How AI and Machine Learning Are Improving DDoS Attack Prevention
Some years ago, competition and reduced sales were the only things businesses had to worry about. Fast forward to the modern era, and cybercriminals have given organizations a different type of headache.
Almost every business in today’s world has an online presence. However, a website exposes your firm to different forms of cyber attacks like DDoS hacks. Fortunately, as cyber threats have evolved over the years, so has the technology to combat them.
Take artificial intelligence and machine learning, for instance—two significant innovations in recent times that are helping cybersecurity teams detect and respond to DDoS attacks faster and more efficiently.
These technologies offer real-time monitoring capabilities and the ability to adapt to evolving DDoS threats. In this article, we share four ways that AI and machine learning are helping organizations prevent DDoS attacks.
Traditional DDoS Protection Techniques and How They Fall Short
Traditional DDoS protection strategies and technologies for most businesses usually include:
- Web Application Firewalls (WAF)
- Rate limiting
- Load balancing
At some point, these methods were effective enough to shut down DDoS attacks. Firewalls, for instance, could identify malicious traffic requests and prevent them from accessing a network.
Rate limiting, on the other hand, determines the number of requests an IP address or device can send, while load balancing spreads traffic across multiple servers, making it impossible to overwhelm a single one.
Unfortunately, cybercriminals evolve their methods. As the threats changed, traditional tools could no longer tackle them.
With botnets, hackers could flood a network with thousands of traffic requests, breaking the resistance of firewalls and rate limiting technologies. It was also possible to overload networks with multiple servers using these large-scale attacks.
Plus, malicious traffic became more sophisticated and could mimic legitimate requests. As such, they were harder for traditional tools to detect.
Businesses now had to find new ways to combat evolving DDoS threats. Thankfully, the AI innovation happened, and implementing these tools into cybersecurity strategies was the solution.
4 Ways AI and Machine Learning Improve DDoS Detection and Protection
1. Real-time Monitoring and Behavioral Analytics
Traditional cybersecurity methods like firewalls typically use a rule-based approach to identify DDoS attacks. Basically, these tools would compare traffic requests against a set of rules to determine if they’re threats to a network.
However, the monitoring process was largely manual. So, in many cases, a DDoS attack would have completely overwhelmed a system before detection. Plus, cybercriminals continuously evolve, leading to more sophisticated attacks that traditional methods cannot identify.
This is where artificial intelligence offers enhanced security. AI cybersecurity tools can analyze large volumes of data quicker than human or traditional technologies. And they carry out this analysis in real-time.
So, they offer round-the-clock monitoring of a network and can more efficiently identify abnormal traffic patterns. Most cybersecurity companies, like Datadome, now use advanced AI tools to offer real-time DDoS protection for companies.
2. Eliminating False Positives
Traditional cybersecurity tools can sometimes block legitimate visitors from accessing a website if they mistake the traffic request as malicious. For businesses, this could result in losing potential customers.
However, AI solves this problem with advanced analytical abilities. AI tools can further analyze suspicious traffic using multiple factors to determine if they’re actual threats or false positives. The result is more accurate threat detection and fewer false alarms.
3. Adapting to New Threats
One reason it’s so difficult for businesses to protect against DDoS threats is that they continuously evolve. Hackers develop new and sophisticated ways to infiltrate an organization, and usually, traditional systems are inadequate to protect against upgraded attacks.
In comparison, AI tools combine with machine learning to learn from each attack and evolve accordingly for enhanced protection. These tools can also be trained on new data to keep up with the changing cybercrime landscape.
4. Automated DDoS Response
Monitoring a network and identifying DDoS threats in real time is one thing, but organizations also want to react to these attacks as quickly as they’re spotted. These near-instant reactions are also a possibility with AI cybersecurity technologies.
With these tools, you can automate responses to DDoS attacks, triggering reactions like rate limiting, blocking suspicious IP addresses, redirecting legit traffic to secure networks, or even alerting the IT team.
When AI cybersecurity tools alert IT teams about threats, they can go a step further and offer recommendations on dealing with the attack. These technologies can also adjust the suggestions in real-time as the attack evolves.
Final Thoughts
Artificial intelligence provides more benefits beyond capabilities like video editing and text generation. Organizations and cybersecurity teams now use these tools to protect against DDoS threats, as well as a number of other emerging cyber threats that modern businesses face.
AI offers real-time monitoring, behavioral analysis, and automated responses to identify and shut down DDoS attacks faster than traditional methods. Machine learning also allows these advanced tools to learn and adapt to the ever-evolving cyber threats.