My Secret Rainbow

DAD在未降维的高维原始数据中表现优异，成功提取大规模数据的关系，通常情况下，网络越深，提取效果越好，这个部分感兴趣的话可以参考下面这篇文章。

• Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436, 2015.

 五、数据标签的可用性Availability of Labels

数据标签是非常重要的事情，标签代表着正常(normal)数据或是未见过(unseen/novel)的数据，对于标签内容的使用同样是现在异常检测方向论文重点考虑的事情。异常检测的模型也可以根据数据标签的内容广义的分为三类，监督，半监督和无监督。

1. 监督Supervised DAD

基于监督学习的DAD文章，整理了两篇医学方向的，由于监督学习对于标签内容的依赖度过重，所以他对于异常检测的问题并不是那么合适，所以它并不如半监督和无监督应用地那么广泛。

• Raghavendra Chalapathy, Ehsan Zare Borzeshi, and Massimo Piccardi. An investigation of recurrent neural architectures for drug name recognition. arXiv preprint arXiv:1609.07585, 2016a.
• Raghavendra Chalapathy, Ehsan Zare Borzeshi, and Massimo Piccardi. Bidirectional lstm-crf for clinical concept extraction. arXiv preprint arXiv:1611.08373, 2016b.

2. 半监督Semi-supervised DAD

由于正常数据集比异常数据集更好获得，所以半监督学习DAD方法被非常广泛的使用，拥有了足够的数据集，我们能更好地标出正常数据，异常数据，新数据的界限，半监督学习模型列举三篇论文。

• Drausin Wulsin, Justin Blanco, Ram Mani, and Brian Litt. Semi-supervised anomaly detection for eeg waveforms using deep belief nets. In Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on, pages 436–441. IEEE, 2010.
• Mutahir Nadeem, Ochaun Marshall, Sarbjit Singh, Xing Fang, and Xiaohong Yuan. Semi-supervised deep neural network for network intrusion detection. 2016.
• Hongchao Song, Zhuqing Jiang, Aidong Men, and Bo Yang. A hybrid semi-supervised anomaly detection model for high-dimensional data. Computational intelligence and neuroscience, 2017.

3. 无监督Unsupervised DAD

传统机器学习算法其实我感觉更倾向于直接从数据集中让机器去学习一些东西，然后直接用参数的方式表示出来，异常检测问题同样我们也用自动标签的方式去检测是否异常，因为有时候可能数据难以获取。自动解码器是无监督DAD的核心，所以这里深度学习的一些神经网络大有可为，例如RNN，LSTM等等。我们这里只列举了一种采用变种半监督学习方法的论文，应用于异常数据降维，表现效果超越很多传统降维算法，如PCA，Isolation等等。

• Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, and Sean Robinson. Deep learning for unsupervised insider threat detection in structured cybersecurity data streams. arXiv preprint arXiv:1710.00811, 2017.

 六、基于训练对象的模型

按照训练对象的区别，我们把训练模型单独划分为两类，变种模型与单分类神经网络。

1. 深度变种模型Deep Hybrid Models(DHM)

• Jerone TA Andrews, Edward J Morton, and Lewis D Griffin. Detecting anomalous data using auto-encoders. International Journal of Machine Learning and Computing, 6(1):21, 2016a.
• Tolga Ergen, Ali Hassan Mirza, and Suleyman Serdar Kozat. Unsupervised and semi-supervised anomaly detection with lstm neural networks. arXiv preprint arXiv:1710.09207, 2017.

2. 单分类神经网络One-Class Neural Networks(OC-NN)

• Raghavendra Chalapathy, Aditya Krishna Menon, and Sanjay Chawla. Anomaly detection using one-class neural networks. arXiv preprint arXiv:1802.06360, 2018a.

 七、数据异常类型

1. 点集Point

举信用卡盗刷的例子，点集异常就是指单笔交易大金额支出，比如你都花1块2块的钱，突然有一天消费了1k，那可能就出现了异常情况，但这个方向好像没有人单独发过文章。

2. 连续集Contextual or Conditional

连续集就是指上下文相关的连续数据，某一个中间数据出现了异常情况，可能引起了梯度消失爆炸等等问题。

• Xiuyao Song, Mingxi Wu, Christopher Jermaine, and Sanjay Ranka. Conditional anomaly detection. IEEE Transactions on Knowledge and Data Engineering, 19(5):631–645, 2007.

3. 团队集Collective or Group

还是信用卡盗刷的例子，如果某天你的信用卡突然短时间内不停地消费50元，那机器可能会发现，这些团队数据集的消费出现了异常，这种情况我们也在其他场合经常遇到。

• Raghavendra Chalapathy, Edward Toth, and Sanjay Chawla. Group anomaly detection using deep generative models. arXiv preprint arXiv:1804.04876, 2018b.
• Lo¨ıc Bontemps, James McDermott, Nhien-An Le-Khac, et al. Collective anomaly detection based on long short-term memory recurrent neural networks. In International Conference on Future Data and Security Engineering, pages 141–152. Springer, 2016.
• Daniel B Araya, Katarina Grolinger, Hany F ElYamany, Miriam AM Capretz, and G Bitsuamlak. Collective contextual anomaly detection framework for smart buildings. In Neural Networks (IJCNN), 2016 International Joint Conference on, pages 511–518. IEEE, 2016.
• Naifan Zhuang, Tuoerhongjiang Yusufu, Jun Ye, and Kien A Hua. Group activity recognition with differential recurrent convolutional neural networks. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, pages 526–531. IEEE, 2017.

   八、idea新颖的论文

这里我再给大家推荐8篇idea比较新颖的论文，可供大家阅读与交流。
[1] Liu W, Luo W, Lian D, et al. Future frame prediction for anomaly detection–a new baseline[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 6536-6545.

[2] Gong D, Liu L, Le V, et al. Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1705-1714.

[3] Park H, Noh J, Ham B. Learning Memory-guided Normality for Anomaly Detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 14372-14381.

[4] Zhao Y, Deng B, Shen C, et al. Spatio-temporal autoencoder for video anomaly detection[C]//Proceedings of the 25th ACM international conference on Multimedia. 2017: 1933-1941.

[5] Ionescu R T, Khan F S, Georgescu M I, et al. Object-centric auto-encoders and dummy anomalies for abnormal event detection in video[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 7842-7851.

[6] Liu W, Luo W, Li Z, et al. Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies[C]//IJCAI. 2019: 3023-3030.

[7] Sultani W, Chen C, Shah M. Real-world anomaly detection in surveillance videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 6479-6488.

[8] Luo W, Liu W, Gao S. A revisit of sparse coding based anomaly detection in stacked rnn framework[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 341-349.

原文：https://mp.weixin.qq.com/s/trB_mZTUlATyo6FFjiLXxw