NVIDIA Morpheus is a GPU-accelerated cybersecurity AI framework that makes it easy to build and scale cybersecurity applications that harness adaptive pipelines supporting a wider range of model complexity than previously feasible. Morpheus makes it possible to analyze up to 100 percent of your data in real-time, for more accurate detection and faster remediation of threats as they occur. Morpheus also provides the ability to leverage AI to adjust to threats and compensate on the fly, at line rate. With Morpheus organizations can attack the issue of cybersecurity head on. Rather than continuously chasing the cybersecurity problem, Morpheus provides the ability to propel you ahead of a breach and address the cybersecurity issue. With the world in a "discover and respond" state, where companies are finding breaches much too late, in a way that is way behind the curve, NVIDIA’s Morpheus cybersecurity AI framework enables any organization to warp to the present and begin to defend itself in real time.
Massive Performance and Scale - Morpheus is GPU-accelerated enabling, for the first time, the ability to inspect all network traffic in real-time, flag anomalies, and provide insights on these anomalies so that threats can be addressed quickly. It enables AI inference and real-time monitoring of every server and packet across the entire network.
Rapid Development and Deployment - Morpheus integrates AI frameworks and tools that make it easier for developers to build cybersecurity solutions. Organizations that lack AI expertise can still leverage AI for cybersecurity because Morpheus leverages tools for every stage of the AI workflow, from data preparation to training, inference, and deploying at scale.
Real-time Telemetry - The Morpheus native graph streaming engine can receive rich, real-time network telemetry from every NVIDIA BlueField DPU-accelerated server or NVIDIA AppShield in the data center without impacting performance. Integrating the framework into a third-party cybersecurity offering brings the world’s best AI computing to communication networks.
AI Cybersecurity Capabilities – Deploy your own models using common deep learning frameworks. Or use a Morpheus pre-trained and tested model to get a jump-start in building applications to identify leaked sensitive information, detect malware or fraud, do network mapping, flag user behavior changes, or and identify errors via logs.
More details of the July 2023 release (23.07) can be found here.
More details of the March 2023 release (23.03) can be found here.
This release placed emphasis on re-organization and improvements of Morpheus documentation.
More details of the January 2023 release (23.01) can be found here.
Public Cloud: Morpheus can be deployed into AWS EC2, GCP, Azure, and now OCI using an appropriate GPU-enabled instance and NVIDIA’s Cloud Native Stack. Note that when installing Kubernetes to an Ubuntu OCI instance using the Stack, it may be necessary to relax some pre-defined iptables rules. Some information can be found here.
More details of the November 2022 release (22.11) can be found here.
Public Cloud: Morpheus can be deployed into AWS EC2, GCP, and Azure using an appropriate GPU-enabled instance and NVIDIA’s Cloud Native Core GPU Operator.
Digital Fingerprinting: This use case now includes finely tunable explainability and thresholding to customize a workflow for your environment, and a new visualization tool for digital fingerprinting providing massive data reduction for analyzed events so that security analysts can more quickly investigate and remediate.
Sensitive Information Detection: This use case now includes a visual graph-based explainer enabling security analysts to more easily identify leaked sensitive data.
Multi-process pipeline: This feature enables new workflows to be intelligently batched to reduce latency.
The details of the July 2022 maintenance release (22.06) can be found here.
NVIDIA Morpheus is generally available for download on GitHub or NGC in April 2022.
Streaming Graph Neural Networks for Fraud Detection: unlocks a capability that was not feasible before without massive amounts of labeled data. GNN requires orders of magnitude less training data to do feature engineering for fraud detection.
Developer Toolkit: Developers can easily leverage the benefits of back pressure, reactive programming, and fibers to build cybersecurity solutions using a higher-level API that allows them to program traditionally but gain these benefits. These optimizations don’t exist in any other streaming framework. Morpheus now provides documentation for building custom pipelines with Morpheus’ Python and C++ abstraction layers.
Performance improvements: Increased acceleration and multi-GPU support enabling support for an increased number of models, and exponentially larger models on a single node. Scale up to as many GPUs supported on an NVIDIA-Certified or NVIDIA DGX server. Developers typically have to choose between writing something quickly, think Python, with minimal lines of code, or writing something more performant that doesn’t have the performance ceiling that Python does. With Morpheus, you get both. Developers can write orders of magnitude less code (down to 180 lines from over 1,000 lines) and get an unbounded performance ceiling. This enables developers to achieve better performance in less time, translating to cost savings.
New Integrations: NVIDIA Morpheus provides the optimal AI/ML-based accelerated cybersecurity pipeline to modern enterprise data centers with NVIDIA BlueField as a high-speed, high-fidelity source and NVIDIA AppShield to identify and stop ransomware at the point of entry.
Morpheus DLI Course: Get hands-on experience building end-to-end pipelines with Morpheus for several cybersecurity use cases including Sensitive Information Detection and Digital Fingerprinting. Enroll in the Morpheus DLI course and use Morpheus to ingest and preprocess data, perform AI-enabled inference on the data, and stream the results in real time for analysis and action.
NVIDIA BlueField-2 DPU (Data Processing Unit), available today, enables true software defined, hardware accelerated data center infrastructure. It enables a computer in front of computer (application server) architecture that offloads, accelerates and isolates data center functions.The DPU also extends the simple static security logging model and implements sophisticated dynamic telemetry that evolves with new policies being determined and adjusted. NVIDIA’s upcoming BlueField-3 takes these capabilities to a whole new level. It is the industry’s first DPU to offer 400Gb/s networking, bringing 5X more compute power and 4X more crypto accelerations compared to the previous generation - all while delivering full backward compatibility with BlueField-2 through the DOCA (Software Defined Data Center infrastructure on a Chip Architecture) software development kit (SDK).
The continued growth of data center traffic, which is driven by the growth of East West network traffic results in building larger distributed data centers. This opens the door to new and more sophisticated cyberattacks, making traditional perimeter security models obsolete. New pervasive, intelligent, and adaptive approaches that can keep up with the huge amount of distributed data is needed. The Morpheus cyber platform leverages NVIDIA’s core competencies in deep learning and AI and applies them to networking and distributed computing to enable the next generation of advanced data center security solutions.
Morpheus is distributed as open source software under the Apache Software License 2.0.
NVIDIA AI Enterprise provides global support for NVIDIA AI software, including Morpheus. For more information on NVIDIA AI Enterprise please consult this overview and the NVIDIA AI Enterprise End User License Agreement.