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// Copyright (c) 2011 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "chrome/renderer/safe_browsing/scorer.h"
#include <stdint.h>
#include "base/containers/hash_tables.h"
#include "base/files/file_path.h"
#include "base/files/scoped_temp_dir.h"
#include "base/format_macros.h"
#include "base/memory/scoped_ptr.h"
#include "base/message_loop/message_loop.h"
#include "base/threading/thread.h"
#include "chrome/common/safe_browsing/client_model.pb.h"
#include "chrome/renderer/safe_browsing/features.h"
#include "testing/gmock/include/gmock/gmock.h"
#include "testing/gtest/include/gtest/gtest.h"
namespace safe_browsing {
class PhishingScorerTest : public ::testing::Test {
protected:
void SetUp() override {
// Setup a simple model. Note that the scorer does not care about
// how features are encoded so we use readable strings here to make
// the test simpler to follow.
model_.Clear();
model_.add_hashes("feature1");
model_.add_hashes("feature2");
model_.add_hashes("feature3");
model_.add_hashes("token one");
model_.add_hashes("token two");
ClientSideModel::Rule* rule;
rule = model_.add_rule();
rule->set_weight(0.5);
rule = model_.add_rule();
rule->add_feature(0); // feature1
rule->set_weight(2.0);
rule = model_.add_rule();
rule->add_feature(0); // feature1
rule->add_feature(1); // feature2
rule->set_weight(3.0);
model_.add_page_term(3); // token one
model_.add_page_term(4); // token two
// These will be murmur3 hashes, but for this test it's not necessary
// that the hashes correspond to actual words.
model_.add_page_word(1000U);
model_.add_page_word(2000U);
model_.add_page_word(3000U);
model_.set_max_words_per_term(2);
model_.set_murmur_hash_seed(12345U);
model_.set_max_shingles_per_page(10);
model_.set_shingle_size(3);
}
ClientSideModel model_;
};
TEST_F(PhishingScorerTest, HasValidModel) {
scoped_ptr<Scorer> scorer;
scorer.reset(Scorer::Create(model_.SerializeAsString()));
EXPECT_TRUE(scorer.get() != NULL);
// Invalid model string.
scorer.reset(Scorer::Create("bogus string"));
EXPECT_FALSE(scorer.get());
// Mode is missing a required field.
model_.clear_max_words_per_term();
scorer.reset(Scorer::Create(model_.SerializePartialAsString()));
EXPECT_FALSE(scorer.get());
}
TEST_F(PhishingScorerTest, PageTerms) {
scoped_ptr<Scorer> scorer(Scorer::Create(model_.SerializeAsString()));
ASSERT_TRUE(scorer.get());
// Use std::vector instead of base::hash_set for comparison.
// On Android, EXPECT_THAT(..., ContainerEq(...)) doesn't support
// std::hash_set, but std::vector works fine.
std::vector<std::string> expected_page_terms;
expected_page_terms.push_back("token one");
expected_page_terms.push_back("token two");
std::sort(expected_page_terms.begin(), expected_page_terms.end());
base::hash_set<std::string> page_terms = scorer->page_terms();
std::vector<std::string> page_terms_v(page_terms.begin(), page_terms.end());
std::sort(page_terms_v.begin(), page_terms_v.end());
EXPECT_THAT(page_terms_v, ::testing::ContainerEq(expected_page_terms));
}
TEST_F(PhishingScorerTest, PageWords) {
scoped_ptr<Scorer> scorer(Scorer::Create(model_.SerializeAsString()));
ASSERT_TRUE(scorer.get());
std::vector<uint32_t> expected_page_words;
expected_page_words.push_back(1000U);
expected_page_words.push_back(2000U);
expected_page_words.push_back(3000U);
std::sort(expected_page_words.begin(), expected_page_words.end());
base::hash_set<uint32_t> page_words = scorer->page_words();
std::vector<uint32_t> page_words_v(page_words.begin(), page_words.end());
std::sort(page_words_v.begin(), page_words_v.end());
EXPECT_THAT(page_words_v, ::testing::ContainerEq(expected_page_words));
EXPECT_EQ(2U, scorer->max_words_per_term());
EXPECT_EQ(12345U, scorer->murmurhash3_seed());
EXPECT_EQ(10U, scorer->max_shingles_per_page());
EXPECT_EQ(3U, scorer->shingle_size());
}
TEST_F(PhishingScorerTest, ComputeScore) {
scoped_ptr<Scorer> scorer(Scorer::Create(model_.SerializeAsString()));
ASSERT_TRUE(scorer.get());
// An empty feature map should match the empty rule.
FeatureMap features;
// The expected logodds is 0.5 (empty rule) => p = exp(0.5) / (exp(0.5) + 1)
// => 0.62245933120185459
EXPECT_DOUBLE_EQ(0.62245933120185459, scorer->ComputeScore(features));
// Same if the feature does not match any rule.
EXPECT_TRUE(features.AddBooleanFeature("not existing feature"));
EXPECT_DOUBLE_EQ(0.62245933120185459, scorer->ComputeScore(features));
// Feature 1 matches which means that the logodds will be:
// 0.5 (empty rule) + 2.0 (rule weight) * 0.15 (feature weight) = 0.8
// => p = 0.6899744811276125
EXPECT_TRUE(features.AddRealFeature("feature1", 0.15));
EXPECT_DOUBLE_EQ(0.6899744811276125, scorer->ComputeScore(features));
// Now, both feature 1 and feature 2 match. Expected logodds:
// 0.5 (empty rule) + 2.0 (rule weight) * 0.15 (feature weight) +
// 3.0 (rule weight) * 0.15 (feature1 weight) * 1.0 (feature2) weight = 9.8
// => p = 0.99999627336071584
EXPECT_TRUE(features.AddBooleanFeature("feature2"));
EXPECT_DOUBLE_EQ(0.77729986117469119, scorer->ComputeScore(features));
}
} // namespace safe_browsing
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