Unsupervised anomaly detection using style distillation

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Reference: https://hoya012.github.io/blog/anomaly-detection-overview-1/

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Reference: https://hoya012.github.io/blog/anomaly-detection-overview-1/

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Reference: https://towardsdatascience.com/knowledge-distillation-simplified-dd4973dbc764
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Reference: https://towardsdatascience.com/knowledge-distillation-simplified-dd4973dbc764
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SNUAI 9th | Unsupervised Anomaly Detection Overview using Convolutional Autoencoders
Reference: https://towardsdatascience.com/knowledge-distillation-simplified-dd4973dbc764
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過去10年間で、機械学習(Machine Learning: ML)ベースのネットワーク侵入検知技術はWebセキュリティ業界において注目に値する勢いを獲得しました。一部の侵入検知システム(Intrusion Detection System: IDS)はこの技術を利用して侵入を検出し、ネットワークサービスに影響を与えさせることなく弾くことに成功しています。MLベースのIDSについて簡単に説明すると、IDSはまずWebのアクセスログを入力として正常なトラフィックのモデルを構築します。そして、オンライン処理システムにより正しいトラフィックの情報と不正なトラフィックの情報をモデルに学習させていきます。IDSはモデルと比較してある閾値以上離れたトラフィックを不正なものとして判定します。この理論では、システムに与えられるデータが多いほどモデルが正確になるため、進化・成長を続けるWebのトラフィックパターンを解析する上では一見して万能なシステムであるかのように思われます。しかし、このお伽話はそう長くは続きませんでした。この分類モデルに対して、攻撃者が「ポイズニング」を行うことで侵入検知を回避していることが分かったのです。攻撃者はそれ自体は無害なトラフィックを大量に送り、攻撃ベクトルが閾値内に収まるように少しずつモデルを学習させるという方法で攻撃に成功しました。この講演では、この「モデルポイズニング」攻撃のデモを行うと共に、MLベースの異常検知システムを攻撃者の操作による影響を受けにくくするための手法を解説します。背景にあるMLの理論そのものよりも、実世界で動いているこういったIDSやそれらを無効化する攻撃、そしてネットワーク侵入検知に関わる開発者への影響についてお話しします。さらに、最も重要な点としてMLベースのネットワーク侵入検知の未来についてお話しします。

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PyData London 2018 This talk will focus on the importance of correctly defining an anomaly when conducting anomaly detection using unsupervised machine learning. It will include a review of Isolation Forest algorithm (Liu et al. 2008), and a demonstration of how this algorithm can be applied to transaction monitoring, specifically to detect money laundering. --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

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Liming Han, Yimin Lin, Guoguang Du, Shiguo Lian : DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints, arXiv preprint arXiv:1906.11435 (2019), https://arxiv.org/abs/1906.11435 公開URL: https://arxiv.org/abs/1906.11435 概要: 6-DoF運動の推定はロボット工学において重要な課題の一つです。教師あり学習のVisual Inertial Odometry(VIO)は存在しますが、学習のための大量のGround Truthデータが必要となります。この問題を解決するために、低コストのステレオカメラを用いた自己教師ありVIOシステムが提案されています。2次元のオプティカルフロー特徴量とIMUのデータをマージすることによって、絶対位置の軌跡を推定します。

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