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Jubatusハンズオン
機械学習はじめてみた
2016/Feb/27 (Sat) 10:00 - 18:00
背景
» 「機械学習とかいいかも」と思って企画書書いたら通った
» 担当者は自分
» 機械学習知らないヤバい
今日目指したゴール
» 機械学習で、なにか自分が知らないアルゴリズムを動かしてみる
» 一応まじめに計算の背景を追ってみる
» 論文まで一応追ってみる
» コードを書くのは次回以降がんばる
anomaly(異常検知)を動かした
» 異常検知は熱いらしい
» Anomaly チュートリアルを参考に、Anomaly(異常検知)を動かして
みた
Jubatusハンズオン 機械学習はじめてみた
利用したデータについて
» 今回はKDDCup 1999のデータを利用
» U.S. Air Force のNWのアクセスログから、正常なアクセスなのか
異常なアクセスなのかを判断する
ログの例
0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,235,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,29,29,1.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,219,1337,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0.00,0.00,0.00,0.00,1.00,0.00,0.00,39,39,1.00,0.00,0.03,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,217,2032,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0.00,0.00,0.00,0.00,1.00,0.00,0.00,49,49,1.00,0.00,0.02,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,217,2032,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,6,6,0.00,0.00,0.00,0.00,1.00,0.00,0.00,59,59,1.00,0.00,0.02,0.00,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,212,1940,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,2,0.00,0.00,0.00,0.00,1.00,0.00,1.00,1,69,1.00,0.00,1.00,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,159,4087,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,5,0.00,0.00,0.00,0.00,1.00,0.00,0.00,11,79,1.00,0.00,0.09,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,210,151,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,8,89,1.00,0.00,0.12,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,212,786,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,8,99,1.00,0.00,0.12,0.05,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,210,624,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,18,18,0.00,0.00,0.00,0.00,1.00,0.00,0.00,18,109,1.00,0.00,0.06,0.05,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,177,1985,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,28,119,1.00,0.00,0.04,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,222,773,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,11,0.00,0.00,0.00,0.00,1.00,0.00,0.00,38,129,1.00,0.00,0.03,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,256,1169,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,4,4,0.00,0.00,0.00,0.00,1.00,0.00,0.00,4,139,1.00,0.00,0.25,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,241,259,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0.00,0.00,0.00,0.00,1.00,0.00,0.00,14,149,1.00,0.00,0.07,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,260,1837,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,11,11,0.00,0.00,0.00,0.00,1.00,0.00,0.00,24,159,1.00,0.00,0.04,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,241,261,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0.00,0.00,0.00,0.00,1.00,0.00,0.00,34,169,1.00,0.00,0.03,0.04,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,257,818,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,12,12,0.00,0.00,0.00,0.00,1.00,0.00,0.00,44,179,1.00,0.00,0.02,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,233,255,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,8,0.00,0.00,0.00,0.00,1.00,0.00,0.25,54,189,1.00,0.00,0.02,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,233,504,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,7,7,0.00,0.00,0.00,0.00,1.00,0.00,0.00,64,199,1.00,0.00,0.02,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,256,1273,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,17,17,0.00,0.00,0.00,0.00,1.00,0.00,0.00,74,209,1.00,0.00,0.01,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,234,255,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,5,5,0.00,0.00,0.00,0.00,1.00,0.00,0.00,84,219,1.00,0.00,0.01,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,241,259,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,12,12,0.00,0.00,0.00,0.00,1.00,0.00,0.00,94,229,1.00,0.00,0.01,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,239,968,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,3,3,0.00,0.00,0.00,0.00,1.00,0.00,0.00,3,239,1.00,0.00,0.33,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,245,1919,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,13,13,0.00,0.00,0.00,0.00,1.00,0.00,0.00,13,249,1.00,0.00,0.08,0.03,0.00,0.00,0.00,0.00,normal.
0,tcp,http,SF,248,2129,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,23,23,0.00,0.00,0.00,0.00,1.00,0.00,0.00,23,255,1.00,0.00,0.04,0.03,0.00,0.00,0.00,0.00,normal.
学習用のデータ
» アクセスログをあらかじめ与え、LOF(Local Outliner Factor:
詳細後述)の計算に用いる
» 何が正常で何が異常か、あらかじめ判断できていなくて良い
» それが判断できているのであれば、単なる分類問題になる
出力
» LOF : データのスコア
» 類似するデータが多い場合(≒正常)、1近辺の値になる
» 類似するデータが少ない場合(≒異常)、非常に大きな値になる
(1000とか)
» LOFの詳細な定義は別資料参照
» 「金塊か、キノコ料理か」(外れ値検出問題)を解く
[LOF(local outlier factor)]
利点
» 正常なデータを用意できれば、異常なデータの候補が見つかる
» 正常/異常の分類をやろうとすると、異常なデータは正常なデータ
と比較して稀になりがち → 正常なデータよりに学習しがち
» ヘタすると正常なデータだけで学習できる
» オンライン学習可能なので、頑張れば時系列で変化するデータにも対
応できそう
欠点
» このアルゴリズムだけでは異常度のスコアしか算出できない
» どのスコア以上なら異常とみなすかという問題は解決しない
» 解決のためには、偽陽性(不要なアラームの発生)・偽陰性(アラー
ムが鳴らない異常)をどこまで認めるかの決めが必要
感想1. 楽しい
» 動かすのは簡単
» Jubatusすごい
» 人力では導けない知識を導出できるのは単純に感動
感想2. 説明がしんどい
» で、それが何なの、に答えるのがしんどい
» こうなりかねない
» A : スコアを見える化しました
» B : で、どうしたらいいんだ
» C : 今と何が違うんだ
» D : 絶対に見逃さないようには出来ないのか
感想3. 次はコード書く
» 頑張る

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