How AI is Transforming the Battle Against DDoS Attacks

How to Use AI to Stop DDoS Attacks

By Tyler Treat, Director of Security Architecture

The Bad Guys Use AI: Shouldn’t You?

By debilitating IT infrastructures, DDoS attacks (Distributed Denial of Service) produce big headaches. With advances in artificial intelligence (AI), the bad guys can now strike more easily than ever.

If you don’t protect your server infrastructure properly, you will present an easy target. AI tools can quickly search for and exploit digital assets and hunt relentlessly until they find vulnerabilities.

However, you can use AI, too, to bolster your security posture and defend those same digital assets. Read on the learn how.

What Is a DDoS attack?

DDoS occurs when attackers commandeer a sizable network or pool of compromised devices to overwhelm systems, network devices, websites, or applications. For example, if a company runs a vulnerable version of a web server hosting its website, DDoS can prevent that server from responding to traffic from legitimate users.

The technique floods digital assets with malicious traffic from multiple sources: thousands or even hundreds of thousands of compromised devices known as a botnet. Unlike a denial-of-service attack that originates from a single source, DDoS is more difficult to detect, block, and trace. The attacks appear to originate from many sources.

By degrading service availability, DDoS disrupts business operations and serves as a smokescreen for other malicious activities. Sometimes, a specific service on a device may be vulnerable. If the DDoS attack hits hard enough, it can shut down the entire supporting network infrastructure.

As attacks cripple critical digital infrastructure, this leads to service outages and loss of revenue. Customers, employees, and business partners can’t access the impacted target. The volume of attack traffic also consumes internal resources and distracts security teams from addressing other threats.

Who Originates DDoS Attacks?

Traditional defenses against DDoS attacks rely on a combination of on-premises mitigation appliances, ISP-level traffic filtering, rate limiting, and cloud-based scrubbing services. These methods try to identify and block malicious traffic before it reaches critical infrastructure.

However, static rules and signature-based detection struggle to keep pace with modern, adaptive DDoS campaigns. Since DDoS attacks blend in with legitimate traffic and target application-level services, they can be difficult for legacy security tools to detect.

For enterprises that deliver high-availability applications — e-commerce platforms, SaaS providers, and colocation data centers — DDoS attacks don’t just create technical challenges. They also erode customer trust and can generate substantial financial penalties tied to customer SLAs and compliance requirements.

The person launching a DDoS attack may be politically motivated, sponsored by a nation state, or it could be a lone wolf with an axe to grind. Online services now allow people to pay in bitcoin for DDoS time and simply point an attack at a target he or she wants to hit for a specified amount of time.

DDoS activity will likely ramp up as AI tools become more mainstream. The bad guys no longer need to hire a programmer on the Dark Web who knows how to find weaknesses. AI does all the work much faster, scanning public IP addresses to find targets with vulnerabilities.

How AI Helps Defend Against DDoS Attacks

Just as cybercriminals leverage AI to launch DDoS attacks, security teams can do the same to stop DDoS attacks. AI excels at identifying subtle deviations in network behavior, enabling more precise and proactive mitigation and finding vulnerabilities missed by traditional definition-based scanners.

In addition, AI machine learning algorithms can analyze massive volumes of traffic in real time to detect anomalies that indicate DDoS events, often before traditional tools raise alerts. When integrated into security platforms, AI can then adapt to meet evolving threats. This includes reducing false positives by learning the normal traffic patterns unique to each enterprise environment.

Another plus comes from DDoS mitigation platforms that apply AI to their detection and response processes. Rather than a security engineer watching logs flow by looking for alarms, programmatic responses identify malicious traffic and then apply automated remediation measures.

This capability is key since AI can iterate attacks to keep looking for weaknesses. When security measures block an attack, AI just keeps changing identifiers.

With AI built into the mitigation platform, enterprises can monitor for new attacks and respond just as quickly to what comes next. AI acts a force-multiplier for security professionals just as it does for the bad guys.

Best Practices for Insulating Digital Assets

• Run customer-facing applications on content delivery networks.
• Identify high-value assets and critical services that require the most protection.
• Implement layered defense measures.
• Use AI tools that support behavior-based analytics, anomaly detection, and automated response.
• Leverage high-quality telemetry to prioritize data hygiene and centralize logging.
• Brief the InfoSec team on the decision-making process of AI tools so they trust the outputs.

Collaborating with Colocation Partners

Colocation providers offering managed security services can better protect customers targeted by DDoS attacks. The leading providers layer defenses, such as placing web servers behind load balancers with proper configurations and verifying if applications have quality web code to remove risks that could be exploited. They also offer visibility at the physical and network edge, where AI-driven detection is most effective.

When setting up a new colocation environment, collaborate with your partner to ensure integration between AI tools and the infrastructure. This includes shared telemetry feeds, synchronized incident response plans, and access to edge-layer data, such as NetFlow, syslogs, and physical access logs. This joint architecture approach enables both parties to act early on threat indicators and build a more resilient defense posture.

Another key strategy with which colocation providers can assist customers is routing Internet traffic through a content delivery network (CDN). These networks block anything going to resources at a colocation data center that’s not legitimate. A CDN can take the hit because the hosting company has a massive network and advanced scrubbing technology.

Managed Mitigation Services Block DDoS Attacks in Real Time

Here at DataBank, our colocation facilities provide managed mitigation services that eliminate DDoS attacks in real time, including stealthy sub-saturating attacks and volumetric attacks at layers 3 through 7 for both IPv4 and IPv6 traffic. Just as importantly, our service allows friendly user traffic to flow uninterrupted, even while under attack.

The DataBank DDoS Mitigation Process 

As a managed services partner, we orchestrate incident response, checking if customer networks block DDoS attacks effectively, and blackholing any attacking IP addresses at upstream carriers. We analyze the style of attack as it goes through the response phases—from detection to eradication and root cause analysis.

We also offer intrusion detection and response services and apply multiple layers of defense. In addition to physical security controls, these include network perimeter controls, access controls, and host system controls. We monitor all these layers continuously to not only protect individual customer infrastructures within our colocation facilities, but also our collective customer ecosystems.