Enabling AI-powered MDR Services Using Lanner NCA-6530 And NVIDIA A30 - Lanner Electronics | Network Appliance | uCPE SD-WAN| MEC Server | Intelligent Edge Appliance

Introduction

As cyber threats continue to evolve in sophistication and complexity, organizations are facing challenges in effectively detecting and responding to security incidents in real time.

Managed Detection and Response (MDR) services have emerged as an industrial automation solution to this problem, offering continuous monitoring, threat detection and rapid incident response. Only by incorporating AI, machine learning and high-performance hardware can organizations of all sizes significantly enhance their threat detection, response capabilities and overall cybersecurity posture.

Solution

Lanner’s NCA-6530 is a high-performance 2U 19” rackmount network security platform with support for dual 4th Gen Intel Xeon Scalable processors (codenamed Sapphire Rapids-SP) and it features a built-in accelerator for high throughputs, advanced hardware-enabled security and support for up to 8x NIC modules for expansion. This appliance supports up to 1536GB of system memory, 6x hot-swappable fans, 1600W/2000W redundant power supply, Intel® QAT, and optional PCIe expansion.

Designed specifically for network security applications, the NCA-6530’s robust hardware components and flexible network interfaces together make it ideal for processing and analyzing large amounts of network traffic in real time.

The NVIDIA A30 Tensor Core GPU, on the other hand, is a powerful AI accelerator capable of handling complex AI workloads such as deep learning and neural network computations.

The NCA-6530’s compatibility with NVIDIA A30 Tensor Core GPU makes available versatile and unprecedented compute acceleration for mainstream enterprise servers at every scale, not only providing the necessary computational power for real-time AI inference but also enabling accelerated deep learning algorithms for security tasks including data mining, data collation and sifting.

The NCA-6530 is seamlessly complemented by the A30 when handling additional AI workloads, providing optimal performance and improved scalability for MDR services.

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Benefits

Lanner NCA-6530 and NVIDIA A30 Tensor Core GPU bring together security automation, machine learning and human intelligence for MDR services capable of learning to identify patterns associated with known cyber threats, anomalous behaviors and zero-day vulnerabilities through data collection, AI model training, real-time threat detection/response and continuous learning.

• Data Collection

The NCA-6530 captures network traffic data from sources within an organization's network infrastructure, including endpoints, servers and network devices; such data is then preprocessed in order to remove noise and irrelevant information.

• AI Model Training

The AI model is trained using historical data that includes labeled instances of normal and malicious network activities. The training process does leverage NVIDIA A30 GPU’s computational power in order to accelerate the training of deep learning models.

• Real-Time Threat Detection

Upon training completion and system feedback, the incoming network traffic can then be analyzed in real time, identifying patterns and behaviors that deviate from the established baseline, enabling rapid detection of suspicious activities and potential security threats.

• Incident Response

The MDR system triggers an automated or human-driven incident response process as soon as a potential threat is detected; the severity of the threat is classified and the correct course of actions for threat containment and elimination is recommended.

• Continuous Learning

The AI model is continuously updated using newly collected data to adapt to emerging threats and evolving attack techniques.

Conclusion

The seamless synergy between the Lanner NCA-6530 network appliance and the NVIDIA A30 Tensor Core GPU creates a robust solution for implementing AI-enabled MDR services, making available powerful advantages such as enhanced threat detection, efficient resource utilization, automated response and adaptability for all who wish to transition from a slow and reactive security service to a cyber security service anticipates and stops threats at their nascent stage without delays.

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