, 70]: 0.695431472081 +New extrema at [10000, 0.75, 0.65, 70]: 0.994813119756 New extrema at [10000, 0.75, 0.8500000000000001, 70]: 1.0 [10000, 0.75, 0.8500000000000001, 70] OmniscientEvilGuard compromise rate at [success_rate, adversary_capacity, path_bias_pct]: P(CompromisedExit|OmniscientEvilGuard) <= (c/n)*(100/PATH_BIAS_PCT) -New extrema at [10000, 0.75, 0.05, 70]: 0.0680291553523 -New extrema at [10000, 0.75, 0.25, 70]: 0.356724310419 -New extrema at [10000, 0.75, 0.45, 70]: 0.646151649293 -New extrema at [10000, 0.75, 0.65, 70]: 0.917728688757 -New extrema at [10000, 0.75, 0.8500000000000001, 70]: 1.0 +New extrema at [10000, 0.75, 0.05, 70]: 0.0713570713571 +New extrema at [10000, 0.75, 0.25, 70]: 0.368157781907 +New extrema at [10000, 0.75, 0.45, 70]: 0.640582607454 +New extrema at [10000, 0.75, 0.65, 70]: 0.913658881812 +New extrema at [10000, 0.75, 0.8500000000000001, 70]: 0.999881544658 [10000, 0.75, 0.8500000000000001, 70] OmniscientEvilGuard compromise at [success_rate, adversary_capacity, path_bias_pct]: P(CompromisedExit|OmniscientEvilGuard) <= (c/n)*(100/PATH_BIAS_PCT) -New extrema at [10000, 0.75, 0.2, 20]: 0.927127409497 -New extrema at [10000, 0.75, 0.2, 40]: 0.497260956175 -New extrema at [10000, 0.75, 0.2, 60]: 0.328728362184 -New extrema at [10000, 0.75, 0.2, 80]: 0.257664872982 +New extrema at [10000, 0.75, 0.2, 20]: 0.923076923077 +New extrema at [10000, 0.75, 0.2, 40]: 0.514037267081 +New extrema at [10000, 0.75, 0.2, 60]: 0.339663501583 +New extrema at [10000, 0.75, 0.2, 80]: 0.246594175728 [10000, 0.75, 0.2, 80] @@ -46,107 +46,142 @@ New extrema at [10000, 0.75, 0.2, 80]: 0.257664872982 UnrepentantEvilGuard compromised circs at [success_rate, adversary_capacity, path_bias_pct]: circs_per_client ~= success_rate*c/n*MIN_CIRCS for c/n < PATH_BIAS_PCT || c/n < success_rate ~= success_rate*circ_attempts*c/n for c/n > PATH_BIAS_PCT && c/n > success_rate -New extrema at [100000, 0.75, 0.05, 50]: 0.742451509698 -New extrema at [100000, 0.75, 0.25, 50]: 3.73414682937 -New extrema at [100000, 0.75, 0.45, 50]: 6.91443298969 -New extrema at [100000, 0.75, 0.65, 50]: 30.9604339502 -New extrema at [100000, 0.75, 0.8500000000000001, 50]: 63741.0 +New extrema at [100000, 0.75, 0.05, 50]: 0.761847630474 +New extrema at [100000, 0.75, 0.25, 50]: 3.7874 +New extrema at [100000, 0.75, 0.45, 50]: 6.9507552245 +New extrema at [100000, 0.75, 0.65, 50]: 32.9502688172 +New extrema at [100000, 0.75, 0.8500000000000001, 50]: 63872.0 [100000, 0.75, 0.8500000000000001, 50] PassiveEvilGuard compromised circs at [success_rate, adversary_capacity, path_bias_pct]: circs_per_client ~= success_rate * circ_attempts * c/n -New extrema at [100000, 0.75, 0.05, 50]: 3657.0 -New extrema at [100000, 0.75, 0.25, 50]: 18619.0 -New extrema at [100000, 0.75, 0.45, 50]: 33849.0 -New extrema at [100000, 0.75, 0.65, 50]: 49115.0 -New extrema at [100000, 0.75, 0.8500000000000001, 50]: 63625.0 +New extrema at [100000, 0.75, 0.05, 50]: 3814.0 +New extrema at [100000, 0.75, 0.25, 50]: 19004.0 +New extrema at [100000, 0.75, 0.45, 50]: 33870.0 +New extrema at [100000, 0.75, 0.65, 50]: 48850.0 +New extrema at [100000, 0.75, 0.8500000000000001, 50]: 63845.0 [100000, 0.75, 0.8500000000000001, 50] ProbabalisticEvilGuard compromised circs at [success_rate, adversary_capacity, path_bias_pct]: circs_per_client ~= success_rate * circ_attempts * c/n -New extrema at [100000, 0.75, 0.05, 50]: 4036.0 -New extrema at [100000, 0.75, 0.65, 50]: 65053.0 -New extrema at [100000, 0.75, 0.8500000000000001, 50]: 85187.0 +New extrema at [100000, 0.75, 0.05, 50]: 3971.0 +New extrema at [100000, 0.75, 0.65, 50]: 64979.0 +New extrema at [100000, 0.75, 0.8500000000000001, 50]: 84854.0 [100000, 0.75, 0.8500000000000001, 50] OmniscientEvilGuard compromised circs at [success_rate, adversary_capacity, path_bias_pct]: circs_per_client ~= circ_attempts * c/n -New extrema at [100000, 0.75, 0.05, 50]: 4936.0 -New extrema at [100000, 0.75, 0.25, 50]: 25097.0 -New extrema at [100000, 0.75, 0.45, 50]: 44944.0 -New extrema at [100000, 0.75, 0.65, 50]: 65078.0 -New extrema at [100000, 0.75, 0.8500000000000001, 50]: 84984.0 +New extrema at [100000, 0.75, 0.05, 50]: 4917.0 +New extrema at [100000, 0.75, 0.25, 50]: 25141.0 +New extrema at [100000, 0.75, 0.45, 50]: 45100.0 +New extrema at [100000, 0.75, 0.65, 50]: 64926.0 +New extrema at [100000, 0.75, 0.8500000000000001, 50]: 85085.0 [100000, 0.75, 0.8500000000000001, 50] -===================== FALSE POSITIVES ============================ - -Notice false positive rates at [trials, success_rate, min_circs, path_bias_pct]: -(Results are some function of success_rate - path_bias_pct vs min_circs) -New extrema at [100000, 0.65, 50, 70]: 0.0151544227886 -New extrema at [100000, 0.65, 100, 70]: 0.00748851148851 -New extrema at [100000, 0.65, 150, 70]: 0.00394002998501 -New extrema at [100000, 0.65, 250, 70]: 0.00385037406484 -[100000, 0.65, 250, 70] +===================== False Positives ============================ -Notice false positive rates at [trials, success_rate, min_circs, path_bias_pct]: +Startup false positive counts at [trials, success_rate, min_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs min_circs) -New extrema at [100000, 0.7, 50, 70]: 0.0087836081959 -New extrema at [100000, 0.7, 100, 70]: 0.00484715284715 -New extrema at [100000, 0.7, 150, 70]: 0.00275262368816 -New extrema at [100000, 0.7, 200, 70]: 0.00241916167665 -New extrema at [100000, 0.7, 250, 70]: 0.00180548628429 -New extrema at [100000, 0.7, 300, 70]: 0.0 -[100000, 0.7, 500, 70] - -Notice false positives at [trials, success_rate, min_circs, path_bias_pct]: +New extrema at [100000, 0.8, 20, 70]: 1423 +New extrema at [100000, 0.8, 40, 70]: 301 +New extrema at [100000, 0.8, 60, 70]: 93 +New extrema at [100000, 0.8, 80, 70]: 38 +New extrema at [100000, 0.8, 100, 70]: 18 +New extrema at [100000, 0.8, 120, 70]: 5 +New extrema at [100000, 0.8, 160, 70]: 1 +New extrema at [100000, 0.8, 180, 70]: 0 +[100000, 0.8, 200, 70] + +Startup false positive counts at [trials, success_rate, min_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs min_circs) -New extrema at [100000, 0.75, 20, 70]: 0.00484143171366 -New extrema at [100000, 0.75, 40, 70]: 0.00279088364654 -New extrema at [100000, 0.75, 60, 70]: 0.00188602279544 -New extrema at [100000, 0.75, 80, 70]: 0.00137170263789 -New extrema at [100000, 0.75, 100, 70]: 0.000995004995005 -New extrema at [100000, 0.75, 120, 70]: 0.000834532374101 -New extrema at [100000, 0.75, 140, 70]: 0.000581818181818 -New extrema at [100000, 0.75, 160, 70]: 0.000472843450479 -New extrema at [100000, 0.75, 200, 70]: 0.000287425149701 -New extrema at [100000, 0.75, 240, 70]: 0.000143884892086 -New extrema at [100000, 0.75, 260, 70]: 0.0 -[100000, 0.75, 400, 70] - -Reject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]: +New extrema at [100000, 0.45, 20, 30]: 811 +New extrema at [100000, 0.45, 40, 30]: 123 +New extrema at [100000, 0.45, 60, 30]: 25 +New extrema at [100000, 0.45, 80, 30]: 3 +New extrema at [100000, 0.45, 100, 30]: 0 +[100000, 0.45, 200, 30] + +False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.65, 50, 70]: 0.0346242687866 -New extrema at [1000000, 0.65, 100, 70]: 0.0328767123288 -New extrema at [1000000, 0.65, 150, 70]: 0.0322721591761 -New extrema at [1000000, 0.65, 200, 70]: 0.0318086382723 -New extrema at [1000000, 0.65, 250, 70]: 0.0317770557361 -[1000000, 0.65, 250, 70] - -Reject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]: -(Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.7, 50, 70]: 0.019617019149 -New extrema at [1000000, 0.7, 100, 70]: 0.014500549945 -New extrema at [1000000, 0.7, 150, 70]: 0.0125621156826 -New extrema at [1000000, 0.7, 200, 70]: 0.0109148170366 -New extrema at [1000000, 0.7, 250, 70]: 0.00958460384904 -New extrema at [1000000, 0.7, 300, 70]: 0.00869939018295 -New extrema at [1000000, 0.7, 350, 70]: 0.00858299595142 -New extrema at [1000000, 0.7, 400, 70]: 0.00795681727309 -New extrema at [1000000, 0.7, 450, 70]: 0.00731071018042 -New extrema at [1000000, 0.7, 500, 70]: 0.00690254872564 +New extrema at [1000000, 0.7, 100, 70]: 17156 +New extrema at [1000000, 0.7, 150, 70]: 13887 +New extrema at [1000000, 0.7, 200, 70]: 12295 +New extrema at [1000000, 0.7, 250, 70]: 11436 +New extrema at [1000000, 0.7, 300, 70]: 10390 +New extrema at [1000000, 0.7, 350, 70]: 9703 +New extrema at [1000000, 0.7, 400, 70]: 8697 +New extrema at [1000000, 0.7, 500, 70]: 8271 [1000000, 0.7, 500, 70] -Reject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]: +False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: (Results are some function of success_rate - path_bias_pct vs scale_circs) -New extrema at [1000000, 0.75, 50, 70]: 0.00594470276486 -New extrema at [1000000, 0.75, 100, 70]: 0.00212178782122 -New extrema at [1000000, 0.75, 150, 70]: 0.000978853172024 -New extrema at [1000000, 0.75, 200, 70]: 0.000467906418716 -New extrema at [1000000, 0.75, 250, 70]: 0.000266933266683 -New extrema at [1000000, 0.75, 300, 70]: 0.000119964010797 -New extrema at [1000000, 0.75, 350, 70]: 7.99720097966e-05 -New extrema at [1000000, 0.75, 400, 70]: 3.59856057577e-05 -New extrema at [1000000, 0.75, 450, 70]: 1.99910040482e-05 -New extrema at [1000000, 0.75, 500, 70]: 2.99850074963e-06 +New extrema at [1000000, 0.75, 100, 70]: 3679 +New extrema at [1000000, 0.75, 150, 70]: 1826 +New extrema at [1000000, 0.75, 200, 70]: 1134 +New extrema at [1000000, 0.75, 250, 70]: 577 +New extrema at [1000000, 0.75, 300, 70]: 365 +New extrema at [1000000, 0.75, 350, 70]: 228 +New extrema at [1000000, 0.75, 400, 70]: 117 +New extrema at [1000000, 0.75, 450, 70]: 77 [1000000, 0.75, 500, 70] + +False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +(Results are some function of success_rate - path_bias_pct vs scale_circs) +New extrema at [1000000, 0.8, 100, 70]: 123 +New extrema at [1000000, 0.8, 150, 70]: 29 +New extrema at [1000000, 0.8, 200, 70]: 9 +New extrema at [1000000, 0.8, 250, 70]: 3 +New extrema at [1000000, 0.8, 300, 70]: 0 +[1000000, 0.8, 500, 70] + +False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +(Results are some function of success_rate - path_bias_pct vs scale_circs) +New extrema at [1000000, 0.55, 100, 50]: 4739 +New extrema at [1000000, 0.55, 150, 50]: 2754 +New extrema at [1000000, 0.55, 200, 50]: 1618 +New extrema at [1000000, 0.55, 250, 50]: 1123 +New extrema at [1000000, 0.55, 300, 50]: 682 +New extrema at [1000000, 0.55, 350, 50]: 326 +New extrema at [1000000, 0.55, 400, 50]: 322 +New extrema at [1000000, 0.55, 450, 50]: 189 +New extrema at [1000000, 0.55, 500, 50]: 152 +[1000000, 0.55, 500, 50] + +False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +(Results are some function of success_rate - path_bias_pct vs scale_circs) +New extrema at [1000000, 0.6, 100, 50]: 519 +New extrema at [1000000, 0.6, 150, 50]: 103 +New extrema at [1000000, 0.6, 200, 50]: 17 +New extrema at [1000000, 0.6, 250, 50]: 5 +New extrema at [1000000, 0.6, 300, 50]: 2 +New extrema at [1000000, 0.6, 350, 50]: 1 +New extrema at [1000000, 0.6, 450, 50]: 0 +[1000000, 0.6, 500, 50] + +False positive counts at [trials, success_rate, scale_circs, path_bias_pct]: +(Results are some function of success_rate - path_bias_pct vs scale_circs) +New extrema at [1000000, 0.45, 100, 30]: 17 +New extrema at [1000000, 0.45, 150, 30]: 0 +[1000000, 0.45, 500, 30] + + +===================== DoS Attack Duration ======================== + +DoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]: +New extrema at [0.8, 0.05, 30, 200]: 150 +New extrema at [0.8, 0.05, 30, 300]: 215 +New extrema at [0.8, 0.05, 30, 400]: 330 +New extrema at [0.8, 0.05, 30, 500]: 368 +New extrema at [0.8, 0.05, 30, 600]: 460 +New extrema at [0.8, 0.05, 30, 700]: 539 +New extrema at [0.8, 0.05, 30, 800]: 593 +New extrema at [0.8, 0.05, 30, 900]: 660 +New extrema at [0.8, 0.05, 30, 1000]: 760 +[0.8, 0.05, 30, 1000] + +DoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]: +New extrema at [0.8, 0.25, 30, 500]: 828 +New extrema at [0.8, 0.15, 30, 500]: 503 +New extrema at [0.8, 0.04999999999999999, 30, 500]: 355 +[0.8, 0.04999999999999999, 30, 500]

1 0

[torflow/master] Add a Guard DoS simulation.
by mikeperry＠torproject.org 10 Oct '12

10 Oct '12

commit 77ee2aa8d0bff2a85268a233b18448bb2b0c9317 Author: Mike Perry <mikeperry-git(a)fscked.org> Date: Wed Oct 10 16:22:11 2012 -0700 Add a Guard DoS simulation. --- CircuitAnalysis/PathBias/path_bias.py | 39 ++++++++++++++++++++++++++++++++- 1 files changed, 38 insertions(+), 1 deletions(-) diff --git a/CircuitAnalysis/PathBias/path_bias.py b/CircuitAnalysis/PathBias/path_bias.py index 2487bc9..54e8581 100755 --- a/CircuitAnalysis/PathBias/path_bias.py +++ b/CircuitAnalysis/PathBias/path_bias.py @@ -341,6 +341,24 @@ def generic_rate_test(g, trials, success_rate, adversary_capacity, path_bias_pct return rate_fcn(g) +def dos_attack_test(success_rate, dos_success_rate, path_bias_pct, scale_thresh): + global PATH_BIAS_PCT + global PATH_BIAS_SCALE_THRESHOLD + PATH_BIAS_PCT = path_bias_pct + PATH_BIAS_SCALE_THRESHOLD = scale_thresh + + g = Guard(success_rate) + + simulate_circs_until(g, PATH_BIAS_SCALE_THRESHOLD, lambda g: False) + g.rejected_count = 0 + + g.succeed_rate = dos_success_rate + + simulate_circs_until(g, 10000, lambda g: g.rejected_count > 0) + + return g.first_hops_total - PATH_BIAS_SCALE_THRESHOLD + + ################ Multi-Dementianal Analysis ##################### # If brute force doesn't work, you're not using enough @@ -497,7 +515,7 @@ def main(): if True: - print "\n\n===================== FALSE POSITIVES ============================" + print "\n\n===================== False Positives ============================" print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" @@ -564,6 +582,25 @@ def main(): [(1000000,1000000), (0.45, 0.45), (100,500), (30, 30)], [0, -0.1, 50, 5]) + if True: + print "\n\n===================== DoS Attack Duration ========================" + print "\nDoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]:" + print brute_force(lambda x,y: x>y, + dos_attack_test, + #dos_attack_test(g, trials, success_rate, dos_success_rate, path_bias_pct): + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(0.80, 0.80), (0.05,0.05), (30, 30), (200, 1000)], + [-0.1, -0.1, 5, 100]) + + print "\nDoS attack durations (in circs) at [success_rate, dos_success_rate, path_bias_pct, scale_thresh]:" + print brute_force(lambda x,y: x<y, + dos_attack_test, + #dos_attack_test(g, trials, success_rate, dos_success_rate, path_bias_pct): + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(0.80, 0.80), (0.25,0.05), (30, 30), (500, 500)], + [-0.1, -0.1, 5, 100]) + + if __name__ == "__main__": main() #sys.argv)

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[torflow/master] Represent false positives as raw counts.
by mikeperry＠torproject.org 10 Oct '12

10 Oct '12

commit 71fb889aaf099fa99c53f3dcb87dff09aec77814 Author: Mike Perry <mikeperry-git(a)fscked.org> Date: Wed Oct 10 14:55:11 2012 -0700 Represent false positives as raw counts. Also, clarify the startup false positive test. --- CircuitAnalysis/PathBias/path_bias.py | 67 ++++++++++++++++++++------------- 1 files changed, 41 insertions(+), 26 deletions(-) diff --git a/CircuitAnalysis/PathBias/path_bias.py b/CircuitAnalysis/PathBias/path_bias.py index 621e2c1..2487bc9 100755 --- a/CircuitAnalysis/PathBias/path_bias.py +++ b/CircuitAnalysis/PathBias/path_bias.py @@ -15,7 +15,7 @@ PATH_BIAS_MIN_CIRCS = 20 # XXX: An int divisor was wrong here. Fix that in Tor. We might # even want a weighted moving average, but that will be trickier # to analyze. -PATH_BIAS_SCALE_FACTOR = 95 +PATH_BIAS_SCALE_FACTOR = 50 PATH_BIAS_SCALE_THRESHOLD = 250 # XXX: We should only emit warnings if we are above the scaling threshhold.. @@ -33,7 +33,7 @@ def reset_globals(): PATH_BIAS_PCT = 70 PATH_BIAS_MIN_CIRCS = 20 - PATH_BIAS_SCALE_FACTOR = 95 + PATH_BIAS_SCALE_FACTOR = 50 PATH_BIAS_SCALE_THRESHOLD = 250 PATH_BIAS_WARN_CIRCS = PATH_BIAS_SCALE_THRESHOLD*(PATH_BIAS_SCALE_FACTOR/100.0) @@ -294,7 +294,7 @@ def simulate_circs_until(g, circ_count, say_when): # success_rate # PATH_BIAS_MIN_CIRCS = 20 # PATH_BIAS_PCT = 70 -def notice_false_positive_test(trials, success_rate, min_circs, path_bias_pct): +def startup_false_positive_test(trials, success_rate, min_circs, path_bias_pct): # FIXME: Look it's just easier this way, ok? Get off my back already global PATH_BIAS_MIN_CIRCS global PATH_BIAS_PCT @@ -309,7 +309,7 @@ def notice_false_positive_test(trials, success_rate, min_circs, path_bias_pct): #print g._get_rate() - return g.reject_rate() + return g.rejected_count def reject_false_positive_test(trials, success_rate, scale_circs, path_bias_pct): # FIXME: Look it's just easier this way, ok? Get off my back already @@ -327,9 +327,7 @@ def reject_false_positive_test(trials, success_rate, scale_circs, path_bias_pct) simulate_circs_until(g, trials, lambda g: False) - #print g._get_rate() - - return g.reject_rate() + return g.rejected_count def generic_rate_test(g, trials, success_rate, adversary_capacity, path_bias_pct, rate_fcn): # FIXME: Look it's just easier this way, ok? Get off my back already @@ -501,52 +499,69 @@ def main(): if True: print "\n\n===================== FALSE POSITIVES ============================" - print "\nNotice false positive rates at [trials, success_rate, min_circs, path_bias_pct]:" + print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" print brute_force(lambda x,y: x<y, - notice_false_positive_test, + startup_false_positive_test, #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.65, 0.65), (50,250), (70, 70)], - [0, -0.1, 50, 5]) + [(100000,100000), (0.80, 0.80), (20,200), (70, 70)], + [0, -0.1, 20, 5]) - print "\nNotice false positive rates at [trials, success_rate, min_circs, path_bias_pct]:" + print "\nStartup false positive counts at [trials, success_rate, min_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs min_circs)" print brute_force(lambda x,y: x<y, - notice_false_positive_test, + startup_false_positive_test, #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.70, 0.70), (50,500), (70, 70)], + [(100000,100000), (0.45, 0.45), (20,200), (30, 30)], + [0, -0.1, 20, 5]) + + + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" + print brute_force(lambda x,y: x<y, + reject_false_positive_test, + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(1000000,1000000), (0.70, 0.70), (100,500), (70, 70)], [0, -0.1, 50, 5]) - print "\nNotice false positives at [trials, success_rate, min_circs, path_bias_pct]:" - print "(Results are some function of success_rate - path_bias_pct vs min_circs)" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, - notice_false_positive_test, - #false_positive_test(trials, success_rate, min_circs, path_bias_pct): - [(100000,100000), (0.75, 0.75), (20,400), (70, 70)], - [0, -0.1, 20, 5]) + reject_false_positive_test, + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(1000000,1000000), (0.75, 0.75), (100,500), (70, 70)], + [0, -0.1, 50, 5]) + + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" + print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" + print brute_force(lambda x,y: x<y, + reject_false_positive_test, + #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): + [(1000000,1000000), (0.80, 0.80), (100,500), (70, 70)], + [0, -0.1, 50, 5]) - print "\nReject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): - [(1000000,1000000), (0.65, 0.65), (50,250), (70, 70)], + [(1000000,1000000), (0.55, 0.55), (100,500), (50, 50)], [0, -0.1, 50, 5]) - print "\nReject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): - [(1000000,1000000), (0.70, 0.70), (50,500), (70, 70)], + [(1000000,1000000), (0.60, 0.60), (100,500), (50, 50)], [0, -0.1, 50, 5]) - print "\nReject false positive rates at [trials, success_rate, scale_circs, path_bias_pct]:" + print "\nFalse positive counts at [trials, success_rate, scale_circs, path_bias_pct]:" print "(Results are some function of success_rate - path_bias_pct vs scale_circs)" print brute_force(lambda x,y: x<y, reject_false_positive_test, #false_positive_test(trials, success_rate, scale_circs, path_bias_pct): - [(1000000,1000000), (0.75, 0.75), (50,500), (70, 70)], + [(1000000,1000000), (0.45, 0.45), (100,500), (30, 30)], [0, -0.1, 50, 5])

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r25844: {website} Update torbutton to 1.4.6.3. --This line, and tsose below, w (in website/trunk: include torbutton)
by Mike Perry 10 Oct '12

10 Oct '12

Author: mikeperry Date: 2012-10-10 18:08:25 +0000 (Wed, 10 Oct 2012) New Revision: 25844 Modified: website/trunk/include/versions.wmi website/trunk/torbutton/update.rdf Log: Update torbutton to 1.4.6.3. --This line, and tsose below, will be ignored-- cks ssh vescum "/home/mirroradm/bin/trigger-mirrors" M include/versions.wmi M torbutton/update.rdf Modified: website/trunk/include/versions.wmi =================================================================== --- website/trunk/include/versions.wmi 2012-10-09 22:44:12 UTC (rev 25843) +++ website/trunk/include/versions.wmi 2012-10-10 18:08:25 UTC (rev 25844) @@ -109,8 +109,8 @@ <define-tag file-source-alpha whitespace=delete>tor-<version-alpha>.tar.gz</define-tag> <define-tag package-source-alpha whitespace=delete>../dist/<file-source-alpha></define-tag> -<define-tag version-torbutton whitespace=delete>1.4.6.2 (12 Sep 2012)</define-tag> -<define-tag version-hash-torbutton whitespace=delete>sha1:1182fa2dcb095ad8054a218c41bc0a849b5b30ed</define-tag> +<define-tag version-torbutton whitespace=delete>1.4.6.3 (10 Oct 2012)</define-tag> +<define-tag version-hash-torbutton whitespace=delete>sha1:968455a5b702b3cabb6c16499e531a5a8f4c5f21</define-tag> <define-tag version-torbutton-alpha whitespace=delete>None</define-tag> <define-tag version-hash-torbutton-alpha whitespace=delete>sha1:c7323cd408ebee28ee23b6a91fe056d59de668f5</define-tag> Modified: website/trunk/torbutton/update.rdf =================================================================== (Binary files differ)

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