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Evaluating GPU Usage in Cybersecurity

TABLE OF CONTENTS

Cyber threats continue to grow in both sophistication and frequency as IoT cyber attacks alone are expected to double next year. A report by Cybersecurity Venture has predicted that global cybercrime costs will reach USD 10.5 trillion annually by 2025. Yet the average time to detect and contain a data breach is 287 days. To keep pace, organisations need advanced security solutions that can accelerate threat detection, analysis and mitigation.

GPUs offer amazing capabilities for cybersecurity, leveraging their parallel processing power to achieve dramatic performance gains. By offloading critical workloads like intrusion detection, malware analysis, and encryption to the GPU, solutions can rapidly scale to beat the worst-case scenario (100% malicious traffic). 

However, simply adding GPUs does not guarantee effective security outcomes. Organisations must evaluate usage across key metrics to ensure GPU resources are allocated properly across various workloads. Optimised GPU utilisation can maximise threat coverage while also lowering costs, which are around USD 4.35 million per breach.

Role of GPUs in Cybersecurity

Renowned for their high processing power, GPUs help to enhance various cybersecurity applications, thanks to their ability to handle large volumes of data and complex computations efficiently. Now, let’s discuss the role of GPUs in cybersecurity, particularly in areas like threat detection, malware analysis, and intrusion prevention, which are increasingly significant.

Threat Detection

GPUs accelerate threat detection by enabling network security tools to rapidly analyse large volumes of traffic for indicators of compromise. Their massively parallel architecture facilitates high-speed pattern matching, machine learning inference, decryption/inspection of encrypted traffic, and other techniques to identify malicious activity. By offloading these heavy workloads, GPUs act as a network security co-processor to vastly improve the speed and scale of detecting known and emerging threats.

Malware Analysis

The surging volume and sophistication of malware require accelerated analysis to derive timely threat intelligence and improve defences. GPUs parallel capabilities are aptly suited to speed up malware static, dynamic, and variant analysis. Static analysis inspects code without execution to classify malware family/strain rapidly using machine learning. Dynamic analysis observes real-time behaviours by executing malware in an isolated sandbox to identify capabilities. The variant analysis compares similarities and changes against earlier samples to pinpoint code reuse or evolutionary links. 

Intrusion Prevention

Intrusion prevention systems (IPS) take quick actions to identify threats before they can spread or cause harm. Here, GPUs dramatically accelerate pattern-matching workloads to instantly detect attacks for rapid auto-quarantining and blocking at line-rate network speeds. Monitoring traffic and simultaneously handling threat prevention countermeasures is possible even at 100Gbps by offloading compute-intensive rules onto GPUs.

Tasks Where GPUs Excel in Cybersecurity 

When it comes to cybersecurity workloads, GPUs have tremendous performance gains for tasks involving parallelisation, floating point calculations and machine learning inference. 

Machine Learning Inference

GPU for machine learning and tensor core accelerators specialise in efficiently running predictions from large, complex neural network models used for threat detection. Sophisticated deep-learning cybersecurity models with hundreds of millions of parametres can analyse network traffic, emails, files, endpoints, and other data points for subtle indicators of cyberattacks. These enormous models are too computationally intense to run in real-time on CPUs but GPU parallelism makes scoring practical on live data flows.

Data Parallelisation

The massively parallel architecture of GPUs enables concurrent processing of huge volumes of security telemetry to uncover anomalies. Threat hunting leverages data parallelisation by running correlation analysis across diverse datasets like network traffic, DNS requests, user activity logs, file changes and more in unison to spot hard-to-detect attack patterns. GPUs scale out this multi-dimensional security analytics to terabytes of historical data. 

Network Solution

Inline network security tools rely on ultra-fast pattern matching against constantly updated threats to catch attacks in real-time. GPUs scan inbound and outbound network traffic at line-rate speeds by comparing packets and payloads against signatures and regular expression rules in parallel. For example, NVIDIA BlueField supports hardware-accelerated encryption and decryption of both Ethernet storage traffic and the storage media itself, helping protect against data theft or exfiltration. It offloads IPsec at up to 100Gb/s (data on the wire) and 256-bit AES-XTS at up to 200Gb/s (data at rest), reducing the risk of data theft if an adversary has tapped the storage network or if the physical storage drives are stolen or sold or disposed of improperly.

Metrics for Evaluating GPU Usage in Cybersecurity

Evaluating GPU usage in cybersecurity involves understanding several key metrics that directly impact the performance and effectiveness of security solutions. These metrics include throughput, latency, memory utilisation, and power consumption.

  1. Throughput: This refers to the amount of data processed by the GPU in a given period. High throughput is crucial for cybersecurity applications where large volumes of data need to be analysed quickly, such as in network traffic analysis or AI-driven threat detection. NVIDIA’s Morpheus, for example, is a GPU-accelerated SDK that can inspect all network traffic in real-time, flag anomalies, and provide insights on these anomalies so that threats can be addressed quickly.

  2. Latency: Latency measures the time it takes for a task to be completed after it has been initiated. In cybersecurity, lower latency is critical for real-time threat detection and response. 

  3. Memory utilisation: This metric refers to how effectively a GPU uses its memory resources. Efficient memory utilisation is important for handling the large and complex datasets typical in cybersecurity applications. For example, GPUs are used for their parallel processing capabilities in AI-driven cybersecurity, allowing for fast and effective processing of large datasets​​.

  4. Power Consumption: Power consumption is the most important consideration, especially as GPUs are used extensively and in more powerful configurations for complex cybersecurity tasks. Monitoring power usage ensures that the GPU performs optimally without overheating or consuming excessive energy. Proper management of power consumption can prevent thermal throttling and maintain the efficiency of the GPU​​.

Real-world Impact of Cybersecurity Solutions

Understanding how GPUs handle tasks in terms of throughput, latency, memory usage, and power consumption is key to designing effective real-world cybersecurity solutions as mentioned below:

Throughput

In practical terms, a GPU with high throughput enables cybersecurity systems to:

  • Detect and Respond to Threats Faster: High-throughput GPUs can quickly analyse traffic and data, leading to faster identification of potential threats. This speed is crucial in mitigating threats before they cause significant harm.

  • Handle Larger Datasets: Cybersecurity involves analysing vast amounts of data. High-throughput GPUs can process these large datasets more efficiently, making them ideal for AI-driven threat detection and analysis.

Latency

In cybersecurity, low latency is essential for immediate threat detection and response. The real-world impacts include:

  • Real-Time Threat Detection: Low-latency GPUs allow for quicker processing of data, which is essential for detecting and responding to threats in real-time. This speed can be the difference between stopping an attack in its tracks and suffering a major security breach.

  • Improved System Responsiveness: Systems that rely on GPUs with lower latency will be more responsive. This means that security measures, such as intrusion detection systems, can operate more effectively, with minimal delay in alerting or taking action.

Memory utilisation

Efficient memory utilisation helps in:

  • Handling Complex Tasks: Cybersecurity tasks often involve complex datasets and algorithms. Efficient memory utilisation allows for more sophisticated and intensive processing without running out of memory.

  • Enhanced Performance in Parallel Processing: Many cybersecurity applications require parallel processing capabilities. Effective memory use in GPUs ensures that these applications run smoothly and efficiently, which is particularly important for AI and machine learning models used in threat detection and analysis.

Power Consumption

The impact of power consumption in cybersecurity GPU usage includes:

  • Sustainability and Cost Efficiency: Lower power consumption means less energy is used, which not only reduces operational costs but is also better for the environment.

  • Preventing Overheating and Maintaining Efficiency: High power consumption can lead to overheating, which may reduce the efficiency and lifespan of a GPU. Proper management of power consumption ensures that GPUs run optimally, maintaining performance without the risk of thermal throttling.

Challenges with GPU Usage in Cybersecurity 

While beneficial for various tasks like complex calculations and pattern recognition, GPUs come with their own set of challenges such as:

  1. High Energy Consumption: GPUs are known for their high energy consumption, which can be a significant issue, especially in large-scale cybersecurity operations. The cost and environmental impact of running multiple GPUs can be substantial. However, we are 100% renewable powered to minimise sustainability concerns associated with high energy use.

  2. Scalability Issues: Integrating and scaling GPU resources to meet the dynamic and growing demands of cybersecurity can be challenging for on-premise implementations. It requires significant investment and expertise to ensure that the GPU infrastructure can scale effectively. But as a cloud-based service, we handle all aspects of scalability, allowing you to easily meet your growing needs.

  3. Compatibility and Integration Challenges: Not all cybersecurity software and tools are optimised for GPU processing. This lack of compatibility can lead to the underutilisation of GPU resources or necessitate additional development work to integrate GPUs into existing cybersecurity infrastructures.

  4. Cost of Investment: High-end GPUs are expensive, and the teams to manage on-premise cloud are arguably even more expensive. The initial investment for setting up a GPU-based cybersecurity system can be prohibitive for smaller organisations. The ongoing maintenance and upgrade costs add to the financial burden. We offer cost-effective off-premise cloud solutions, and a fully transparent on-demand pricing model with no hidden fees allowing you to effectively budget your workloads.

Strategies for Optimising GPU Usage in Cybersecurity

Optimising GPU usage in cybersecurity involves leveraging high parallel processing power to enhance the performance of security-related tasks. Here are five strategies to optimise GPU usage in cybersecurity:

  1. Parallel Processing for Large-Scale Data Analysis: Cybersecurity often involves analysing vast amounts of data to detect anomalies or malicious activities. GPUs are excellent for this task due to their ability to perform parallel processing. By distributing data analysis tasks across multiple GPU cores, you can significantly speed up the process of sifting through large datasets, such as network traffic logs or large-scale security event data.

  2. Efficient Algorithm Implementation: Not all algorithms benefit equally from GPU acceleration. Identify and implement inherently parallelisable algorithms, such as certain types of encryption/decryption algorithms, hash functions, or machine learning algorithms. 

  3. Machine Learning and AI for Threat Detection: Machine learning models, particularly those involving deep learning, can require significant computational resources, especially when training on large datasets. Utilising GPUs for training and running these models can greatly reduce the time required for model training and inference. This is particularly useful in cybersecurity for real-time threat detection, anomaly detection, and predictive analytics.

  4. Offloading Routine Tasks to GPUs: Identify routine, computationally intensive tasks that can be offloaded to GPUs. This could include tasks like pattern matching, regular expression evaluation in network traffic, or cryptographic calculations. By offloading these tasks from the CPU to the GPU, you can free up CPU resources for other tasks while benefiting from the speed of GPU processing.

  5. Optimised Resource Management and Scheduling: Efficiently manage GPU resources to maximise their utilisation. This includes optimising task scheduling to ensure that the GPU is kept busy with relevant tasks and minimising idle time. Techniques such as batch processing and prioritising tasks based on their urgency and importance can help in utilising the GPU effectively without leaving it underutilised.

Choosing the Right GPU for Cybersecurity

To meet your cybersecurity needs, it is essential to choose the right GPU. Here’s how you can find the right GPU based on these factors: 

  • Performance Requirements: Determine the specific tasks the GPU will perform in cybersecurity applications, such as deep learning, data analysis, or real-time threat detection. Higher computational tasks require GPUs with greater processing power, more cores, and higher memory bandwidth.

  • Memory Capacity: A GPU with ample memory is crucial for handling large datasets common in cybersecurity applications. Look for a GPU with high VRAM to efficiently process and analyse large volumes of data without performance bottlenecks.

  • Compatibility with Software and Tools: Ensure the GPU is compatible with key cybersecurity tools and software frameworks you plan to use, like TensorFlow, PyTorch, or specific intrusion detection systems. Some GPUs are optimised for certain platforms or libraries, enhancing performance and efficiency.

  • Thermal and Power Efficiency: Cybersecurity applications can run for extended periods, so consider the thermal design and power efficiency of the GPU. A GPU that operates cooler and uses less power reduces the risk of overheating and can lower operational costs in long-term deployments.

  • Future Scalability and Upgrade Path: Choose a GPU that allows for scalability. As cybersecurity threats evolve and data volumes grow, the ability to upgrade or integrate additional GPUs without overhauling the entire system ensures long-term viability and adaptability to new challenges.

How Hyperstack Meet Your Needs

Hyperstack offers a wide variety of GPU options, ensuring that you select the one that best fits your specific project needs. Apart from that, you also get:

  • Virtual Machine Customisation (VMs): We offer extensive customisation of VMs. This means you can adjust your environment precisely to your cybersecurity needs, ensuring the right balance of resources for maximum efficiency and productivity. You can read our documentation to learn how Hyperstack’s VM APIs can help you create and manage customised VMs tailored to your specific needs.

  • Optimised Network Architecture: Our network architecture is specifically optimised for GPU performance. This results in maximised efficiency for GPU-accelerated workloads, which is crucial for intensive cybersecurity tasks.

  • Robust API and User Experience: We promise a first-class API and a user-friendly experience, including one-click deployment options and role-based access control. This makes it easier to manage cybersecurity tasks efficiently and effectively.

  • Transparent Cloud GPU Pricing: We operate on a transparent pricing model with no hidden costs. This usage-based pricing means you pay only for the resources you consume, allowing for cost-effective scaling of your cybersecurity operations.

Conclusion 

While GPUs have great potential to improve cybersecurity, organisations need to identify the best ways to utilise them. The key considerations include how much faster tasks can be done, the impact on response time, power needs, and hardware compatibility. We recommend opting for flexible cloud solutions that can make adoption easier. This allows you to get started with cybersecurity measures without large upfront investments into on-premise hardware and software.

As your needs grow, your security should too. Hyperstack's scalable cloud infrastructure adapts seamlessly to your demands. Sign up today and say goodbye to security limitations.

 

FAQs

Is GPU important for cybersecurity?

Yes, GPUs play an important role in cybersecurity. Their parallel processing architecture can accelerate machine learning and advanced analytics models for threat detection and response. GPUs streamline tasks like analysing malware variants, evaluating network activity patterns, running complex encryption/decryption workloads, and training AI models on large datasets. This allows for essential security capabilities like zero-day threat detection, insider threat monitoring, forensic analysis, and more. 

Are there specific security challenges that GPU usage addresses?

GPU usage addresses specific security challenges, such as the need for swift pattern recognition, efficient encryption/decryption, and accelerated machine learning algorithms. Parallel computing on GPUs enables quicker analysis of vast datasets for cybersecurity systems to respond promptly to evolving threats.

Are there specific cybersecurity tools that leverage GPU capabilities?

Yes, NVIDIA’s Morpheus, for example, is a GPU-accelerated SDK that can inspect all network traffic in real-time, flag anomalies, and provide insights on these anomalies so that threats can be addressed quickly.

 

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