How to Train AI Dragons to Solve Network Security Problems
AI is becoming a game changer in network security, but it’s not always easy to decide which type of AI to use — generative, predictive or a mix of both. In the fast-evolving landscape of cyberthreats, and with distributed denial of service (DDoS) attacks in particular, the right AI can make all the difference.
Let’s explore how AI can help service providers and cloud builders keep their networks secure and why “feeding your AI dragons” with relevant, high-quality data is essential for implementing AI for DDoS security.
Which AI to Use for Network Security?
Most of us probably know the differences between generative AI (GenAI) and predictive AI. GenAI is primarily used to generate new content, such as text, images, videos, music or combined media, that looks and feels like it was made by humans. Predictive AI is used when we need to make predictions or forecasts about future outcomes based on observed historical data.
As for which one to use for network security, the answer is the usual, “it depends.” Both AI approaches apply to network security, but they solve different problems.
Generative AI for DDoS Security
GenAI’s ability to take a vast data set as input and generate human-centric output can make it an indispensable tool for improving network security operations. The technology can excel at tasks that speed up, improve and automate human interactions with network-related data. It can also enhance security management and reporting by streamlining operational processes and reducing the potential for human error.
For example, GenAI can help correlate threat alerts and alarms, while automating incident response. Large language models (LLMs) can report on detected threats and attacks in a simple, actionable format. When a threat is detected, GenAI can investigate its potential impact across many related systems and trigger alerts across different security domains and tools. This gives network and security operations teams a holistic and precise picture of what’s happening and helps them respond better and faster.
GenAI can also provide process-focused recommendations for proactive network defenses. This helps security teams streamline their processes and avoid mistakes.
Predictive AI for DDoS Security
While GenAI enhances human involvement, it’s the predictive AI that can be the true workhorse for identifying and mitigating DDoS attacks.
Predictive AI focuses on analyzing historical data and current traffic profiles to detect threat patterns and warn about imminent threats. This allows security systems and teams to respond to DDoS attacks automatically and neutralize them before they do significant damage. The key to success lies in using high-quality data to train the models.
The DDoS Problem: Distinguishing Bad From Good traffic in Real-Time
The frequency, scale and sophistication of DDoS attacks have grown exponentially over the last several years. Bad actors are making greater use of botnets — large sets of insecure IoT devices or compromised systems — to launch massive attacks. And many DDoS attacks now seem to have some automation and AI behind them.
Fighting DDoS is all about identifying malicious traffic that aims to disrupt connectivity or bring down network infrastructure while letting legitimate traffic flow through. To achieve this, DDoS detection and mitigation need to be done in real-time, all the time.
So, how do we distinguish between bad and good network traffic?
Network security teams have traditionally relied on manual traffic monitoring, observing and tracking traffic baselines in peacetime, when there are no attacks. This legacy approach involves defining many thresholds specific to network protocols and interfaces. A new game release or a sudden traffic shift from one transit provider to another could see many of these thresholds crossed, resulting in false alarms (caused by false positives—good traffic identified as DDoS). Worse, new DDoS attacks originating from never-before-seen IP addresses and using novel techniques with short, fast-changing vectors would go undetected (false negatives).
With today’s traffic volumes and complexity, we need DDoS detection that is more automated, making it much smarter and faster. Predictive AI can help.
Good-Quality Data is Essential
We all know AI’s mantra: More data, faster processing, large models and you’re off to the races. But what if a problem is so specific — like network or DDoS security — that it doesn’t have a lot of publicly or privately available data you can use to solve it?
As with other AI applications, the quality of the data you feed an AI-based DDoS defense system determines the accuracy and effectiveness of its solutions. To train your AI dragon to defend against DDoS attacks, you need detailed, real-world DDoS traffic data. Since this data is not widely and publicly available, your best option is to work with experts who have access to this data or, even better, have analyzed and used it to train their own AI dragons.
To ensure effective DDoS detection, look at real-world, network-specific data and global trends as they apply to the network you want to protect. This global perspective adds valuable context that makes it easier to detect emerging or worldwide threats.
Data from the field matters most. A good, comprehensive data set on DDoS attacks should provide a large sample of attacks happening in the real world, from traditional amplification/reflection attacks to new DDoS attacks that use botnets and automation.
This DDoS knowledge base must include a range of attack-related data points, encompassing all relevant and diverse attack vectors along with a wide range of additional parameters such as IP flows, detailed series data, TCP flags and time-to-live (TTL) values. All this data will allow AI models to learn about DDoS attacks and help DDoS detection systems differentiate between legitimate traffic spikes and malicious DDoS traffic.
AI Models Matter, Too
Predictive AI models shine when it comes to detecting DDoS patterns in real-time. By using machine learning techniques such as time-series analysis, classification and regression, they can recognize patterns of attacks that might be invisible to human analysts. If specific IP traffic parameters exhibit unusual timing or behavior (e.g., sudden surges in traffic originating from millions of IoT devices) for example, predictive AI can flag it as potential DDoS activity.
As mentioned, one of the big challenges of DDoS detection is understanding what isn’t a DDoS attack. Legitimate network traffic can sometimes exhibit strange patterns, such as a flood of traffic when a game developer releases a highly anticipated title.
So, what does real-world DDoS traffic look like? Most attacks still fall into three categories: amplification, reflection and botnet-based flooding. But application-layer attacks are becoming increasingly common.
AI must learn to recognize the signs of all these attacks and differentiate them from regular traffic. A botnet attack, for instance, typically results in distributed attack traffic that originates from many compromised IoT devices. This is where information from the larger internet context is essential. A given attack might be new to the network being targeted. However, understanding that some of the attack’s source IP addresses are “repeat offenders” might help the system flag this traffic as DDoS.
Once your AI systems reliably identify these behaviors, they can be fine-tuned for better performance (lower percentage of false positives and negatives), speed and accuracy, and trained using new data from the field. To effectively defend against future generations of DDoS attacks, your AI system needs to adapt, learn and evolve. Powerful AI models must be continually trained using new data from the network and the internet so they can quickly and accurately defend against new attack techniques.
Gathering data and training your AI models is a good start. Once a model is trained, you need to ensure that it is sustainable. A system that depends on constant data updates and validation will stay sharp and effective against emerging threats.
AI models must be rigorously tested and validated to ensure that they will continuously perform when their inferencing scales up (i.e., make accurate decisions). To test an AI model’s performance, we can again turn to large DDoS data sets, now using them to extensively test DDoS detection and mitigation capabilities based on data from thousands of real-world DDoS attacks. By analyzing the nuances of DDoS attack vectors, such as IP header manipulation and parameter variance, we can fine-tune AI models for more effective DDoS defense.
Towards AI-Powered DDoS Defense
As the frequency and scale of DDoS attacks continue to grow, traditional security methods are increasingly inadequate. The complexity of the threat landscape demands smarter, faster and more automated solutions. Predictive AI is at the heart of these advancements because it enables real-time threat detection and response.
With vast amounts of real-world data and the power of predictive AI at our disposal, we’re entering a new era of network security. DDoS attacks may be evolving, but with AI, so is our ability to protect networks from them. The future of network security is not just about reacting to threats—it’s about detecting them as they appear and mitigating them before they can inflict damage on network infrastructure, services and users.
By embracing AI-based solutions for DDoS security, organizations can move beyond the limits of legacy DDoS protection approaches into a new age of proactive, data-driven defense. It’s time to train your AI dragon, feed it with the best data on DDoS you have—and watch it protect your network like never before.