The emergence of artificial intelligence has changed the future development trend of IT, and will continue to benefit more industries in the future. The basic principle of AI is that you can collect data, analyze it, make decisions based on knowing the results and learn from the results. That’s why applying AI to cybersecurity gives it new capabilities.
There are a number of reasons why cybersecurity has matured with IT, and the exponential growth of massive amounts of data has made data breaches more common. For example: weak or stolen security credentials, such as passwords; Malware in the form of viruses, ransomware, phishing scams; Social engineering; Threats from within the enterprise; Incorrect IT system configuration and user errors; Vulnerable applications, mismanagement of permissions, etc. The increasing number of hacking attacks has prompted enterprises to adopt artificial intelligence in their network security architecture to improve efficiency and more accurate data defense. At the same time, the development of artificial intelligence has given hackers the ability to improve their attack methods and means.
The impact of artificial intelligence on network security
The development of things always has two sides. On the one hand, artificial intelligence makes it more possible to build intelligent defense system, on the other hand, hackers are also using it to improve the threat ability. In the past, hackers have used sophisticated techniques to write malware code. Now, malware can be sold as smart solutions that just plug and play. This has brought many criminals into the field who do not have the technical skills, thus increasing the number of hackers.
So to defend against such easy-to-use intelligent threats, smarter solutions are needed. For example, network monitoring tools based on artificial intelligence can be used to analyze user behavior, identify patterns, and identify abnormal behavior in the network. Respond accordingly to quickly identify security vulnerabilities. It can detect, monitor, and shut down more media and means of cyber attacks than can be done manually.
The way it works is that ai models are deployed at all endpoints of the enterprise, capturing vast amounts of data for each application in the enterprise to develop profiles. This helps establish a baseline of behavior, and if there is a statistically significant deviation from the code of behavior, the algorithm flags it and investigates it further. Ai could also boost biometric authentication. One of users’ pains in protecting digital assets has long been the need to conceive, remember and periodically change strong passwords. Hackers often use weak encryption methods to penetrate and compromise data security. Now, this can be remedied by using biometric logins that scan a fingerprint, retina or palm print. Biometric logins can be used alone or in conjunction with passwords to control and monitor access.
Automatic defense can greatly reduce resource costs
Because malware is now widely automated, rather than being hacked directly. The automation of malware has made attacks more frequent, sophisticated and relentless.
In particular, the threat of automated malware to iot devices has multiplied as usage has increased. Iot devices are of particular concern because device manufacturers don’t prioritize security when making their products, and users rarely consider security when connecting devices, making them a prime target for Internet attack traffic.
In cybersecurity defense, automation can also save cybersecurity teams time and investment costs. Cybersecurity teams perform many routine tasks that need to be automated because they face recurring events, insider threats, and device management challenges that take time away from their more critical tasks. So automating these mundane tasks not only frees up human resources, but also yields results with greater accuracy in a shorter period of time.
Machine learning ADAPTS to evolving malware
Malware is usually a program with a strict purpose or protocol. Hackers can apply artificial intelligence to them, adapting to each attack and learning from it. Malware that uses artificial intelligence can also mimic human or trusted elements in IT systems, making IT easier for hackers to build polymorphic malware that confuses.
Key assets for malware detection are virus definitions or databases with malware identifiers and signatures that help identify threats. Hackers can use machine learning to evade detection, but cybersecurity teams can also use it to quickly identify risks. Hackers often tweak their malware code to evade security software. A malware database with machine learning can detect malware, whether it is existing or modified, and the system can block it based on events previously thought to be malicious.
Using AI can easily identify evolving threats. Ai systems can be trained to detect ransomware and malware attacks before threats enter the system. Once found, they can be isolated from the system. The predictive capabilities of artificial intelligence outpace the speed of traditional methods. So, there are many advantages to using machine learning in network security, such as: monitoring and analyzing multiple endpoints to deal with cyber threats; Detect malicious activity before it manifests itself as a full-blown attack; Automating routine safety tasks; Eliminating zero-day bugs and so on.
Artificial intelligence is increasingly being applied to Internet security, such as in spam filtering; Network intrusion detection and protection; Fraud identification; Botnet detection; Secure user authentication; Security event prediction and so on.