Data Science & System Security | Projects

DATA SCIENCE & SYSTEM SECURITY

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Automated Security Intelligence

Every day it becomes harder to guarantee enterprise security. Sophisticated attacks are launched from economically-driven, well-organized attackers. Systems are complex and evolving, and it is difficult, if not impossible, to keep track of security vulnerabilities and prepare every employee with enough knowledge and skills. To address these challenges, we propose the automated security intelligence project, inspired by the words of Sun Tzu: “If you know your enemies and yourself, you can win a hundred battles without a single loss.”

NGLA: Next Generation Log Analytics

Computer systems generate a huge number of heterogeneous logs which provide rich contextual information about system status and are critical sources for system monitoring and diagnosis. Manually interpreting those logs is not effective, however, due to their large volume and complicated domain-specific syntax and semantic knowledge.

Complex System Modeling and Optimization

With ubiquitous sensing and networking capability, traditional complex physical systems have been undergoing revolutionary changes in their ICT capabilities. They are now equipped with a large number of sensors distributed across different parts of the system, which collect a tremendous amount of data from system operation.

Big Data Analytics

With fast-growing volumes of data in our world, the use of big data will become key to accelerate productivity growth. Our Big Data vision is to build an ecosystem that can handle a large volume and variety of data and extract knowledge from it pertaining to business intelligence, infrastructure management, public safety, health care, fraud detection, etc.

Time Series Sensor Data Analysis

Deep Document Analysis and Large Language Models

Unstructured data is growing at an unprecedented rate, valuable knowledge, including findings, observations, business demand, opportunities, is widely recorded as texts in documents. We are developing advanced analysis engines for mining text data in documents, aiming to discover valuable knowledge from large-scale documents and provide informed decision-making for users.

Skill Acquisition Learning (SAiL)

This project aims to learn skills by mimicking experts’ behaviors in given tasks. The proposed SAiL engine is trained to perform action prediction tasks from demonstrations by learning a mapping function between observed states and actions. The main challenges in real applications, medical and health care, for example, are that the collection of such experts’ demonstrations is very expensive and It takes a large amount of time and money for expert training.

Complex System Simulation and Modeling

As the real-world systems are getting more complex and often beyond the comprehension of humans, the understanding and management of complex systems have emerged as one of the biggest challenges for research, policy and industry. Customers require effective methods and tools to assist them in various tasks, such as accurately predicting the impact of infrastructure investments on supply chain optimization and comprehending the behavior of expansive Internet-based systems in diverse scenarios.

Safe and Trustworthy AI

By leveraging big data and deep learning, in recent years, AI technologies have made significant progress. They have been adopted in many applications including malware detection, image classification, and stock market prediction. As our society becomes more automated, more and more systems will rely on AI techniques. And instead of augmenting human decisions, some AI systems will make their own decisions and execute autonomously.

Dynamic Graph Analysis

In many big data applications, data with complex structures are connected for their explicit/implicit interactions and are naturally represented as graphs/networks. The world is full of complex and dynamic interactions between diverse objects. The flood of dynamic graph data poses great computational challenges and entails interdisciplinary collaborations.

Multimodal Data Analysis

Multimodal data are prevalent in industrial monitoring, finance and healthcare. In particular, time series are often tagged with text comments from experts that provide layman users with the domain knowledge to understand the charts. Texts give the patterns qualitative meaning while time series makes the words quantitative. Analyzing the relationship between different data types is the key to unraveling the hidden structure of such data.

AI for IT Operations (AIOps)

IT operation is one of the technological foundations of the increasingly digitalized world. It is responsible for ensuring that digitalized businesses and societies run reliably, efficiently and safely. With the rapid advances in networking, computers, and hardware, we face an explosive growth of complexity in networked applications and information services. These large-scale, often distributed, information systems usually consist of a great variety of components that work together in a highly complex, coordinated, and evolving manner.

AI for Space

From planetary image analysis to spacecraft monitoring, AI is becoming an increasingly important tool in space exploration and development. Our AI-powered monitoring solution will perform intricate checks to ensure the spacecraft and satellites operate correctly during the production and operation phases.

About Us

NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.