Cyber security is a game of cat and mouse. “Rats” always have the upper hand, because new malware is always hidden and difficult to detect. Despite billions of dollars being poured into the field each year, cyberattacks continue to occur and hackers make a fortune from them.
Artificial intelligence, has been a lot of attention recently, people are very enthusiastic about this technology. Almost every industry has introduced artificial intelligence, and the cybersecurity industry certainly can’t miss the craze. Artificial intelligence makes security systems easier to train and more flexible in dealing with dynamic situations. New vulnerabilities can be easily identified and analyzed to deter further attacks by malicious attackers. It also gives security vendors, businesses, and us the upper hand in dealing with cyber attacks.
Ding Ke, vice president of Tencent, believes that the future security ecology needs three genes:
First, build on deep global connectivity. Today, the issue of network security has broken through the boundary between virtual and real, the boundary between countries and regions, and become a ubiquitous global issue.
Second, it is characterized by the common duty of human and artificial intelligence. With the explosive growth of various Internet technologies, the means of network attacks are also constantly enriched and upgraded, the only constant is the change itself. To defend against network attacks, we must have the ability of quick identification, quick reaction and quick learning.
Third, have intelligent dynamic defense capabilities. The essence of network security is the confrontation between attack and defense. In the traditional mode of attack and defense, the initiative is often in the hands of the network attackers, and the security forces can only take it passively. However, in the future security ecology, through data and technology exchange and information sharing, members can inspire each other, automatically upgrade their security defense capabilities, and even predict where threats are to a certain extent.
Ai prevents malware and file execution
File attack is still the main means of network attack. The most common types of files used in file attacks include executable files (.exe), Acrobat Reader (.pdf), and Microsoft Office files. As little as one line of code can generate a new malicious file with the same malicious purpose, just signed differently. A small change in its behavior can fool traditional signature-based antivirus programs, as well as more advanced heuristic endpoint detection and response (EDR) solutions, and even net-based solutions like sandboxes.
These problems can be avoided by using ai’s strong search capabilities to detect the slightest code change among the millions of features in each suspect file.
Advantages of artificial intelligence
Organizations face millions of threats every day that are impossible for security researchers to analyze and categorize. However, machine learning can accomplish this task efficiently. Unsupervised and supervised machine learning allows us to leverage current threat knowledge and media. Once machines are combined with the ability to detect new attacks and discover new vulnerabilities, organizations’ systems will be able to defend against threats in a more efficient way.
But, like every machine learning algorithm, these advanced algorithms also require manual learning because humans are more capable of recognizing anomalies, while machines may put them in different situations and ignore security threats. There is another benefit to AI-based systems, which, in theory, will operate in a more accurate manner, eventually eliminating human error. In addition, these systems can perform multiple tasks simultaneously, monitor and protect a large number of devices and systems, and ultimately mitigate large-scale attacks.
Artificial intelligence is also a double-edged sword, benefit network security industry at the same time, there are some safety factors need to consider, +, artificial intelligence is the most powerful network security, the network security of the world, predominantly people mainly rely on the professional set up rules, so do not conform to the rules of attack would be missed. Machine learning, which relies on anomaly detection, is prone to false positives and too many “cries of Wolf” that can lead to distrust. Using AI to learn and find the most likely attacks, which can then be handed over to human experts to identify, is a way to combine the best of both.