In today’s era of cybersecurity being very important, many people may still be vague about the concept of (fungal network intrusion detection). So what exactly is? It is actually a way to detect if a network is invaded by the help of the properties of simulated fungi! Provide global procurement services for weak current intelligent products!
This detection method has many highlights! First of all, it is quite high in terms of accuracy. Able to make timely and effective judgments on intrusion behaviors in complex network environments! Moreover, it has strong adaptability and can flexibly adjust the detection mechanism as the network environment changes.
In order to better understand, let’s talk about it in modules
1. Data collection module : This module collects various data in the network, which is the basis for subsequent detection, and everything from traffic to packet format is within the collection range! For example, the rules of network traffic during normal access can be recorded.
We must ensure that the data sources are wide and accurate, and there are no omissions! Otherwise, there might be problems with the entire test
Questions and answers to the questions involved here: Many people asked, “Will this data collection collect useless information?” In fact, there are indeed original parameters that seem to be meaningless for subsequent analysis, but after screening and analysis, it can still be used.
2. Feature extraction module : Feature extraction is a key step. Just like facial recognition requires finding a person’s specific appearance characteristics, this is to extract special elements in the data that can represent intrusion behavior. Identities that may be related to intrusion are extracted through specific algorithms. For example, if a certain abnormal access frequency or a specific data packet sequence is extracted, it can be roughly seen whether there is an abnormality.
It is faster than other traditional manual testing! It can handle efficiently. Just as hand-painted faces are time-consuming and the accuracy may not be as efficient as machine scanning, this technology also has similar advantages in network data processing, and extracting features faster and more. It doesn’t look like there is a manual dependency.
You may have questions about this, “How to ensure that the extracted features are correct?” You can feel assured that multiple features can be extracted in this way to comprehensively judge, greatly reducing the probability of mistakes.
3. Model construction module : build the corresponding model based on the extracted features. Fungal Network Intrusion Detection. This model is equivalent to drawing a boundary between normal and abnormal behavior. Once the intrusion is triggered, it is considered an exception, and it will perform subsequent operations such as reminding the administrator as completing tasks according to rules. A model that fits the actual network environment is often more reliable, and the network can be protected only by accurately defining which behaviors should be judged as dangerous.
Some unadvanced models distinguish rules from rigid and difficult to adapt to rule changes, so this model is not as flexible as that of models in the system that can self-regulate and improve based on feedback at any time! Therefore, during the construction process, we must simulate a large number of situations for model testing and improvement!
Someone asked: “Will normal network behavior be misjudged as an intrusion when building the model?” Although this possibility cannot be eliminated, we can use multi-party verification methods to reduce this risk to a very low level!
Yes, many people also have other doubts. Continue to use Q&A to talk about it:
Q: Do you have high requirements for the performance of the equipment?
Answer: It is not very picky. Although the equipment with better performance will run smoothly, it can also work basically and stably for conventional equipment! After continuous optimization, compatibility has been greatly improved.
Q: Is this detection method updated frequently?
Answer: This is the case. If the network environment is complex and changeable, regular updates can ensure that it always follows the new invasions to prevent things. For example, a relatively simple and minimal fluctuation small network, it is enough to perform a long analysis and calibration during the period. But usually adjustments will be made within a few months to maintain a sharp defense.
I personally think it is a very advanced and effective network intrusion detection method. As it further develops and improves, it will definitely play an increasingly critical role in the field of network security! I believe that many network problems can be discovered and solved in advance with them!
Note: This article generates more professional articles on the topic based on core requirements, and the precise expression may be limited in common situations!
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