Security of AI network

Now more data are generated than before. With the development of data analysis tools, organizations in all walks of life pay more and more attention to the collection and storage of big data.

Big data and more and more cloud storage solutions make it easier for cyber criminals to design new attacks.

With our technological breakthrough, hackers are also equipped with better tools.

Therefore, data privacy and network security are threatened. Technology giants have begun to explore whether artificial intelligence can provide better network security.

Several companies even began to adopt AI based solutions to improve security as soon as possible.

Why does AI have the potential of network security?

AI includes a model to analyze and learn from data.

AI models can identify trends and patterns in data. Therefore, it can become an effective tool to find threats and attacks.

How does AI improve network security?

Here are some ways AI promises better network security.

First, management loopholes.

Most companies adopt a passive strategy, that is, they take measures only after they find vulnerabilities.

Artificial intelligence can take active measures to enable the model to find abnormalities and remind relevant departments in advance.

2. Better authentication.

Relying on the traditional user name and password to log in to the account has proved vulnerable for many times.

Most people don't try to create a strong password.

Even so, passwords can be stored in unencrypted files for easy remembering.

Artificial intelligence based login solutions use a variety of factors to learn each user's login mode.

For each user, the system will calculate the risk analysis score according to various factors (such as IP address, login time, location, etc.). So these login systems can better prevent attacks.

With a huge data set, people can train and build models to identify phishing attacks.

AI can be used to detect common sources of phishing and alarm in time.

3、 Actively detect threats.

Network security threats can cause great damage to any organization. In order not to damage network security, artificial intelligence helps to quickly detect and manage threats.

Monitoring algorithms have been used to build ml models that can classify whether specific situations pose a threat.

However, it has been observed that AI alone often leads to many false positives.

Therefore, network security experts recommend a combination of traditional methods and artificial intelligence based solutions.

Limitations of AI in network security.

Technology can be a double-edged sword.

On the one hand, large organizations invest in R & D to maximize the benefits of artificial intelligence.

On the other hand, malicious people can also use artificial intelligence. If the system using the supervision algorithm is invaded by a hacker, the hacker can change the classification and group label for ease of use.

Then, achieving the overall goal of AI is ineffective.

Indeed, artificial intelligence provides a variety of solutions to improve network security, but it also has its own limitations.

These are some limitations of the implementation of artificial intelligence for network security.

1、 Expenses.

The solution based on artificial intelligence needs a system with strong computing power and using data.

SMEs cannot invest in AI solutions.

2、 It's data collection.

Data form the core of AI based solutions. The more data, the higher the accuracy of the model using this data.

These data must have a sufficient number of items, including malicious attacks.

At present, how many companies can implement unbiased data acquisition technology?

3、 Hacker point of view.

Hackers have the right to use the correct tools of AI. Hackers with knowledge can establish an anti AI model before launching an attack. In this case, the victim is at a loss.

Use AI for cyber security companies.

Google implemented machine learning to mark spam for Google users.

Watson, IBM's cognitive learning platform, has invested in the research of using machine learning to automatically perform safe operations.