Disruption for financial gain

Financial exploitation is a typical motivator for DDoS attacks in Minecraft. Cybercriminals frequently demand ransom to stop their attacks. Server owners, especially those with lucrative private or public servers, may feel pressured to pay to restore normalcy. In some cases, bad actors intentionally disrupt competitors to draw players to their own servers, leveraging downtime for monetary advantage.

Services that offer DDoS attacks for hire make these attacks more accessible and widespread. These malicious services target Minecraft servers because the game is so popular, making it an attractive and easy option for attackers.

Player and server rivalries

Rivalries within the Minecraft ecosystem often escalate to DDoS attacks, driven by competition among players, servers, hosts, and businesses. Players may target opponents during tournaments to disrupt their gaming experience, hoping to secure prize money for themselves. Similarly, players on one server may initiate attacks to draw members to their server and harm the reputation of other servers. Beyond individual players, server hosts also engage in DDoS attacks to disrupt and induce outages for their rivals, subsequently attempting to poach their customers. On a bigger scale, local pirate servers may target gaming service providers entering new markets to harm their brand and hold onto market share. These rivalries highlight the competitive and occasionally antagonistic character of the Minecraft community, where the stakes frequently surpass in-game achievements.

Personal vendettas and retaliation

Personal conflicts can occasionally be the source of DDoS attacks in Minecraft. In these situations, servers are targeted in retribution by individual gamers or disgruntled former employees. These attacks are frequently the result of complaints about unsolved conflicts, bans, or disagreements over in-game behavior. Retaliation-driven DDoS events can cause significant disruption, although lower in scope than attacks with financial motivations.

Displaying technical mastery

Some attackers carry out DDoS attacks to showcase their abilities. Minecraft is a perfect testing ground because of its large player base and community-driven server infrastructure. Successful strikes that demonstrate their skills enhance reputations within some underground communities. Instead of being a means to an end, the act itself becomes a badge of honor for those involved.

Hacktivism

Hacktivists—people who employ hacking as a form of protest—occasionally target Minecraft servers to further their political or social goals. These attacks are meant to raise awareness of a subject rather than be driven by personal grievances or material gain. To promote their message, they might, for instance, assault servers that are thought to support unfair policies or practices. This would be an example of digital activism. Even though they are less frequent, these instances highlight the various reasons why DDoS attacks occur.

Data theft

Minecraft servers often hold significant user data, including email addresses, usernames, and sometimes even payment information. Malicious actors sometimes launch DDoS attacks as a smokescreen to divert server administrators’ attention from their attempts to breach the server and steal confidential information. This dual-purpose approach disrupts gameplay and poses significant risks to user privacy and security, making data theft one of the more insidious motives behind such attacks.

Securing the Minecraft ecosystem

DDoS attacks against Minecraft are motivated by various factors, including personal grudges, data theft, and financial gain. Every attack reveals wider cybersecurity threats, interferes with gameplay, and damages community trust. Understanding these motivations can help server owners take informed steps to secure their servers, but often, investing in reliable DDoS protection is the simplest and most effective way to guarantee that Minecraft remains a safe and enjoyable experience for players worldwide. By addressing the root causes and improving server resilience, stakeholders can mitigate the impact of such attacks and protect the integrity of the game.

Gcore offers robust, multi-layered security solutions designed to shield gaming communities from the ever-growing threat of DDoS attacks. Founded by gamers for gamers, Gcore understands the industry’s unique challenges. Our tools enable smooth gameplay and peace of mind for both server owners and players.

Want an in-depth look at how to secure your Minecraft servers?

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In this tutorial, we’ll show you exactly how to set up DeepSeek 70B using Ollama and a Web UI on Gcore Everywhere Inference. By the end, you’ll have a fully functional environment where you can easily interact with your custom LLM via a user-friendly interface. This process involves three simple steps: deploying Ollama, deploying the web UI, and configuring the web UI and connecting to Ollama.

Let’s get started!

Step 1: Deploy Ollama

1. Log in to Gcore Everywhere Inference and select Deploy Custom Model.

2. In the model image field, enter ollama/ollama.
3. Set the Port to 11434.

4. Under Pod Configuration, configure the following:
5. Select GPU-Optimized.
6. Choose a GPU type, such as 1×A100 or 1×H100.
7. Choose a region (e.g., Luxembourg-3).

6. Set an autoscaling policy or use the default settings.
7. Name your deployment (e.g., ollama).
8. Click Deploy model on the right side of the screen.

Once deployed, you’ll have an Ollama endpoint ready to serve your model.

Step 2: Deploy the Web UI for Ollama

1. Go back to the Gcore Everywhere Inference console and select Deploy Custom Model again.
2. In the Model Image field, enter ghcr.io/open-webui/open-webui:main.
3. Set the Port to 8080.

4. Under Pod Configuration, set:
   • CPU-Optimized.
   • Choose 4 vCPU / 16 GiB RAM.
5. Select the same region as before (e.g., Luxembourg-3).

6. Configure an autoscaling policy or use the default settings.
7. Name your deployment (e.g., webui).
8. Click Deploy model on the right side of the screen.

9. Once deployed, navigate to the Web UI endpoint from the Gcore Customer Portal.

Step 3: Configure the Web UI

1. From the Web UI endpoint and set up a username and password when prompted.

2. Log in and navigate to the admin panel.

3. Go to Settings → Connections → Disable the OpenAI API integration.
4. In the Ollama API field, enter the endpoint for your Ollama deployment. You can find this in the Gcore Customer Portal. It will look similar to this: https://<your-ollama-deployment>.ai.gcore.dev/.

5. Click Save to confirm your changes.

Step 4: Pull and Use DeepSeek 70B

1. Open the chat section in the Web UI.
2. In the Select a model field, type deepseek-r1:70b.
3. Click Pull to download the model.

4. Wait for the download to complete.
5. Once downloaded, select the model and start chatting!

Your AI environment is ready to explore

By following these steps, you’ve successfully deployed DeepSeek 70B on Gcore Everywhere Inference with Ollama. This setup provides a powerful and user-friendly environment for experimenting with LLMs, prototyping AI-driven features, or integrating advanced conversational AI into your applications.

Ready to unlock the full potential of AI? Gcore Everywhere Inference offers outstanding scalability, performance, and support, making it the perfect solution for developers and businesses working with advanced AI models. Dive deeper into our powerful tools and resources by exploring our AI blog and docs.

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This is where a content delivery network (CDN) comes in, operating in the background to help end users access digital content quickly, securely, and without interruption. In this article, we’ll explain how a CDN works to optimize the delivery of websites, applications, media, and other online content, even during high-traffic spikes and cyberattacks. If you’re new to CDNs, you might want to check out our introductory article first.

Key components of a CDN

A CDN is a network of interconnected servers that work together to optimize content delivery. These servers communicate to guarantee that data reaches users as quickly and efficiently as possible. The core of a CDN consists of globally distributed edge servers, also known as points of presence (PoPs):

• Origin server: The central server where website data is stored. Content is distributed from the origin to other servers in the CDN to improve availability and performance.
• Points of presence (PoPs): A globally distributed network of edge servers. PoPs store cached content—pre-saved copies of web pages, images, videos, and other assets. By serving cached content from the nearest PoP to the user, the CDN reduces the distance data needs to travel, improving load times and minimizing strain on the origin server. The more PoPs a network has, the faster content is served globally.

How a CDN delivers content

CDNs rely on edge servers to store content in a cache, enabling faster delivery to end users. The delivery process differs depending on whether the content is already cached or needs to be fetched from the origin server.

A cache hit occurs when the requested content is already stored on a CDN’s edge server. Here’s the process:

1. User requests content: When a user visits a website, their device sends a request to load the necessary content.
2. Closest edge server responds: The CDN routes the request to the nearest edge server to the user, minimizing travel time.
3. Content delivered: The edge server delivers the cached content directly to the user. This is faster because:
4. The distance between the user and the server is shorter.
5. The edge server has already optimized the content for delivery.

What happens during a cache miss?

A cache miss occurs when the requested content is not yet stored on the edge server. In this case, the CDN fetches the content from the origin server and then updates its cache:

1. User requests content: The process begins when a user’s device sends a request to load website content.
2. The closest server responds: As usual, the CDN routes the request to the nearest edge server.
3. Request to the origin server: If the content isn’t cached, the CDN fetches it from the origin server, which houses the original website data. The edge server then delivers it to the user.
4. Content cached on edge servers: After retrieving the content, the edge server stores a copy in its cache. This ensures that future requests for the same content can be delivered quickly without returning to the origin server.

Do you need a CDN?

Behind every fast, reliable website is a series of split-second processes working to optimize content delivery. A CDN caches content closer to users, balances traffic across multiple servers, and intelligently routes requests to deliver smooth performance. This reduces latency, prevents downtime, and strengthens security—all critical for businesses serving global audiences.

Whether you’re running an e-commerce platform, a streaming service, or a high-traffic website, a CDN ensures your content is delivered quickly, securely, and without interruption, no matter where your users are or how much demand your site experiences.

Take your website’s performance to the next level with Gcore CDN. Powered by a global network of over 180+ points of presence, our CDN enables lightning-fast content delivery, robust security, and unparalleled reliability. Don’t let slow load times or security risks hold you back. Contact our team today to learn how Gcore can elevate your online presence.

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This comprehensive guide covers commands and tools so you can easily calculate and analyze directory sizes in a Linux environment. We will guide you step-by-step through three methods: du, ncdu, and ls -la. They’re all effective and each offers different benefits.

What is a Linux directory?

A Linux directory is a special type of file that functions as a container for storing files and subdirectories. It plays a key role in organizing the Linux file system by creating a hierarchical structure. This arrangement simplifies file management, making it easier to locate, access, and organize related files. Directories are fundamental components that help ensure smooth system operations by maintaining order and facilitating seamless file access in Linux environments.

#1 Get Linux directory size using the du command

Using the du command, you can easily determine a directory’s size by displaying the disk space used by files and directories. The output can be customized to be presented in human-readable formats like kilobytes (KB), megabytes (MB), or gigabytes (GB).

Check the size of a specific directory in Linux

To get the size of a specific directory, open your terminal and type the following command:

du -sh /path/to/directory

In this command, replace /path/to/directory with the actual path of the directory you want to assess. The -s flag stands for “summary” and will only display the total size of the specified directory. The -h flag makes the output human-readable, showing sizes in a more understandable format.

Example: Here, we used the path /home/ubuntu/, where ubuntu is the name of our username directory. We used the du command to retrieve an output of 32K for this directory, indicating a size of 32 KB.

Check the size of all directories in Linux

To get the size of all files and directories within the current directory, use the following command:

sudo du -h /path/to/directory

Example: In this instance, we again used the path /home/ubuntu/, with ubuntu representing our username directory. Using the command du -h, we obtained an output listing all files and directories within that particular path.

#2 Get Linux directory size using ncdu

If you’re looking for a more interactive and feature-rich approach to exploring directory sizes, consider using the ncdu (NCurses Disk Usage) tool. ncdu provides a visual representation of disk usage and allows you to navigate through directories, view size details, and identify large files with ease.

For Debian or Ubuntu, use this command:

sudo apt-get install ncdu

Once installed, run ncdu followed by the path to the directory you want to analyze:

ncdu /path/to/directory

This will launch the ncdu interface, which shows a breakdown of file and subdirectory sizes. Use the arrow keys to navigate and explore various folders, and press q to exit the tool.

Example: Here’s a sample output of using the ncdu command to analyze the home directory. Simply enter the ncdu command and press Enter. The displayed output will look something like this:

#3 Get Linux directory size using 1s -1a

You can alternatively opt to use the ls command to list the files and directories within a directory. The options -l and -a modify the default behavior of ls as follows:

1. -l (long listing format)
   • Displays the detailed information for each file and directory
   • Shows file permissions, the number of links, owner, group, file size, the timestamp of the last modification, and the file/directory name
2. -a (all files)
   • Instructs ls to include all files, including hidden files and directories
   • Includes hidden files on Linux that typically have names beginning with a . (dot)

ls -la lists all files (including hidden ones) in long format, providing detailed information such as permissions, owner, group, size, and last modification time. This command is especially useful when you want to inspect file attributes or see hidden files and directories.

Example: When you enter ls -la command and press Enter, you will see an output similar to this:

Each line includes:

1. File type and permissions (e.g., drwxr-xr-x):
   • The first character indicates the file type
     • - for a regular file
     • d for a directory
     • l for a symbolic link
   • The next nine characters are permissions in groups of three (rwx):
     • r = read
     • w = write
     • x = execute
     • Permissions are shown for three classes of users: owner, group, and others.
2. Number of links (e.g., 2):
   • For regular files, this usually indicates the number of hard links
   • For directories, it often reflects subdirectory links (e.g., the . and .. entries)
3. Owner and group (e.g., user group)
4. File size (e.g., 4096 or 1045 bytes)
5. Modification date and time (e.g., Jan 7 09:34)
6. File name (e.g., .bashrc, notes.txt, Documents):
   • Files or directories that begin with a dot (.) are hidden (e.g., .bashrc)

Conclusion

That’s it! You can now determine the size of a directory in Linux. Measuring directory sizes is a crucial skill for efficient storage management. Whether you choose the straightforward du command, use the visual advantages of the ncdu tool, or opt for the versatility of ls -la, this expertise enhances your ability to uphold an organized and efficient Linux environment.

Looking to deploy Linux in the cloud? With Gcore Edge Cloud, you can choose from a wide range of pre-configured virtual machines suitable for Linux:

• Affordable shared compute resources starting from €3.2 per month
• Deploy across 50+ cloud regions with dedicated servers for low-latency applications
• Secure apps and data with DDoS protection, WAF, and encryption at no additional cost

Get started today

What is AI inference?

AI inference is the application phase of artificial intelligence. Once a model has been trained on large datasets, it shifts from “learning mode” to “doing mode”—providing predictions or decisions from new data inputs.

For example, an e-commerce platform with a model trained on purchasing behavior uses inference to personalize recommendations for each site visitor. Without re-training from scratch, the model quickly adapts to new browsing patterns and purchasing signals, offering instant, relevant suggestions.

By enabling actionable insights, inference is transforming how businesses and technologies function, empowering relevance and instant responsiveness in an increasingly data-driven world.

How does AI inference work? A practical guide

AI inference has four steps: data preparation, model loading, processing and prediction, and output generation.

#1 Data preparation

The first step involves transforming raw input—such as text, images, or numerical data—into a format that the AI model can process. For instance, customer feedback might be converted into numerical representations of words and patterns, or an image could be resized and normalized. Proper data preparation ensures that the AI model can effectively understand and analyze the input. For businesses, this means making sure that input data is clean, well-structured, and formatted according to the model’s requirements.

#2 Model loading

Once the input data is ready, the trained AI model is loaded into memory. This model, equipped with patterns and relationships learned during training, acts as the foundation for predictions and decisions.

Businesses must make sure that their infrastructure is capable of quickly loading and deploying AI models, especially during high-demand periods. We simplify this process by providing a high-performance platform with global scalability. Your models are loaded and operational in seconds, whether you’re using a custom model or an open-source one.

#3 Processing and prediction

In this step, the prepared data is passed through the model’s neural networks, which apply learned patterns to generate insights or predictions. For example, a customer service AI might analyze incoming messages to determine if they express satisfaction or frustration.

The speed and accuracy of this stage depend on access to low-latency infrastructure capable of handling complex calculations. Our edge inference solution means data processing happens close to the source, reducing latency and enabling real-time decision making.

#4 Output generation

The final stage translates the model’s mathematical outputs into meaningful insights, such as predictions, labels, or recommendations. These outputs must be integrated into business workflows or customer-facing applications in a way that’s easy to understand and actionable.

We help streamline this step by offering APIs and integration tools that allow businesses to seamlessly incorporate inference results into their operations, so outputs are accessible and actionable in real time.

A real-life example

Let’s look at how this works in practice. Consider a retail business implementing AI for inventory management. The system continuously:

1. Receives data from point-of-sale systems and warehouse scanners
2. Processes this information through trained AI models
3. Generates predictions about future inventory needs
4. Adjusts order quantities and timing automatically

All of this happens in milliseconds, making real-time decisions possible. However, the speed and efficiency depend on choosing the right infrastructure for your needs.

The technology stack behind inference

To make this process work smoothly, specialized computing infrastructure and software need to work together.

Computing infrastructure

Modern AI inference relies on specialized hardware designed to process mathematical operations quickly. While training AI models often requires expensive, high-powered graphics processors (GPUs), inference can run on more cost-effective hardware options:

• CPUs: Suitable for smaller-scale applications
• Edge devices: For processing data locally on smartphones or IoT devices or other hardware closer to the data source, resulting in low latency and better privacy.
• Cloud-based inference servers: Designed for handling large-scale operations, enabling centralized processing and flexible scaling.

When evaluating computing infrastructure for AI, businesses should prioritize solutions that address latency, scalability, and ease of use. Edge inference capabilities are essential for deploying models closer to end users, which optimizes performance globally even during peak demand. Flexible access to diverse hardware options like GPUs, CPUs, and advanced accelerators ensures adaptability, while user-friendly tools and automated scaling enable seamless management and consistent performance.

Software optimization

The efficiency of inference depends heavily on software optimization. When done right, software optimization ensures that AI applications are fast, responsive, and scalable, making them practical for real-world use.

Look for the following to identify a solution that reduces inference processing time and supports optimized results:

• Model compression and optimization: The computational load is reduced and inference occurs faster—without sacrificing accuracy.
• Workload distribution and automation: This means that resources are allocated efficiently and cost-effectively.
• Integration: Look for APIs and tools that connect seamlessly with existing business systems.

The future of AI inference

We anticipate three major trends for the future of AI inference.

First, we’re seeing a dramatic shift toward specialized AI accelerators and custom silicon. New chips are being developed and existing ones optimized specifically for inference workloads. These purpose-built processors are delivering significant improvements in both performance and energy efficiency compared to traditional GPUs. This specialization is making AI inference more cost-effective and environmentally sustainable, particularly for companies running large-scale operations.

The second major trend is the emergence of lightweight, efficient models designed specifically for inference. While large language models like GPT-4 showcase the potential of AI, many businesses are finding that smaller, task-specific models can deliver comparable or better results for their particular needs. These “small language models” (SLMs) and domain-adapted models are trained on focused datasets and optimized for specific tasks, making them more practical for real-world deployment. This approach is particularly valuable for edge computing scenarios where computing resources are limited.

Finally, the infrastructure for AI inference is becoming more sophisticated and accessible. Advanced orchestration tools are automating the complex process of model deployment, scaling, and monitoring. These platforms can automatically optimize model performance based on factors like latency requirements, cost constraints, and traffic patterns. This automation is making it possible for companies to deploy AI solutions without maintaining large specialized teams of ML engineers.

Dive into more of our predictions for AI inference in 2025 and beyond in our dedicated article.

Accelerate inference adoption for your business

AI inference is rapidly becoming a differentiator for businesses. By applying trained AI models to new data, companies can make instant predictions, automate decision-making, and optimize operations across industries. However, achieving these benefits depends on having the right infrastructure and expertise behind the scenes. This is where the choice of inference provider plays a critical role. The provider’s infrastructure determines latency, scalability, and overall efficiency, which directly affect business outcomes. A well-equipped provider allows businesses to maximize the value of their AI investments.

At Gcore, we are uniquely positioned to meet these needs with our edge inference solution. Leveraging a secure, global network of over 180 points of presence equipped with NVIDIA GPUs, we deliver ultra-fast, low-latency inference capabilities. Intuitively deploy and scale open-source or custom models on our powerful platform that accelerates AI adoption for a competitive edge in an increasingly AI-driven world.

Get a complimentary consultation about your AI inference needs

At Gcore, we’ve been expanding our model catalog to simplify AI model testing and deployment. As businesses scale their AI applications across various units, identifying the best model for specific tasks becomes critical. For example, some applications, like cancer screening, prioritize accuracy over latency. On the other hand, time-sensitive use cases like fraud detection demand rapid processing, while cost may drive decisions for lightweight applications like chatbot development.

This guide provides a comprehensive overview of the AI models supported on the Gcore platform, their characteristics, and their most effective use cases to help you choose the right model for your needs. Our inference solution also supports custom AI models.

Large language models (LLMs)

LLMs are foundational for applications requiring human-like understanding and generation of text, making them crucial for customer service, research, and educational tools. These models are versatile and cover a range of applications:

• Text generation (e.g., creative writing, content creation)
• Summarization
• Question answering
• Instruction following (specific to instruct-tuned models)
• Sentiment analysis
• Translation
• Code generation and debugging (if fine-tuned for programming tasks)

Models supported by Gcore

Gcore supports the following models for inference, available in the Gcore Customer Portal. Activate them at the click of a button.

Business applications

LLMs play a pivotal role in various business scenarios; choosing the right model will be primarily influenced by task complexity. For lightweight tasks like chatbot development and FAQ automation, models like Llama-3.2-1B-Instruct are highly effective. Medium complexity tasks, including document summarization and multilingual sentiment analysis, can leverage models like Llama-3.2-3B-Instruct and Qwen2.5-7B-Instruct. For high-performance needs like real-time customer service or healthcare diagnostics, models like LLaMA-Pro-8B and Mistral-Nemo-Instruct-2407 provide robust solutions. Complex, large-scale applications, like market forecasting and legal document synthesis, are ideally suited for advanced models like QwQ-32B-Preview. Additionally, specialized solutions for niche industries can benefit from Marco-o1’s unique capabilities.

Image generation

Image generation models empower industries like entertainment, advertising, and e-commerce to create engaging content that captures the audience’s attention. These models excel in producing creative and high-quality visuals. Key tasks include:

• Generating photorealistic images
• Artistic rendering (e.g., illustrations, concept art)
• Image enhancement (e.g., super-resolution, inpainting)
• Marketing and branding visuals

Models supported by Gcore

We currently support six models via the Gcore Customer Portal, or you can bring your own image generation model to our inference platform.

Business applications

In high-quality image generation, models like stable-diffusion-xl and stable-cascade are commonly employed for creating marketing visuals, concept art for gaming, and detailed e-commerce product visualizations. Real-time applications, such as AR/VR customizations and interactive customer tools, benefit from the speed of ByteDance/SDXL-Lightning and FLUX.1-schnell. FLUX.1-dev and stable-diffusion-3.5-large-turbo are excellent options for experimentation and development, allowing startups and enterprises to prototype generative AI workflows cost-effectively. Specialized use cases, such as ultra-high-quality visuals for luxury goods or architectural renders, also find tailored solutions with stable-cascade.

Speech recognition

Speech recognition models are essential for industries like media, healthcare, and education, where transcription accuracy and speed directly impact their efficacy. They facilitate:

• Accurate speech-to-text transcription
• Low-latency live audio conversion
• Multilingual speech processing and translation
• Automated note-taking and content creation

Models supported by Gcore

At Gcore, our inference service supports two Whisper models, as well as custom speech recognition models.

Business applications

Speech recognition technology supports a wide range of business functions, all requiring precision and accuracy, delivered at speed. For real-time transcription, whisper-large-v3-turbo is ideal for live captioning and speech analytics applications. High-accuracy tasks, including legal transcription, academic research, and multilingual content localization, leverage the advanced capabilities of whisper-large-v3. These models enable faster, more accurate workflows in sectors where precise audio-to-text conversion is crucial.

Multimodal models

By bridging text, image, and other data modalities, multimodel models unlock innovative solutions for industries requiring complex data analysis. These models integrate diverse data types for applications in:

• Image captioning
• Visual question answering
• Multilingual document processing
• Robotic vision

Models supported by Gcore

We currently support the following multimodal models:

Business applications

For tasks like image captioning and visual question answering, Pixtral-12B-2409 provides robust capabilities in generating descriptive text and answering questions based on visual content. Qwen2-VL-7B-Instruct supports document analysis and robotic vision, enabling systems to extract insights from documents or understand their physical surroundings. These applications are transformative for industries ranging from digital media to robotics.

A multitude of models, supported by Gcore

Start developing on the Gcore platform today, leveraging top-tier GPUs for seamless AI model training and deployment. Simplify large-scale, cross-regional AI operations with our inference-at-the-edge solutions, backed by over a decade of CDN expertise.

Get started with Inference at the Edge today

How do DDoS attacks work?

DDoS attacks work by overwhelming and flooding a company’s resources so that legitimate users cannot get through. The attacker creates huge amounts of malicious traffic by creating a botnet, a collection of compromised devices that work together to carry out the attack without the device owners’ knowledge. The attacker, referred to as the botmaster, sends instructions to the botnet in order to implement the attack. The attacker forces these bots to send an enormous amount of internet traffic to a victim’s resource. As a result, the server can’t process real users trying to access the website or app. This causes customer dissatisfaction and frustration, lost revenue, and reputational damage for companies.

Think of it this way: Imagine a vast call center. Someone dials the number but gets a busy tone. This is because a single spammer has made thousands of automated calls from different phones. The call center’s lines are overloaded, and the legitimate callers cannot get through.

DDoS attacks work similarly, but online: The fraudster’s activity completely blocks the end users from reaching the website or online service.

Different types of DDoS attacks

There are three categories of DDoS attacks, each attacking a different network communication layer. These layers come from the OSI (Open Systems Interconnection) model, the foundational framework for network communication that describes how different systems and devices connect and communicate. This model has seven layers. DDoS attacks seek to exploit vulnerabilities across three of them: L3, L4, and L7.

While all three types of attacks have the same end goal, they differ in how they work and which online resources they target. L3 and L4 DDoS attacks target servers and infrastructure, while L7 attacks affect the app itself.

• Volumetric attacks (L3) overwhelm the network equipment, bandwidth, or server with a high volume of traffic.
• Connection protocol attacks (L4) target the resources of a network-based service, like website firewalls or server operating systems.
• Application layer attacks (L7) overwhelm the network layer, where the application operates with many malicious requests, which leads to application failure.

1. Volumetric attacks (L3)

L3, or volumetric, DDoS attacks are the most common form of DDoS attack. They work by flooding internal networks with malicious traffic, aiming to exhaust bandwidth and disrupt the connection between the target network or service and the internet. By exploiting key communication protocols, attackers send massive amounts of traffic, often with spoofed IP addresses, to overwhelm the victim’s network. As the network equipment strains to process this influx of data, legitimate requests are delayed or dropped, leading to service degradation or even complete network failure.

2. Connection protocol attacks (L4)

Protocol attacks occur when attackers send connection requests from multiple IP addresses to target server open ports. One common tactic is a SYN flood, where attackers initiate connections without completing them. This forces the server to allocate resources to these unfinished sessions, quickly leading to resource exhaustion. As these fake requests consume the server’s CPU and memory, legitimate traffic is unable to get through. Firewalls and load balancers managing incoming traffic can also be overwhelmed, resulting in service outages.

3. Application layer attacks (L7)

Application layer attacks strike at the L7 layer, where applications operate. Web applications handle everything from simple static websites to complex platforms like e-commerce sites, social media networks, and SaaS solutions. In an L7 attack, a hacker deploys multiple bots or machines to repeatedly request the same resource until the server becomes overwhelmed.

By mimicking genuine user behavior, attackers flood the web application with seemingly legitimate requests, often at high rates. For example, they might repeatedly submit incorrect login credentials or overload the search function by continuously searching for products. As the server consumes its resources managing these fake requests, genuine users experience slow response times or may be completely denied access to the application.

How can DDoS attacks be prevented?

To stay one step ahead of attackers, use a DDoS protection solution to protect your web resources. A mitigation solution detects and blocks harmful DDoS traffic sent by attackers, keeping your servers and applications safe and functional. If an attacker targets your server, your legitimate users won’t notice any change—even during a considerable attack—because the protection solution will allow safe traffic while identifying and blocking malicious requests.

DDoS protection providers also give you reports on attempted DDoS attacks. This way, you can track when the attack happened, as well as the size and scale of the attack. This enables you to respond effectively, analyze the potential implications of the attack, and implement risk management strategies to mitigate future disruptions.

Repel DDoS attacks with Gcore

At Gcore, we offer robust and proven security solutions to protect your business from DDoS attacks. Gcore DDoS Protection provides comprehensive mitigation at L3, L4, and L7 for websites, apps, and servers. We also offer L7 protection as part of Gcore WAAP, which keeps your web apps and APIs secure against a range of modern threats using AI-enabled threat detection.

Take a look at our recent Radar report to learn more about the latest DDoS attack trends and the changing strategies and patterns of cyberattacks.

Read our DDoS Attack Trends Radar report

A CDN is a globally distributed network of servers that work together to deliver content to users quickly, minimizing latency. Instead of relying on a single server, a CDN uses edge servers—called points of presence (PoPs)—to cache or temporarily store copies of your content closer to the user. This optimizes website performance, drastically cuts down on load times, and improves the user experience. Research suggests that a one-second lag in page loading speed can significantly decrease engagement, citing a 7% decline in conversions and an 11% decrease in page visits. CDNs considerably speed up load times by reducing latency through content caching closer to the user. By splitting up your website’s traffic over several servers, CDNs also protect it from online threats. Distributed denial-of-service (DDoS) attacks are lessened by CDNs because they spread traffic among a network of servers, improving security and availability. 

What Challenges Do CDNs Address? 

CDNs tackle two key challenges to improve website and application performance: 

• Slow load times: Users sometimes experience frustratingly slow-loading websites and applications. This is because data must travel from a server to the end user’s device, causing latency. CDNs move servers closer to end users, reducing the distance that data has to travel and speeding up load times.  
• High traffic volumes: High traffic volumes during peak times or cyberattacks can overwhelm your website and lead to latency or site unavailability. Since CDNs distribute traffic across multiple servers, no single server is overwhelmed. This helps prevent crashes and delivers smooth performance for all users.

Common Use Cases for CDNs 

CDNs are vital across a range of industries, providing measurable improvements in content delivery and user experience. 

• E-commerce websites use CDNs to guarantee quick page loading and frictionless shopping experiences, even during periods of high traffic. Speed is crucial for online businesses. A study found that the average cost of downtime for e-commerce websites is around $500,000 per hour. This includes lost sales, operational costs, and long-term damage to brand reputation 
• Streaming services rely on CDNs to deliver high-quality video content while minimizing buffering. Netflix states that its CDN contributes to the daily delivery of over 125 million hours of streaming content, guaranteeing a seamless experience for customers worldwide. 
• Gaming companies use CDNs to lower latency and provide a consistent real-time user experience, especially during live multiplayer matches, where it is essential to preserve an engaging and fair gameplay experience. 
• News outlets and blogs benefit from CDNs by ensuring their content loads quickly for readers around the world, during large-scale traffic surges, especially during major events like elections or breaking news.  

The Benefits of a CDN 

Faster Website Performance 

Every second counts when delivering content online. Slow websites frustrate users and harm your business. CDNs speed up content delivery by caching data closer to users, reducing page and file load times. 

Whether you’re delivering static content (such as CSS, HTML or JPG files) or dynamic content (like data generated by user interactions or API calls), a CDN ensures optimal performance regardless of user location. While factors like DNS settings, server configurations, and code optimization all play a role, the physical distance between your origin server and your users is a factor that only a CDN can solve. 

Increased Availability and Reliability 

Downtime can seriously affect online businesses. Hardware failures, traffic surges, and cyberattacks can reduce your website’s availability, harming your customers’ experience and causing financial or reputational damage. In fact, around 98% of organizations report that just one hour of downtime costs over $100,000. 

A CDN ensures that your website remains available, fast, and reliable by leveraging essential features such as: 

• Load balancing: This process dynamically distributes traffic across multiple servers to optimize performance and prevent overload.
• Intelligent failover: Automatically redirects traffic if a server goes offline, ensuring continuity with minimal disruption.
• Anycast routing: Directs users to the closest or most efficient server, further reducing latency and enhancing response times.

Security Features 

As cyber threats continue to grow in sophistication and frequency, securing your website or application is more critical than ever. According to recent statistics from Cobalt’s 2024 Cybersecurity Report, weekly attacks worldwide increased by 8% in 2023, while attackers used more sophisticated strategies to exploit vulnerabilities. Strong security measures that not only safeguard your website but also guarantee optimal performance are necessary in light of these evolving threats. 

CDN security features not only improve website performance but also defend against a wide range of attacks by distributing traffic across multiple servers, which mitigates DDoS attacks and filters out malicious traffic before it reaches your website. These features, from DDoS protection to safeguarding APIs, help maintain uptime, protect sensitive data, and guarantee a seamless user experience. Most modern solutions like Gcore CDN integrate robust security measures into content delivery, such as: 

• SSL/TLS encryption facilitates secure data transmission by encrypting traffic, protecting sensitive information from being intercepted.
• L3/L4 DDoS protection blocks large-scale cyberattacks designed to flood your network and disrupt services. 
• L7 DDoS protection guards your website from more complex attacks targeting how the website functions, helping it continue to operate smoothly. 
• Web application firewall (WAF) acts as a shield, blocking harmful traffic such as hacking attempts or malicious scripts before they can affect your site. 
• API security protects the communication between your application and other software, preventing unauthorized access or data theft.
• Bot protection identifies harmful automated traffic (bots), preventing activities like data scraping or login attempts with stolen credentials while allowing useful bots (like search engine crawlers) to function normally. 

Elevate Your Online Experience With a CDN 

A CDN is no longer a luxury—it’s a necessity for businesses that want to deliver fast, reliable, and secure online experiences. Whether your goal is to optimize performance, manage high traffic, or protect your site from attacks, a well-configured CDN makes all the difference. 

Ready to enhance your website’s performance? Our futureproof CDN runs on a global network of over 180 points of presence, so your customers get outstanding performance no matter where in the world they’re located. Get in touch with our team today to learn how our CDN can benefit your business. 

Discover Gcore CDN

Gcore Inference at the Edge makes it simple to deploy and manage custom models efficiently, giving you the ability to deploy and scale your favorite Hugging Face models globally in just a few clicks. In this guide, we’ll walk you through how easy it is to harness the power of Gcore’s edge AI infrastructure to deploy a Hugging Face Space model. Whether you’re developing NLP solutions or cutting-edge computer vision applications, deploying at the edge has never been simpler—or more powerful. 

Step 1: Log In to the Gcore Customer Portal 

Go to gcore.com and log in to the Gcore Customer Portal. If you don’t yet have an account, go ahead and create one—it’s free. 

Step 2: Go to Inference at the Edge 

In the Gcore Customer Portal, click Inference at the Edge from the left navigation menu. Then click Deploy custom model. 

Step 3: Choose a Hugging Face Model 

Open huggingface.com and browse the available models. Select the model you want to deploy. Navigate to the corresponding Hugging Face Space for the model. 

Click on Files in the Space and locate the Docker option. 

Copy the Docker image link and startup command from Hugging Face Space. 

Step 4: Deploy the Model on Gcore 

Return to the Gcore Customer Portal deployment page and enter the following details: 

• Model image URL: registry.hf.space/ethux-mistral-pixtral-demo:latest 
• Startup command: python app.py 
• Container port: 7860 

Configure the pod as follows: 

• GPU-optimized: 1x L40S 
• vCPUs: 16 
• RAM: 232GiB 

For optimal performance, choose any available region for routing placement. Name your deployment and click Deploy.

Step 5: Interact with Your Model 

Once the model is up and running, you’ll be provided with an endpoint. You can now interact with the model via this endpoint to test and use your deployed model at the edge.

Powerful, Simple AI Deployment with Gcore 

Gcore Inference at the Edge is the future of AI deployment, combining the ease of Hugging Face integration with the robust infrastructure needed for real-time, scalable, and global solutions. By leveraging edge computing, you can optimize model performance and simultaneously futureproof your business in a world that increasingly demands fast, secure, and localized AI applications. 

Deploying models to the edge allows you to capitalize on real-time insights, improve customer experiences, and outpace your competitors. Whether you’re leading a team of developers or spearheading a new AI initiative, Gcore Inference at the Edge offers the tools you need to innovate at the speed of tomorrow. 

Explore Gcore Inference at the Edge

Step 1: Get Links to Your Videos

First, obtain links to your videos and download them. Look for your provider in the list below, or refer to the general SFTP/S3 storage section if applicable. After completing the steps for your provider, go straight to step 2.

Google Drive

1. Share the file: Open Google Drive and locate the MP4 file you want to download. Right-click on the file and select “Share.”
2. Get the shareable link: In the sharing settings, click “Get link.” Ensure the link-sharing option is turned on.
3. Set sharing permissions: Adjust the sharing permissions so “Anyone with the link” can view or download the file. Copy the generated link.

Amazon S3

1. Edit S3 block public access settings: Go to the S3 management console, select the bucket containing your MP4 file, and edit the Block Public Access settings if necessary.
2. Add a bucket policy: Implement a bucket policy that grants public read access to your files.
3. Get the list of objects: Navigate to the Objects tab, find your MP4 file, and click on the file to obtain the Object URL, which will be your download link.

Vimeo

1. Access the video: Log in to your Vimeo account and go to the video you wish to download.
2. Select options: Click on the “Settings” button (gear icon) below the video player.
3. Get video file link: In the settings menu, go to the “Video File” tab, where you can find the download link for your MP4 file.

MUX

1. Enable master access: Log in to your MUX account, navigate to the video asset, and enable master access if it’s not already enabled.
2. Retrieve URL to master: Once master access is enabled, the URL to the master file will be available in the video asset details. Copy this URL for downloading the file.

Dropbox

1. Create a shareable link: Log in to your Dropbox account and locate the MP4 file you want to share. Click on the “Share” button next to the file.
2. Set access permissions: In the sharing settings, create a link and set the permissions to “Anyone with the link.” Copy the generated link to download the file.

General SFTP or S3 Storage

1. Access storage: Log in to your SFTP or S3 storage service control panel.
2. Manage buckets/directories: Navigate to the appropriate bucket or directory containing your MP4 files.
3. Retrieve download links: Generate HTTP/S links for the files you want to download. You can then use these links to download the files directly.

Step 2: Check Availability to Download

Ensure that your video files are available and ready for download, preventing any interruptions or issues during the migration process.

1. Open HTTP/S link in a browser: Copy the HTTP/S link for the MP4 file and paste it into your browser’s address bar. Press Enter to navigate to the link.
2. Check the video plays correctly in the browser: Verify that the video starts playing once the link is opened. This step ensures that the file is accessible and the link is functioning properly.
3. Right-click to download: While the video is playing, right-click on the video player. Select “Save video as…” from the context menu. Choose a destination on your local disk to save the MP4 file.

Step 3: Upload to Gcore Video Streaming

No matter which provider you’re migrating from, you need to upload your videos to Gcore Video Streaming storage. There are three primary methods to upload videos to Gcore storage:

1. Copy from external storage: If your videos are available via public HTTPS URLs, you can directly copy the video files from external storage to Gcore. This method efficiently transfers files without downloading them to your local device first.
2. Upload from a local device: Videos can be uploaded from your local host, backend, browser, or mobile app using the TUS resumable upload protocol. This method is resilient to interruptions, ensuring a smooth upload process by resuming from the point of failure.
3. Batch upload: This method will soon be available to migrate extensive collections of videos, allowing you to transfer vast numbers of video files efficiently.

The simplest migration option is to obtain video URLs and copy them to Gcore Video Hosting, eliminating the need to download and reupload videos.

Example API Request to Copy Video from External Storage

To copy a video from another server, specify the origin_url attribute in the POST API request. The original video will be downloaded for video hosting on our server. Here is an example of the API request to set a task for copying a video from external storage:

curl -L 'https://api.gcore.com/streaming/videos/' \
-H 'Content-Type: application/json' \
-H 'Authorization: APIKey 1234$0d16599c' \
-d '{ 
  "video": { 
    "name": "Gcore Demo", 
    "description": "Video copied from an external S3 Storage", 
    "origin_url": "https://s-ed1.cloud.gcore.lu/demo-video/gcore.mp4" 
  } 
}

Refer to the complete documentation for detailed steps and examples of API requests. The original file must be in MP4 format or one of the following formats: 3g2, 3gp, asf, avi, dif, dv, flv, f4v, m4v, mov, mp4, mpeg, mpg, mts, m2t, m2ts, qt, wmv, vob, mkv, ogv, webm, vob, ogg, mxf, quicktime, x-ms-wmv, mpeg-tts, vnd.dlna.mpeg-tts. Streaming formats like HLS (.m3u8/.ts) and DASH (.mpd/.m4v) are intended for final video distribution and cannot be used as original file formats. Here are examples of good and bad links:

• Good link: https://demo-files.gvideo.io/gcore.mp4
• Bad link (chunked HLS format): https://demo-files.gvideo.io/hls/master.m3u8

Note: Currently, only one video can be uploaded per request, so transferring your library in batches will require automation.

Migrate to Gcore Video Streaming Today

Gcore Video Streaming makes video migration easy with support for multiple sources and automatic transcoding. Whether you’re moving files from cloud storage, hosting platforms, or API-based services, Gcore streamlines video administration. Store, process, and distribute videos in various formats, complete with features like subtitles and timeline previews.

With seamless migration and automatic transcoding, Gcore ensures your videos are optimized and ready for distribution, saving you time and effort. Simplify your video management and ensure your content is always accessible and in the best format for your audience with Gcore’s robust video streaming solutions.