How AI and IoT are Supercharging the DDoS Threat

The surge in DDoS attack traffic this year has been driven in part by the rapid expansion of IoT devices - from smart watches and home appliances to cars, hundreds of millions of new devices are joining the global internet. Many of these new devices feature poor security and are easily added to attacker's pool of botnets.

It is true that the DDoS threat grows alongside internet expansion. But the relationship isn't linear. The true catalyst behind this surge lies in the mass availability of botnet-for-hire platforms and low-barrier attack tools.

Meanwhile, the number of high-value targets – such as financial institutions, governments, and critical infrastructure – remains relatively fixed. The result is a growing imbalance, in which more attackers are armed with more tools - targeting the same essential services with increasing frequency and complexity.

How AI Makes DDoS More Dangerous

AI and machine learning are impacting the evolution of DDoS strategies and tactics. Threat actors are already experimenting with AI in order to:

• Automate reconnaissance
• Optimize botnet efficiency
• Dynamically shift attack patterns to avoid detection
• Leverage Generative Adversarial Networks (GANs) to generate traffic that mimics legitimate behavior

We have yet to see large-scale, AI-driven DDoS campaigns; but the groundwork is being laid. Automation is accelerating, and cyber defenders must adapt accordingly.

The DDoS Configuration Problem No One Talks About

Many organizations deploy advanced, multi-layered DDoS defenses on Content Delivery Networks (CDNs), Scrubbing Centers, on-prem. devices, and Web Application Firewalls (WAFs). Yet, they continue to suffer from costly downtime.

Protection that isn't properly configured might as well not exist. Modern attackers know this and use multi-vector, low-and-slow techniques to bypass volume-based thresholds and exploit misaligned security layers.

Defenses must now go beyond thresholds. They must be smart, behavior-driven, and continually validated.

Continuous Testing is Critical

To eliminate the risk of damaging DDoS downtime, organizations need to run continuous DDoS attack simulation. This type of ongoing testing is critical for identifying blind spots across complex environments and provides insight into DDoS vulnerabilities and misconfigurations for all known attack vectors. This validation stage helps organizations align their protection layers by identifying and enabling the remediation of DDoS vulnerabilities.

When layered properly and tested continuously, many of the DDoS vulnerabilities that we see, especially in Layers 3 and 4, can be mitigated using existing mitigation tools. It's not about spending more; it's about using what you already have, better.

You're Probably Under-utilizing Your DDoS Protections

Organizations often invest in high-end DDoS mitigation platforms, only to fall back on standard protections like rate limiting and static filters. Unfortunately, these basic tools are more likely to block legitimate traffic and cause disruption than eliminate a real DDoS threat.

Ironically, the more sophisticated features provided by the best DDoS protection solutions such as deep packet inspection, behavior-based filtering, and bot detection are not always deployed - out of fear of false positives. Yet, these advanced mechanisms are less likely to block legitimate traffic, if they are properly configured.

What You Can Do Now

If you're not sure whether your DDoS defenses are optimized, start here:

• Get a free DDoS Threat Rating – use our AI-based DDoS threat rating platform to identify vulnerabilities in your DDoS protection by means of a nondisruptive, predictive, engine-based analysis
• Review your existing configurations
• Contact your DDoS protection vendor to explore available (and often free) advanced features
• Enable protections like SYN protection, L4 challenges, behavioral filtering, and out-of-state mechanisms
• Avoid relying on rate limiting or filtering
• Use a continuous, nondisruptive attack simulation tool to validate your defenses are working optimally

Based on our experience, almost all Layer 3/4 vulnerabilities can be addressed using existing features; no extra budget needed. Layer 7 enhancements may require additional investment, though this investment should be far less costly than the cost of downtime.

Putting AI on the Defense Team

AI is also starting to be incorporated into cyber defense strategies. For example, MazeBolt's RADAR intelligently prioritizes attack vectors that are most likely to cause damage using its AI-powered SmartCycle™ feature. SmartCycle is a new way for complex enterprises, with the largest attack surfaces, to prioritize DDoS vulnerability remediation.

Final Thoughts: AI vs. AI

As attackers get smarter, so must defenders. AI-driven threats are on the horizon – and in some cases, they are already here. Meeting them requires visibility, agility, and better use of the tools already in your stack.

You don't need to overhaul your defenses. You just need to optimize them by testing – and adjusting mitigation policies based on the results.

Are you investing in DDoS protections but still suffering DDoS damage? Speak with a MazeBolt expert!

About the Author

Amit Morson is MazeBolt's VP Services. Amit has over 20 years of experience leading technical support and professional services for cybersecurity companies. With extensive knowledge in IT and security, Amit has a strong technical understanding of complex tech for enterprises and the analytical insight and capabilities necessary for evaluating enterprise business requirements and workflow.