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Machine Learning: A New Way to Avoid Cyberattacks

Cyberattacks are on the rise. One of the main reasons is admittedly increasing our dependence on devices such as desktop, laptop or smartphones. This dependence, of course, has to do with the use of the internet that has become indispensable in everyday life. As well as the habit of using wireless networks and devices (such as, for example, public Wi-Fi), it also proved to be a reason for an increase in cyberattacks.

This increase in cyberattacks requires solutions and defense. One of the solutions that have been created to ensure Internet security is machine learning. Machine Learning is the scientific study of algorithms and statistical models that use electronic systems to perform a particular task efficiently without using explicit instructions based on motifs and conclusions. It is considered as a subset of artificial intelligence. It offers security solutions and helps prevent attacks.

Machine Learning

Machine Learning has applications in everyday life. Some applications are the following.

Machine learning applications play a very important role in detecting unwanted messages (spam). Surveys have shown that more than half of emails today are undesirable. Today, we have powerful spam filters that work on different sets of rules for spam detection and which are effective.

Still machine learning applications are used for security against all sorts of threats and attacks that occur in networks, software and applications. We must remember that penetration or contamination of a network occurs well before detection. Hackers could penetrate systems or networks and stay there without doing anything for months before they started an attack.

Another field of application of mechanical learning is to locate attacks Phishing. Surveys show that hackers are creating more and more phishing techniques making their tracking more difficult. Such attacks are more common and target very personal data.

Until recently, we had traditional malware detection methods that focused on identifying features. Such detection methods today have become irrelevant and outdated. Today we focus more on detecting malware using machine learning.

Any limitations currently in place regarding machine learning will soon be eliminated, and then we will be given more opportunities to detect and prevent crime on the Internet.

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