commit ee9b5da66a11d5289749f7a18703e381702d116d Author: Karsten Loesing karsten.loesing@gmx.net Date: Mon Sep 16 18:06:28 2013 +0200

Add George Danezis' censorship detector.

This is a slightly tweaked version of metrics-tasks.git/task-2718/. --- detector/.gitignore | 2 + detector/country_info.py | 251 ++++++++++++++++++++++++++ detector/detector.py | 437 ++++++++++++++++++++++++++++++++++++++++++++++ detector/detector.sh | 5 + 4 files changed, 695 insertions(+)

diff --git a/detector/.gitignore b/detector/.gitignore new file mode 100644 index 0000000..29a7166 --- /dev/null +++ b/detector/.gitignore @@ -0,0 +1,2 @@ +*.csv + diff --git a/detector/country_info.py b/detector/country_info.py new file mode 100644 index 0000000..9dbdeb5 --- /dev/null +++ b/detector/country_info.py @@ -0,0 +1,251 @@ +# -*- coding: utf-8 -*- + +countries = { + "ad" : "Andorra", + "ae" : "the United Arab Emirates", + "af" : "Afghanistan", + "ag" : "Antigua and Barbuda", + "ai" : "Anguilla", + "al" : "Albania", + "am" : "Armenia", + "an" : "the Netherlands Antilles", + "ao" : "Angola", + "aq" : "Antarctica", + "ar" : "Argentina", + "as" : "American Samoa", + "at" : "Austria", + "au" : "Australia", + "aw" : "Aruba", + "ax" : "the Aland Islands", + "az" : "Azerbaijan", + "ba" : "Bosnia and Herzegovina", + "bb" : "Barbados", + "bd" : "Bangladesh", + "be" : "Belgium", + "bf" : "Burkina Faso", + "bg" : "Bulgaria", + "bh" : "Bahrain", + "bi" : "Burundi", + "bj" : "Benin", + "bl" : "Saint Bartelemey", + "bm" : "Bermuda", + "bn" : "Brunei", + "bo" : "Bolivia", + "br" : "Brazil", + "bs" : "the Bahamas", + "bt" : "Bhutan", + "bv" : "the Bouvet Island", + "bw" : "Botswana", + "by" : "Belarus", + "bz" : "Belize", + "ca" : "Canada", + "cc" : "the Cocos (Keeling) Islands", + "cd" : "the Democratic Republic of the Congo", + "cf" : "Central African Republic", + "cg" : "Congo", + "ch" : "Switzerland", + "ci" : u"Côte d'Ivoire", + "ck" : "the Cook Islands", + "cl" : "Chile", + "cm" : "Cameroon", + "cn" : "China", + "co" : "Colombia", + "cr" : "Costa Rica", + "cu" : "Cuba", + "cv" : "Cape Verde", + "cx" : "the Christmas Island", + "cy" : "Cyprus", + "cz" : "the Czech Republic", + "de" : "Germany", + "dj" : "Djibouti", + "dk" : "Denmark", + "dm" : "Dominica", + "do" : "the Dominican Republic", + "dz" : "Algeria", + "ec" : "Ecuador", + "ee" : "Estonia", + "eg" : "Egypt", + "eh" : "the Western Sahara", + "er" : "Eritrea", + "es" : "Spain", + "et" : "Ethiopia", + "fi" : "Finland", + "fj" : "Fiji", + "fk" : "the Falkland Islands (Malvinas)", + "fm" : "the Federated States of Micronesia", + "fo" : "the Faroe Islands", + "fr" : "France", + "fx" : "Metropolitan France", + "ga" : "Gabon", + "gb" : "the United Kingdom", + "gd" : "Grenada", + "ge" : "Georgia", + "gf" : "French Guiana", + "gg" : "Guernsey", + "gh" : "Ghana", + "gi" : "Gibraltar", + "gl" : "Greenland", + "gm" : "Gambia", + "gn" : "Guinea", + "gp" : "Guadeloupe", + "gq" : "Equatorial Guinea", + "gr" : "Greece", + "gs" : "South Georgia and the South Sandwich Islands", + "gt" : "Guatemala", + "gu" : "Guam", + "gw" : "Guinea-Bissau", + "gy" : "Guyana", + "hk" : "Hong Kong", + "hm" : "Heard Island and McDonald Islands", + "hn" : "Honduras", + "hr" : "Croatia", + "ht" : "Haiti", + "hu" : "Hungary", + "id" : "Indonesia", + "ie" : "Ireland", + "il" : "Israel", + "im" : "the Isle of Man", + "in" : "India", + "io" : "the British Indian Ocean Territory", + "iq" : "Iraq", + "ir" : "Iran", + "is" : "Iceland", + "it" : "Italy", + "je" : "Jersey", + "jm" : "Jamaica", + "jo" : "Jordan", + "jp" : "Japan", + "ke" : "Kenya", + "kg" : "Kyrgyzstan", + "kh" : "Cambodia", + "ki" : "Kiribati", + "km" : "Comoros", + "kn" : "Saint Kitts and Nevis", + "kp" : "North Korea", + "kr" : "the Republic of Korea", + "kw" : "Kuwait", + "ky" : "the Cayman Islands", + "kz" : "Kazakhstan", + "la" : "Laos", + "lb" : "Lebanon", + "lc" : "Saint Lucia", + "li" : "Liechtenstein", + "lk" : "Sri Lanka", + "lr" : "Liberia", + "ls" : "Lesotho", + "lt" : "Lithuania", + "lu" : "Luxembourg", + "lv" : "Latvia", + "ly" : "Libya", + "ma" : "Morocco", + "mc" : "Monaco", + "md" : "the Republic of Moldova", + "me" : "Montenegro", + "mf" : "Saint Martin", + "mg" : "Madagascar", + "mh" : "the Marshall Islands", + "mk" : "Macedonia", + "ml" : "Mali", + "mm" : "Burma", + "mn" : "Mongolia", + "mo" : "Macau", + "mp" : "the Northern Mariana Islands", + "mq" : "Martinique", + "mr" : "Mauritania", + "ms" : "Montserrat", + "mt" : "Malta", + "mu" : "Mauritius", + "mv" : "the Maldives", + "mw" : "Malawi", + "mx" : "Mexico", + "my" : "Malaysia", + "mz" : "Mozambique", + "na" : "Namibia", + "nc" : "New Caledonia", + "ne" : "Niger", + "nf" : "Norfolk Island", + "ng" : "Nigeria", + "ni" : "Nicaragua", + "nl" : "the Netherlands", + "no" : "Norway", + "np" : "Nepal", + "nr" : "Nauru", + "nu" : "Niue", + "nz" : "New Zealand", + "om" : "Oman", + "pa" : "Panama", + "pe" : "Peru", + "pf" : "French Polynesia", + "pg" : "Papua New Guinea", + "ph" : "the Philippines", + "pk" : "Pakistan", + "pl" : "Poland", + "pm" : "Saint Pierre and Miquelon", + "pn" : "the Pitcairn Islands", + "pr" : "Puerto Rico", + "ps" : "the Palestinian Territory", + "pt" : "Portugal", + "pw" : "Palau", + "py" : "Paraguay", + "qa" : "Qatar", + "re" : "Reunion", + "ro" : "Romania", + "rs" : "Serbia", + "ru" : "Russia", + "rw" : "Rwanda", + "sa" : "Saudi Arabia", + "sb" : "the Solomon Islands", + "sc" : "the Seychelles", + "sd" : "Sudan", + "se" : "Sweden", + "sg" : "Singapore", + "sh" : "Saint Helena", + "si" : "Slovenia", + "sj" : "Svalbard and Jan Mayen", + "sk" : "Slovakia", + "sl" : "Sierra Leone", + "sm" : "San Marino", + "sn" : "Senegal", + "so" : "Somalia", + "sr" : "Suriname", + "st" : u"São Tomé and Príncipe", + "sv" : "El Salvador", + "sy" : "the Syrian Arab Republic", + "sz" : "Swaziland", + "tc" : "Turks and Caicos Islands", + "td" : "Chad", + "tf" : "the French Southern Territories", + "tg" : "Togo", + "th" : "Thailand", + "tj" : "Tajikistan", + "tk" : "Tokelau", + "tl" : "East Timor", + "tm" : "Turkmenistan", + "tn" : "Tunisia", + "to" : "Tonga", + "tr" : "Turkey", + "tt" : "Trinidad and Tobago", + "tv" : "Tuvalu", + "tw" : "Taiwan", + "tz" : "the United Republic of Tanzania", + "ua" : "Ukraine", + "ug" : "Uganda", + "um" : "the United States Minor Outlying Islands", + "us" : "the United States", + "uy" : "Uruguay", + "uz" : "Uzbekistan", + "va" : "Vatican City", + "vc" : "Saint Vincent and the Grenadines", + "ve" : "Venezuela", + "vg" : "the British Virgin Islands", + "vi" : "the United States Virgin Islands", + "vn" : "Vietnam", + "vu" : "Vanuatu", + "wf" : "Wallis and Futuna", + "ws" : "Samoa", + "ye" : "Yemen", + "yt" : "Mayotte", + "za" : "South Africa", + "zm" : "Zambia", + "zw" : "Zimbabwe" + } diff --git a/detector/detector.py b/detector/detector.py new file mode 100644 index 0000000..7f924db --- /dev/null +++ b/detector/detector.py @@ -0,0 +1,437 @@ +## Copyright (c) 2011 George Danezis gdane@microsoft.com +## +## All rights reserved. +## +## Redistribution and use in source and binary forms, with or without +## modification, are permitted (subject to the limitations in the +## disclaimer below) provided that the following conditions are met: +## +## * Redistributions of source code must retain the above copyright +## notice, this list of conditions and the following disclaimer. +## +## * Redistributions in binary form must reproduce the above copyright +## notice, this list of conditions and the following disclaimer in the +## documentation and/or other materials provided with the +## distribution. +## +## * Neither the name of <Owner Organization> nor the names of its +## contributors may be used to endorse or promote products derived +## from this software without specific prior written permission. +## +## NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE +## GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT +## HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED +## WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +## MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +## DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +## LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +## CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +## SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR +## BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +## WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +## OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN +## IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +## +## (Clear BSD license: http://labs.metacarta.com/license-explanation.html#license) + +## This script reads a .csv file of the number of Tor users and finds +## anomalies that might be indicative of censorship. + +# Dep: matplotlib +from pylab import * +import matplotlib + +# Dep: numpy +import numpy + +# Dep: scipy +import scipy.stats +from scipy.stats.distributions import norm +from scipy.stats.distributions import poisson + +# Std lib +from datetime import date +from datetime import timedelta +import os.path + +# Country code -> Country names +import country_info + +# write utf8 to file +import codecs + +days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] + +def get_country_name_from_cc(country_code): + if (country_code.lower() in country_info.countries): + return country_info.countries[country_code.lower()] + return country_code # if we didn't find the cc in our map + +""" +Represents a .csv file containing information on the number of +connecting Tor users per country. + +'store': Dictionary with (<country code>, <counter>) as key, and the number of users as value. + <country code> can also be "date"... +'all_dates': List of the data intervals (with default timedelta: 1 day). +'country_codes': List of all relevant country codes. +'MAX_INDEX': Length of store, number of country codes etc. +'date_min': The oldest date found in the .csv. +'date_min': The latest date found in the .csv. +""" +class torstatstore: + def __init__(self, file_name): + f = file(file_name) + country_codes = f.readline() + country_codes = country_codes.strip().split(",") + + store = {} + MAX_INDEX = 0 + for i, line in enumerate(f): + MAX_INDEX += 1 + line_parsed = line.strip().split(",") + for j, (ccode, val) in enumerate(zip(country_codes,line_parsed)): + processed_val = None + if ccode == "date": + try: + year, month, day = int(val[:4]), int(val[5:7]), int(val[8:10]) + processed_val = date(year, month, day) + except Exception, e: + print "Parsing error (ignoring line %s):" % j + print "%s" % val,e + break + + elif val != "NA": + processed_val = int(val) + store[(ccode, i)] = processed_val + + # min and max + date_min = store[("date", 0)] + date_max = store[("date", i)] + + all_dates = [] + d = date_min + dt = timedelta(days=1) + while d <= date_max: + all_dates += [d] + d = d + dt + + # Save for later + self.store = store + self.all_dates = all_dates + self.country_codes = country_codes + self.MAX_INDEX = MAX_INDEX + self.date_min = date_min + self.date_max = date_max + + """Return a list representing a time series of 'ccode' with respect + to the number of connected users. + """ + def get_country_series(self, ccode): + assert ccode in self.country_codes + series = {} + for d in self.all_dates: + series[d] = None + for i in range(self.MAX_INDEX): + series[self.store[("date", i)]] = self.store[(ccode, i)] + sx = [] + for d in self.all_dates: + sx += [series[d]] + return sx + + """Return an ordered list containing tuples of the form (<number of + users>, <country code>). The list is ordered with respect to the + number of users for each country. + """ + def get_largest(self, number): + exclude = set(["all", "??", "date"]) + l = [(self.store[(c, self.MAX_INDEX-1)], c) for c in self.country_codes if c not in exclude] + l.sort() + l.reverse() + return l[:number] + + """Return a dictionary, with <country code> as key, and the time + series of the country code as the value. + """ + def get_largest_locations(self, number): + l = self.get_largest(number) + res = {} + for _, ccode in l[:number]: + res[ccode] = self.get_country_series(ccode) + return res + +"""Return a list containing lists (?) where each such list contains +the difference in users for a time delta of 'days' +""" +def n_day_rel(series, days): + rel = [] + for i, v in enumerate(series): + if series[i] is None: + rel += [None] + continue + + if i - days < 0 or series[i-days] is None or series[i-days] == 0: + rel += [None] + else: + rel += [ float(series[i]) / series[i-days]] + return rel + +# Main model: computes the expected min / max range of number of users +def make_tendencies_minmax(l, INTERVAL = 1): + lminus1 = dict([(ccode, n_day_rel(l[ccode], INTERVAL)) for ccode in l]) + c = lminus1[lminus1.keys()[0]] + dists = [] + minx = [] + maxx = [] + for i in range(len(c)): + vals = [lminus1[ccode][i] for ccode in lminus1.keys() if lminus1[ccode][i] != None] + if len(vals) < 8: + dists += [None] + minx += [None] + maxx += [None] + else: + vals.sort() + median = vals[len(vals)/2] + q1 = vals[len(vals)/4] + q2 = vals[(3*len(vals))/4] + qd = q2 - q1 + vals = [v for v in vals if median - qd*4 < v and v < median + qd*4] + if len(vals) < 8: + dists += [None] + minx += [None] + maxx += [None] + continue + mu, signma = norm.fit(vals) + dists += [(mu, signma)] + maxx += [norm.ppf(0.9999, mu, signma)] + minx += [norm.ppf(1 - 0.9999, mu, signma)] + ## print minx[-1], maxx[-1] + return minx, maxx + +# Makes pretty plots +def raw_plot(series, minc, maxc, labels, xtitle): + assert len(xtitle) == 3 + fname, stitle, slegend = xtitle + + font = {'family' : 'Bitstream Vera Sans', + 'weight' : 'normal', + 'size' : 8} + matplotlib.rc('font', **font) + + ylim( (-max(series)*0.1, max(series)*1.1) ) + plot(labels, series, linewidth=1.0, label="Users") + + wherefill = [] + for mm,mx in zip(minc, maxc): + wherefill += [not (mm == None and mx == None)] + assert mm < mx or (mm == None and mx == None) + + fill_between(labels, minc, maxc, where=wherefill, color="gray", label="Prediction") + + vdown = [] + vup = [] + for i,v in enumerate(series): + if minc[i] != None and v < minc[i]: + vdown += [v] + vup += [None] + elif maxc[i] != None and v > maxc[i]: + vdown += [None] + vup += [v] + else: + vup += [None] + vdown += [None] + + plot(labels, vdown, 'o', ms=10, lw=2, alpha=0.5, mfc='orange', label="Downturns") + plot(labels, vup, 'o', ms=10, lw=2, alpha=0.5, mfc='green', label="Upturns") + + legend(loc=2) + + xlabel('Time (days)') + ylabel('Users') + title(stitle) + grid(True) + F = gcf() + + F.set_size_inches(10,5) + F.savefig(fname, format="png", dpi = (150)) + close() + +def absolute_plot(series, minc, maxc, labels,INTERVAL, xtitle): + in_minc = [] + in_maxc = [] + for i, v in enumerate(series): + if i > 0 and i - INTERVAL >= 0 and series[i] != None and series[i-INTERVAL] != None and series[i-INTERVAL] != 0 and minc[i]!= None and maxc[i]!= None: + in_minc += [minc[i] * poisson.ppf(1-0.9999, series[i-INTERVAL])] + in_maxc += [maxc[i] * poisson.ppf(0.9999, series[i-INTERVAL])] + if not in_minc[-1] < in_maxc[-1]: + print in_minc[-1], in_maxc[-1], series[i-INTERVAL], minc[i], maxc[i] + assert in_minc[-1] < in_maxc[-1] + else: + in_minc += [None] + in_maxc += [None] + raw_plot(series, in_minc, in_maxc, labels, xtitle) + +"""Return the number of downscores and upscores of a time series +'series', given tendencies 'minc' and 'maxc' for the time interval +'INTERVAL'. + +If 'scoring_interval' is specifed we only consider upscore/downscore +that happened in the latest 'scoring_interval' days. +""" +def censor_score(series, minc, maxc, INTERVAL, scoring_interval=None): + upscore = 0 + downscore = 0 + + if scoring_interval is None: + scoring_interval = len(series) + assert(len(series) >= scoring_interval) + + for i, v in enumerate(series): + if i > 0 and i - INTERVAL >= 0 and series[i] != None and series[i-INTERVAL] != None and series[i-INTERVAL] != 0 and minc[i]!= None and maxc[i]!= None: + in_minc = minc[i] * poisson.ppf(1-0.9999, series[i-INTERVAL]) + in_maxc = maxc[i] * poisson.ppf(0.9999, series[i-INTERVAL]) + if (i >= (len(series) - scoring_interval)): + downscore += 1 if minc[i] != None and v < in_minc else 0 + upscore += 1 if maxc[i] != None and v > in_maxc else 0 + + return downscore, upscore + +def plot_target(tss, TARGET, xtitle, minx, maxx, DAYS=365, INTERV = 7): + ctarget = tss.get_country_series(TARGET) + c = n_day_rel(ctarget, INTERV) + absolute_plot(ctarget[-DAYS:], minx[-DAYS:], maxx[-DAYS:], tss.all_dates[-DAYS:],INTERV, xtitle = xtitle) + +def write_censorship_report_prologue(report_file, dates, notification_period): + if (notification_period == 1): + date_str = "%s" % (dates[-1]) # no need for date range if it's just one day + else: + date_str = "%s to %s" % (dates[-notification_period], dates[-1]) + + prologue = "=======================\n" + prologue += "Automatic Censorship Report for %s\n" % (date_str) + prologue += "=======================\n\n" + report_file.write(prologue) + +## Make a league table of censorship + nice graphs +def plot_all(tss, minx, maxx, INTERV, DAYS=None, rdir="img"): + rdir = os.path.realpath(rdir) + if not os.path.exists(rdir) or not os.path.isdir(rdir): + print "ERROR: %s does not exist or is not a directory." % rdir + return + + summary_file = file(os.path.join(rdir, "summary.txt"), "w") + + if DAYS == None: + DAYS = 6*31 + + s = tss.get_largest(200) + scores = [] + for num, li in s: + print ".", + ds,us = censor_score(tss.get_country_series(li)[-DAYS:], minx[-DAYS:], maxx[-DAYS:], INTERV) + # print ds, us + scores += [(ds,num, us, li)] + scores.sort() + scores.reverse() + s = "\n=======================\n" + s+= "Report for %s to %s\n" % (tss.all_dates[-DAYS], tss.all_dates[-1]) + s+= "=======================\n" + print s + summary_file.write(s) + for a,nx, b,c in scores: + if a > 0: + s = "%s -- down: %2d (up: %2d affected: %s)" % (c, a, b, nx) + print s + summary_file.write(s + "\n") + xtitle = (os.path.join(rdir, "%03d-%s-censor.png" % (a,c)), "Tor report for %s -- down: %2d (up: %2d affected: %s)" % (c, a, b, nx),"") + plot_target(tss, c,xtitle, minx, maxx, DAYS, INTERV) + summary_file.close() + +"""Write a CSV report on the minimum/maximum users of each country per date.""" +def write_all(tss, minc, maxc, INTERVAL=7): + ranges_file = file("direct-users-ranges.csv", "w") + ranges_file.write("date,country,minusers,maxusers\n") + exclude = set(["all", "??", "date"]) + for c in tss.country_codes: + if c in exclude: + continue + series = tss.get_country_series(c) + for i, v in enumerate(series): + if i > 0 and i - INTERVAL >= 0 and series[i] != None and series[i-INTERVAL] != None and series[i-INTERVAL] != 0 and minc[i]!= None and maxc[i]!= None: + minv = minc[i] * poisson.ppf(1-0.9999, series[i-INTERVAL]) + maxv = maxc[i] * poisson.ppf(0.9999, series[i-INTERVAL]) + if not minv < maxv: + print minv, maxv, series[i-INTERVAL], minc[i], maxc[i] + assert minv < maxv + ranges_file.write("%s,%s,%s,%s\n" % (tss.all_dates[i], c, minv, maxv)) + ranges_file.close() + +"""Return a URL that points to a graph in metrics.tpo that displays +the number of direct Tor users in country 'country_code', for a +'period'-days period. + +Let's hope that the metrics.tpo URL scheme doesn't change often. +""" +def get_tor_usage_graph_url_for_cc_and_date(country_code, dates, period): + url = "https://metrics.torproject.org/users.html?graph=direct-users&start=%s&am..." % \ + (dates[-period], dates[-1], country_code) + return url + +"""Write a file containing a short censorship report over the last +'notification_period' days. +""" +def write_ml_report(tss, minx, maxx, INTERV, DAYS, notification_period=None): + if notification_period is None: + notification_period = DAYS + + report_file = codecs.open('short_censorship_report.txt', 'w', 'utf-8') + file_prologue_written = False + + s = tss.get_largest(None) # no restrictions, get 'em all. + scores = [] + for num, li in s: + ds,us = censor_score(tss.get_country_series(li)[-DAYS:], minx[-DAYS:], maxx[-DAYS:], INTERV, notification_period) + scores += [(ds,num, us, li)] + scores.sort() + scores.reverse() + + for downscores,users_n,upscores,country_code in scores: + if (downscores > 0) or (upscores > 0): + if not file_prologue_written: + write_censorship_report_prologue(report_file, tss.all_dates, notification_period) + file_prologue_written = True + + if ((upscores > 0) and (downscores == 0)): + s = "We detected an unusual spike of Tor users in %s (%d upscores, %d users):\n" % \ + (get_country_name_from_cc(country_code), upscores, users_n) + else: + s = "We detected %d potential censorship events in %s (users: %d, upscores: %d):\n" % \ + (downscores, get_country_name_from_cc(country_code), users_n, upscores) + + # Also give out a link for the appropriate usage graph for a 90-days period. + s += get_tor_usage_graph_url_for_cc_and_date(country_code, tss.all_dates, 90) + + report_file.write(s + "\n") + + report_file.close() + +def main(): + # Change these to customize script + CSV_FILE = "direct-users.csv" + GRAPH_DIR = "img" + # Time interval to model connection rates. + INTERV = 7 + # Consider maximum DAYS days back. + DAYS= 6 * 31 + + tss = torstatstore(CSV_FILE) + l = tss.get_largest_locations(50) + minx, maxx = make_tendencies_minmax(l, INTERV) + #plot_all(tss, minx, maxx, INTERV, DAYS, rdir=GRAPH_DIR) + write_all(tss, minx, maxx, INTERV) + + # Make our short report; only consider events of the last day + write_ml_report(tss, minx, maxx, INTERV, DAYS, 1) + +if __name__ == "__main__": + main() diff --git a/detector/detector.sh b/detector/detector.sh new file mode 100755 index 0000000..8e2ea47 --- /dev/null +++ b/detector/detector.sh @@ -0,0 +1,5 @@ +#!/bin/bash +wget -qO direct-users.csv --no-check-certificate https://metrics.torproject.org/csv/direct-users.csv +python detector.py +cat short_censorship_report.txt | mail -E -s 'Possible censorship events' tor-censorship-events@lists.torproject.org +