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Machine Learning Helps in Cyber Security

Machine Learning plays a crucial role in cybersecurity. It enables the development of advanced tools and techniques to detect, prevent, and respond to cyber threat. Cyber ​​attack activities are becoming increasingly rampant nowadays, so it is difficult for organizations to use security tools to protect their business and combat cyber attackers. In the past, network security was used based on signature pattern matching or rules. Many organizations rely on antivirus software to improve network security, and it is only used to detect malware that matches the signature or virus definition. The use of machine learning technologies can enhance network security.

Machine learning can identify the attack at the early stage of the attack and prevent it from spreading to the entire system. Many network security companies use advanced analysis methods such as user behavior analytics and predictive analytics to identify APT attacks in the early stages of the threat life cycle. This method can effectively prevent identity data leakage and internal threats.

Many large companies are already investing billions of dollars in this area. For example, Google uses machine learning to analyze threats to mobile apps running on Android and identify and remove malware from infected phones. At the same time, cloud infrastructure giant Amazon acquired harvest.AI startup and launched Macie, a service that uses machine learning to detect, sort, and classify malicious data stored in cloud storage.

How Machine Learning Helps in Cyber Security?

Machine learning is as important as artificial intelligence systems for improving network security and is used to analyze and protect systems from the latest network security threats. Machine learning can help enhance network security by:

  • Prediction and detection of cyber attacks

As hackers continue to conduct cyber attacks, many companies use machine learning to detect cyberattacks. As the number of malware attacks continues to grow over time, organizations need more dynamic detection methods, such as machine learning systems. Machine learning uses data obtained from past cyber attacks to enhance artificial intelligence systems. They constantly deal with cyber-attacks and identify other possible cyber threats.

  • Improving the efficiency of human analysis

Implementing machine learning to improve network security can protect an organization’s data and assets. Without the help of machine learning systems, security experts may spend a lot of time analyzing these threats or wait until a cyber attack occurs before conducting a diagnostic investigation. Machine learning can learn through predictive analysis and identify large amounts of data to reduce the network attack surface, so it is a very effective network security tool. For the security team, the number of security alerts that appear every day may be large.

  • Elimination of bugs and vulnerabilities at the stage of coding

The more code in a project, the more difficult it is to find and fix bugs. Therefore, the cost of finding and fixing an error increases as the project develops. Artificial intelligence can provide instant feedback that can help spot bugs early in the process.

  • Moving to cloud computing infrastructure

Since machine learning systems are now software-based, it is easy to migrate to cloud computing infrastructure. For example, antivirus software requires a good Internet connection to match threats in Real-time and keep the database updated. By using intelligent machine learning software for monitoring, organizations can obtain secure network security services in their cloud computing environment and prevent the infiltration of malicious software.

At present, artificial intelligence and machine learning are providing network security services in various applications or platforms to help organizations effectively resist network attacks.

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