Data Science and System SecurityOur Data Science & System Security department aims to build novel big-data solutions and service platforms to simplify complex systems management. We develop new information technology that supports innovative applications, from big data analytics to the Internet of Things.

Our experimental and theoretical research includes many data science and systems research domains. These include but are not limited to time series mining, deep learning, NLP and large language models, graph mining, signal processing, and cloud computing. Our research aims to fully understand the dynamics of big data from complex systems, retrieve patterns to profile them and build innovative solutions to help the end user manage those systems. We have built several analytic engines and system solutions to process and analyze big data and support various detection, prediction, and optimization applications. Our research has led to award-winning NEC products and publications in top conferences.

Read our data science and system security news and publications from our world-class researchers.

Posts

LogLens: A Real-time Log Analysis System

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Administrators of most user-facing systems depend on periodic log data to get an idea of the health and status of production applications. Logs report information, which is crucial to diagnose the root cause of complex problems. In this paper, we present a real-time log analysis system called LogLens that automates the process of anomaly detection from logs with no (or minimal) target system knowledge and user specification. In LogLens, we employ unsupervised machine learning based techniques to discover patterns in application logs, and then leverage these patterns along with the real-time log parsing for designing advanced log analytics applications. Compared to the existing systems which are primarily limited to log indexing and search capabilities, LogLens presents an extensible system for supporting both stateless and stateful log analysis applications. Currently, LogLens is running at the core of a commercial log analysis solution handling millions of logs generated from the large-scale industrial environments and reported up to 12096x man-hours reduction in troubleshooting operational problems compared to the manual approach.

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Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

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Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score.

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Co-Regularized Deep Multi-Network Embedding

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Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further refined node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is flexible. It allows different networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts different spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.

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Towards a Timely Causality Analysis for Enterprise Security

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The increasingly sophisticated Advanced Persistent Threat (APT) attacks have become a serious challenge for enterprise IT security. Attack causality analysis, which tracks multi-hop causal relationships between files and processes to diagnose attack provenances and consequences, is the first step towards understanding APT attacks and taking appropriate responses. Since attack causality analysis is a time-critical mission, it is essential to design causality tracking systems that extract useful attack information in a timely manner. However, prior work is limited in serving this need. Existing approaches have largely focused on pruning causal dependencies totally irrelevant to the attack, but fail to differentiate and prioritize abnormal events from numerous relevant, yet benign and complicated system operations, resulting in long investigation time and slow responses.

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