Machine Learning and Data Science

Machine Learning and Data Science
This is an overview of all content belonging to this term.


Santa Clara, Oct. 29 2015-Nov. 1 2015

Eagle: User Profile-based Anomaly Detection for Securing Hadoop Clusters

Chaitali Gupta, Ranjan Sinha, Yong Zhang

Existing Big data analytics platforms, such as Hadoop, lack support for user activity monitoring. Several diagnostic tools such as Ganglia, Ambari, and Cloudera Manager are available to monitor health of a cluster, however, they do not provide algorithms to detect security threats or perform user activity monitoring. Hence, there is a need to develop a scalable system that can detect malicious user activities, especially in real-time, so that appropriate actions can be taken against the user. At eBay, we developed such a system named Eagle, which collects audit logs from Hadoop clusters and applications running on them, analyzes users behavior, generates profiles per user of the system, and predicts anomalous user activities based on their prior profiles. Eagle is a highly scalable system, capable of monitoring multiple eBay clusters in real-time. It includes machine-learning algorithms that create user profiles based on the user's history of activities. As far as we know, this is the first activity monitoring system on the Hadoop-ecosystem for the detection of intrusion-related activities using behavior-based profiles of users. When a user performs any operation in the cluster, Eagle matches current user action against his prior activity pattern and raises alarm if it suspects anomalous action. We investigate two machine-learning algorithms: density estimation, and principal component analysis (PCA). In this paper, we introduce the Eagle system, discuss the algorithms in detail, and show performance results. We demonstrate that the sensitivity of the density estimation algorithm is 93%, however the sensitivity of our system increases by 4.94% (on average) to 98% (approximately) by using an ensemble of the two algorithms during anomaly detection.

Proceedings of NAACL-HLT 2015, pages 160–167, Denver, Colorado, May 31 – June 5, 2015. c 2015 Association for Computational Linguistics

Distributed Word Representations Improve NER for e-Commerce

This paper presents a case study of using distributed word representations, word2vec in particular, for improving performance of Named Entity Recognition for the e-Commerce domain. We also demonstrate that distributed word representations trained on a smaller amount of in-domain data are more effective than word vectors trained on very large amount of out-of-domain data, and that their combination gives the best results.

Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 805–814, Jeju, Republic of Korea, 8-14 July 2012. c 2012 Association for Computational Linguistics

Structuring E-Commerce Inventory

Large e-commerce enterprises feature millions of items entered daily by a large variety of sellers. While some sellers provide rich, structured descriptions of their items, a vast majority of them provide unstructured natural language descriptions. In the paper we present a 2 steps method for structuring items into descriptive properties. The first step consists in unsupervised property discovery and extraction. The second step involves supervised property synonym discovery using a maximum entropy based clustering algorithm. We evaluate our method on a year worth of ecommerce
data and show that it achieves excellent precision with good recall.

40th International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015

Switching to and Combining Offline-Adapted Cluster Acoustic Models based on Unsupervised Segment Classification

The performance of automatic speech recognition system degrades significantly when the incoming audio differs from training data. Maximum likelihood linear regression has been widely used for unsupervised adaptation, usually in a multiple-pass recognition process. Here we present a novel adaptation framework for which the offline, supervised, high-quality adaptation is applied to clustered channel/speaker conditions that are defined with automatic and manual clustering of the training data. Upon online recognition, each speech segment is classified into one of the training clusters in an unsupervised way, and the corresponding top acoustic models are used for recognition. Recognition lattice outputs are combined. Experiments are performed on the Wall Street Journal data, and a 37.5% relative reduction of Word Error Rate is reported. The proposed approach is also compared with a general speaker adaptive training approach.

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Correcting Keyboard Layout Errors and Homoglyphs in Queries

Keyboard layout errors and homoglyphs in cross-language queries impact our ability to correctly interpret user information needs and offer relevant results. We present a machine learning approach to correcting these errors, based largely on character-level n-gram features. We demonstrate superior performance over rule-based methods, as well as a significant reduction in the number of queries that yield null search results.