DDoS attack + AI poisoning: how to prevent big data security? (Popular version)

Now, no matter whether it is a daily brush cell phone, APP, or commercial space to engage in airspace scheduling, low-altitude economy of the drone flights, the back of the “big data” and “AI intelligence” are indispensable. Big data is like our “digital warehouse”, storing a variety of key information; AI is like “intelligent housekeeper”, relying on these data to make judgments and work. But these two core babies, there are two major “invisible enemies”, one can directly paralyze the system, one can secretly teach AI bad, ordinary people listen to the confusion, in fact, with the vernacular a talk to understand, we will talk about how to crack the two major threats today, guarding the security of big data.

The first enemy: DDoS attacks - that is, to “paralyze” the system.”

What exactly is a DDoS attack?

Distributed Denial of Service (DDos for short)，distributed denial service (DDOS)Service Attacks.Let's compare a big data platform, website or server, to a popular restaurant. Under normal circumstances, only a small number of customers (normal users) come into the restaurant to eat, and the clerk (the system) can easily receive them in good order.

And DDoS attacks击，That is, the bad guys find thousands of “fake customers (fake users)”, all at once into the restaurant, these people do not eat, do not consume, just occupy the position, blocking the door, constantly shouting for service, the restaurant crowded. The result is: real customers can not come in, the clerk is busy responding to the collapse of the restaurant directly out of business - corresponding to the network, that is!Web site can not be opened, the data can not be adjusted, the system is completely stuck!It's not stealing data, it's just purely making it bad so you can't use it.

Give me a real example.：2016 United StatesDyn's DNSMassive DDoS attack on servers leads to致Twitter、Netflix, Amazon, GitHub, New York Timesisometricpay attention toThe entire network of streaming platforms was down for hours.Users have no access at allThe first time I've ever seen this happen, I've seen it happen. If this happened in our businessAerospace airspace systems, once attacked, the scheduling data of drones and aircraft directlyDisconnection, riskextremely high。

DDoS attacks are harmful and risky in critical areas

The big data platform bears the core function of data storage, calculation and transmission, and has extremely high requirements for network stability, while the “pile up trouble” characteristic of DDoS attacks just hits the pain point of big data platform.

The frequency and scale of such attacks have skyrocketed in recent years.has become a critical system for big data and all types ofNumber one external threat, authoritative monitoring data visualize the level of threat:

Combined with these data, we can see that today's DDoS attacks are extremely powerful, and once the core areas targeted, the consequences are unimaginable. For example, commercial aerospace airspace scheduling system, Shenzhen low-altitude economic UTM control platform, if encountered T-level DDoS attacks, airspace real-time data will be immediately disconnected, the trajectory of the aircraft can not be tracked, which directly triggered a flight safety incident; ordinary enterprises of the big data center was attacked, the business directly shut down, and the economic losses are also incalculable.

How to prevent it? 4 common cracks

1. Traffic “filtering stations” (core defenses):Set up a professional security channel at the entrance to the restaurant, arrange for special people to screen one by one, specializing in pulling out those who do not eat, specializing in disruption of the “child care”, only to release the real to consume the customer. Corresponding to the network isflow cleaningThe professional equipment will automatically identify malicious attack requests and normal access requests, intercept and discard the attack traffic directly, and let only clean and normal data enter the big data platform, blocking most of the damage from the source.
   [Case]Many e-commerce platforms also rely on this feature to filter out malicious brushing and disruptive traffic during big promotions to ensure that normal users can shop normally.
2. Decentralized triage, no bunching (spreading the pressure): Take the head office (core data center), which was originally centralized in oneSplit into multiple small outlets (CDN edge nodes) all over the placeUsers don't have to go to the main store, they can access the data at nearby stores. Even if there are bad guys looking for “trustees” to make trouble, they can only squeeze individual remote stores, the main store where the core data is stored is completely unaffected, completely dispersing the traffic pressure brought about by the attack and avoiding a potpourri.
   [Case]The UTM control platform of Shenzhen's low-altitude economy uses this approach, and even if individual regional nodes are attacked, the city's overall airspace scheduling is not affected.
3. Flexible expansion, add locations at any time:Big data platforms usually only open a “fixed-size site”, just like a restaurant usually only a fixed number of tables and chairs, DDoS attacks suddenly flooded with massive requests, tables and chairs and manpower instantly insufficient, the system is directly stuck. Elastic expansion is the ability of the platform toAutomatic temporary addition of sites, additional arithmetic allocation (peak traffic management, QoS quality of service management).It is equivalent to a restaurant immediately setting up dozens of extra tables and hiring a group of extra clerks, even if there is a sudden surge in requests, it can still take it on and not be instantly overwhelmed, and then automatically return to normal when the attack recedes, without wasting resources, and specialize in dealing with this kind of sudden pileup of disruptions.
   [Case]The live broadcast platform for large-scale events and parties, which encounters a large number of users online at the same time, automatically expands its capacity, exactly the same principle as this one, to prevent instant lag and collapse.
4. Emergency quarantine, stop-loss peddling (last line of defense):In the event of a large-scale attack, the number of “trustees” that are messing with the system far exceeds the ability to defend against them, so if you try to take it on, you'll just crush the system completely. That's when you activate the black hole.Traffic traction (network policy management)This is equivalent to temporarily closing the doors of the restaurant completely, directing all malicious traffic to the blank “black hole”, preventing them from touching the core system, first to protect the big data and servers from being damaged, and then reopen the door to resume operation after the attack is weakened and cleaned up, so as to minimize the loss.
   [Case]This is the method used by some government service platforms to temporarily isolate the attacked IPs and prioritize the preservation of core business when they are hit by super DDoS attacks.

The second enemy: AI poisoning - secretly “teaching” the AI to go astray.”

What is AI poisoning? More insidious than DDoS

AI intelligent housekeeper, all rely on big data “teach” it to do things, teach the content of the right, it will be accurate judgment; teach the content of the wrong, it will be a mess of judgment.AI poisoning, that is, the bad guys secretly into the AI's “learning materials” (training big data) doped.False information, mislabeling, malicious dataThe AIs are slowly being taught to make errors in judgment.

Many people mistakenly think that it takes a large amount of contaminated data to affect the AI, but it is just the opposite, the authority's actual test data proved that a very small amount of poisoning data can make the AI appear serious misjudgment, hidden and harmful than DDoS attacks:

Combined with the data, the threshold of AI poisoning is extremely low, the harm is great, and it is even more hazardous to put it into the actual scene. For example, commercial spaceflight airspace identification AI, the bad guys as long as a very small number of dangerous drones data, labeled as normal data mixed into the learning library, AI will let go of the illegal aircraft after learning; financial risk control AI by a small amount of fake data poisoning, will be malicious fraud operation is judged as a normal transfer of funds, which directly causes financial losses, this type of risk in the commercial spaceflight, low-altitude economy and other fields, is very likely to cause major security accidents.

How do you crack it? Keep an eye on the whole thing. No bad data gets in.

1. Strictly control the entrance of information (blocking at the source):Choosing learning materials for AI is just like choosing textbooks for children, only picking contents from regular publishers and reliable channels, and never using materials from unknown sources. After getting the materials, we will carefully screen them first, delete all the fake information, wrong data and malicious contents, and establish strict auditing standards, so as to prevent bad materials from mixing into the AI's learning library from the first step, and cut off the possibility of poisoning from the root.
   [Case]Commercial spaceflight airspace scheduling AI, only use the official actual measurement of the real aircraft data, never randomly use the data on the Internet from unknown sources, just to prevent poisoning.
2. The learning process is supervised throughout (process control):AI learning, can not be left unattended, we must keep an eye on its learning effect in real time, such as recording its judgment correctness, recognition accuracy. Once you find that it suddenly makes frequent mistakes, judgment logic becomes chaotic, immediately pause the study, a comprehensive investigation is not the learning materials were bad guys tampered with, mixed with fake content, to find the problem in a timely manner after the replacement of clean materials.
   [Case]If the quality control AI in a factory suddenly and frequently judges defective products as qualified, workers will immediately check its learning data, and the probability is that it has been adulterated with fake samples。
3. Real-time error correction and timely fixes (runtime protection):AI formally on the job, but also to check the results of its work in real time, such as airspace control AI has not misjudged the aircraft, wind control AI has not misjudged the risk. Once found that the results are not right, misjudgment, immediately suspend its work, with 100% correct clean data to retrain it, quickly “break it right”, restore normal judgment ability, to avoid errors continue to expand.
   [Case]If the navigation AI suddenly and frequently leads the wrong way, the background will immediately recalibrate with the correct route data and quickly return to normal.
4. Full traceability and easy traceability (after the fact):Every piece of information and every judgmental decision that AI has learned is recorded as if it were a ledger, leaving permanent traces. Once the AI is found to be poisoned, it can follow the records to quickly locate which information and which link has gone wrong, and accurately find the source of contamination, so that it can be quickly repaired and also prevent similar problems from recurring in the future.
   [Case]When the bank's risk control AI goes wrong, staff will look through its learning data records to quickly find out which batch of data led to the error in judgment and solve the problem efficiently.

The most critical: two major threats together to prevent, build a solid double security shield

Combined with authoritative data, 2024-2025 global AI-related security incidents surge year-on-year, the peak of DDoS attacks nearly tripled, the bad guys tend to “two-pronged”: the first large-scale DDoS attacks to mess up the system, while everyone is busy repairing, and sneak to the AI mixed with fake information to do the poisoning, can not be defended.

That's why it's important to combine the two defenses:Outside against being paralyzed by mischief, inside against being taught.While building a DDoS defense system, firmly guard the door of the system to prevent malicious traffic from squeezing in to make trouble, to ensure that the big data platform can run stably and access data normally;

On the one hand, we keep a close eye on the whole process of AI, and keep a watchful eye on the whole process from learning to working, so as not to let false and polluted data teach AI, and to ensure that the judgment it makes is accurate and reliable.

The two are closely coordinated, internal and external joint defense, in order to make big data storage stable, AI with peace of mind, whether it is the daily Internet access, business operations, or commercial spaceflight, low-altitude economy, such as the field of very high security requirements, can run smoothly.