Recently, many peers came to me and asked, now DDoS attacks more and more ruthless, traditional high defense CDN can still hold up? To be honest, last year I personally saw a financial platform was 300Gbps mixed traffic penetration - rule base update half a beat slower, human analysis is too late to respond. Attackers even used AI-generated traffic patterns, specializing in picking the rules of the blind spot drill.
These days, even the CDN has to “defense teammates”. Many companies think that buying a high defense on everything is fine, the results found that the rule base is updated, but the traffic of their own business was mistakenly killed. I have tested a traditional vendor of CC protection, normal user requests actually because of behavior patterns “too regular” as a robot to intercept - you say this is to prevent attacks or users?
At the heart of the problem is the lag in static rule-based defense. The attackers are iterating, but the defense is still relying on manual hacking IPs and adjusting thresholds. Last year, during an e-commerce promotion, the attacker used deep learning to simulate the shopping path of real users, slowly and low-frequency crawling goods inventory. The traditional CDN frequency-based rules were not triggered at all until the inventory anomaly was discovered, at which time the damage was done.
Why has AI defense suddenly become the meat and potatoes? The fundamental reason is that the attack traffic has begun to “biochemical evolution”. I grabbed a batch of DDoS samples in 2024, and found that 37% traffic packets with adaptive features - according to the defense response to dynamically adjust the packet sending strategy. If you still rely on fixed rules at this time, it is simply a medieval shield to block the laser cannon.
Now for how AI defense actually works in the real world. Taking the intelligent engine of CDN5 as an example, they no longer simply look at the HEADER or frequency of a single request, but analyze the behavioral chain with a spatio-temporal model. For example, if a user visits the login page, product details, and payment interface successively within half an hour, the sequence itself has a probability weight.AI will calculate thousands of dimensions in real time, and even the entropy value of the mouse movement trajectory is counted.
Don't trust vendors that boast “100% accuracy”! I've done stress tests and found that pure AI models have a horrendously high false positive rate under bursty traffic. The best solution is always “AI + rules” dual engine. For example, CDN07's hybrid architecture - AI is responsible for anomaly probability scoring, while the traditional engine does secondary checking. In the test, this combination of false positives was reduced to below 0.01%, which is three times more stable than the pure AI program.
Let me show you a real configuration example. The last time I deployed an AI strategy for a gaming client, I used dynamic weight allocation:
The key is in the CONFIDENCE parameter - the AI assigns a threat score of 0-1 to each request, and only triggers an action if the threshold is exceeded. I'd recommend setting it conservatively initially, rather let the big fish off the hook than hurt real users by mistake.
When it comes to effect comparison, we must mention the data to speak. We did AB testing on 08Host's nodes last year:
In particular, for the identification of slow CC attacks, the AI group issued an alert 11 minutes ahead of schedule - enough time for O&M to make three contingency plans.
But don't think you can rest on your laurels when you're on AI! The quality of model training directly determines the effectiveness of the defense. I've seen some vendors train with outdated samples and directly crash when they encounter new types of encrypted traffic. A good AI defense should have the ability to learn online, such as CDN5's real-time feedback closed loop: each manually confirmed false positives/missing positives will immediately feed back to the model, and new strategies can be iterated within 24 hours.
Finally, I would like to give some practical advice: if you want to choose a model now, focus on the vendor's three major capabilities - sample library freshness (at least monthly updates), reasoning latency (must be less than 2 seconds), and the degree of visualization (can you read the logic of the judgment of the AI). Especially the last point, do not use black box AI - by the time it is mistakenly blocked even the cause can not be found.
In fact, I was most surprised by 08Host's solution, they actually made the AI decision-making process into an attack mapping visualization. Each blocked request can see the risk characteristics of the AI labeled points, such as “mouse trajectory deviation from human behavior 0.23”, “this session's API access timing anomaly 87%” ... ... this transparent design greatly reduces the operation and maintenance team. ...This transparent design greatly reduces the learning cost of the operation and maintenance team.
The next six months will definitely see more defense solutions that incorporate big language analytics. I'm already testing parsing the semantic logic of API requests with NLP models - for example, requests that suddenly query sensitive interfaces in large numbers will be flagged even if the frequency is low. This dimension of human behavior recognition is simply not done by traditional rules.
In the end, AI is not to replace traditional defense, but to give the defense system the ability to anticipate. Just like an old driver driving, not waiting to see obstacles before braking, but in advance to perceive the road state changes. Now the attackers are using AI, if the defense is still manually adjusting the rules, it is simply the digital age of cold weapons versus hot weapons.
By the way: some vendors boast of “AI defense” is simply a rule base with a new name! Real AI must be able to detect new attack patterns on its own. The detection method is very simple - throw in a batch of never-before-seen attack samples and see if it can intercept them autonomously without rule updates. We've tested this and there are no more than five that can do this.
In short (tsk, almost using AI-flavored words again), technology iteration is faster than imagined. This time next year, it is estimated that there is no AI capability of high defense CDN with bamboo spears to defend the city an effect - look quite scary, the actual stab through.
