Cybersecurity Ventures’ report shows that global cybercrime losses will be US$6 trillion in 2021, and global spending on combating cybercrime is expected to increase to 10.5 trillion in 2025 U.S. dollars, three times as much as in 2015 ($3 trillion).
Artificial intelligence is almost the only solution.
Statista, another research organization, believes that the value of artificial intelligence in cybersecurity has exceeded US$10 billion in 2020, and is expected to reach US$45 billion by 2027. IBM believes that companies that lack artificial intelligence security have three times the cost of defending against cyberattacks than companies that have deployed AI automated defense systems.
Research data from Meticulous shows that artificial intelligence applications in the field of network security will grow at an annual rate of 24%, reaching $46 billion by 2027.
By analyzing DNS traffic, artificial intelligence can automatically classify domain names to identify domain names such as C2, malicious, spam, phishing, and cloned domains. Before the application of AI, management mainly relied on blacklists, but a large number of updates were arduous. In particular, black products use automatic domain name generation technology to create a large number of domain names and constantly switch domain names. At this time, intelligent algorithms need to be used to learn, detect and block these black domain names.
Currently more than 80% of Internet traffic is encrypted, and traditional means are ineffective except for decryption. With the help of artificial intelligence technology, there is no need to decrypt and analyze the payload, but to identify through metadata and network packets, mainly including:
Encrypted traffic analysis has already played a role in practice, at least to help users not be completely blinded by the growing amount of encrypted traffic. However, since this technology is still in the emerging development stage, there is no need to invest too much cost and energy for the time being.
An AI algorithm that utilizes recurrent neural networks and encoding filters can identify “deepfakes,” found Whether the face in the photo has been replaced. This feature is particularly useful for remote biometrics in financial services, preventing scammers from falsifying photos or videos to pass themselves off as legitimate citizens who can obtain loans.
This AI technology is able to read unstructured data in non-machine readable formats Information, combined with structured data from various network devices, enriches the data set to make accurate judgments.
Based on statistical data, AI can recommend which protection tools to use or which settings need to be changed to automatically Improve network security. Moreover, due to the feedback mechanism, the more data the AI processes, the more accurate the recommendations it will give. For example, MIT's AI2 detects unknown threats with an accuracy of up to 85%. In addition, the scale and speed of intelligent algorithms are unmatched by humans.
Artificial intelligence has broad prospects in the field of network security, but only if it is used reasonably. Like all other technologies, AI is definitely not a silver bullet. Even having the most advanced technology does not mean 100% protection. Artificial intelligence will not save you from serious attacks caused by ignoring basic cybersecurity rules.
The correct approach is to establish an ecosystem that can adapt to constant changes and continuously make corrections or adjustments while developing and implementing intelligent algorithms to generate real benefits. As you can imagine, this is a time-consuming and arduous task, but considering that we are not using AI for hype or fashion, network security based on AI technology will and will eventually produce huge value.
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