Componentize safe_browsing [+1]: move protos to component

chrome/common/safe_browsing/client_model.proto

-// 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.
-//
-// This proto represents a machine learning model which is used to compute
-// the probability that a particular page visited by Chrome is phishing.
-//
-// Note: sine the machine learning model is trained on the server-side and then
-// downloaded onto the client it is important that this proto file stays in
-// sync with the server-side copy.  Otherwise, the client may not be able to
-// parse the server generated model anymore.  If you want to change this
-// protocol definition or you have questions regarding its format please contact
-// chrome-anti-phishing@googlegroups.com.
-
-syntax = "proto2";
-
-option optimize_for = LITE_RUNTIME;
-
-package safe_browsing;
-
-// This protocol buffer represents a machine learning model that is used in
-// client-side phishing detection (in Chrome).  The client extracts a set
-// of features from every website the user visits.  Extracted features map
-// feature names to floating point values (e.g., PageSecureLinksFreq -> 0.9).
-//
-// To compute the phishing score (i.e., the probability that the website is
-// phishing) a scorer will simply compute the sum of all rule scores for a
-// given set of extracted features.  The score of a particular rule corresponds
-// to the product of all feature values that are part of the rule times the
-// rule weight.  If a feature has no value (i.e., is not part of the extracted
-// features) its value will be set to zero.  The overall score is computed
-// by summing up all the rule scores.  This overall score is a logodds and can
-// be converted to a probability like this:
-// p = exp(logodds) / (exp(logodds) + 1).
-//
-// To make it harder for phishers to reverse engineer our machine learning model
-// all the features in the model are hashed with a sha256 hash function.  The
-// feature extractors also hash the extracted features before scoring happens.
-message ClientSideModel {
-  // In order to save some space we store all the hashed strings in a
-  // single repeated field and then the rules as well as page terms
-  // and page words refer to an index in that repeated field.  All
-  // hashes are sha256 hashes stored in binary format.
-  repeated bytes hashes = 1;
-
-  message Rule {
-    // List of indexes into hashes above which are basically hashed
-    // features that form the current rule.
-    repeated int32 feature = 1;
-
-    // The weight for this particular rule.
-    required float weight = 2;
-  }
-
-  // List of rules which make up the model
-  repeated Rule rule = 2;
-
-  // List of indexes that point to the hashed page terms that appear in
-  // the model.  The hashes are computed over page terms that are encoded
-  // as lowercase UTF-8 strings.
-  repeated int32 page_term = 3;
-
-  // List of hashed page words.  The page words correspond to all words that
-  // appear in page terms.  If the term "one two" is in the list of page terms
-  // then "one" and "two" will be in the list of page words.  For page words
-  // we don't use SHA256 because it is too expensive.  We use MurmurHash3
-  // instead.  See: http://code.google.com/p/smhasher.
-  repeated fixed32 page_word = 4;
-
-  // Page terms in page_term contain at most this many page words.
-  required int32 max_words_per_term = 5;
-
-  // Model version number.  Every model that we train should have a different
-  // version number and it should always be larger than the previous model
-  // version.
-  optional int32 version = 6;
-
-  // List of known bad IP subnets.
-  message IPSubnet {
-    // The subnet prefix is a valid 16-byte IPv6 address (in network order) that
-    // is hashed using sha256.
-    required bytes prefix = 1;
-
-    // Network prefix size in bits.  Default is an exact-host match.
-    optional int32 size = 2 [default = 128];
-  };
-  repeated IPSubnet bad_subnet = 7;
-
-  // Murmur hash seed that was used to hash the page words.
-  optional fixed32 murmur_hash_seed = 8;
-
-  // Maximum number of unique shingle hashes per page.
-  optional int32 max_shingles_per_page = 9 [default = 200];
-
-  // The number of words in a shingle.
-  optional int32 shingle_size = 10 [default = 4];
-}