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It’s Better to Fight Advanced Cyber Threats By Machine Learning

Fighting cyber threats by machine learning is an effective way. In this article, we covered why it’s better to fight cyber threats by machine learning.

Advanced threats use different vectors to infiltrate your network. Often used for multi-step attacks that introduce malware designed to be undetectable for some time. If no malware is detected, the malware waits just the right moment to complete its mission.

This would typically collect information and send it to the attacker. The attacker then analyzes the data and decides how to use it to commit fraud or steal more data.

Malware is evolving so rapidly and in some cases so sophisticated that it is becoming more and more difficult to detect. In fact, advanced threats such as zero-day attacks, some ransomware variants, and other previously unknown malware are designed to evade detection.

Therefore, tools and procedures must be used in combination to detect and stop these threats.

Attempting to counter advanced threats using traditional signature-based security solutions is like bringing a knife into a shootout.

Signature-based tools do a great job of blocking previously discovered malware, but they are not effective against new threats. This is where machine learning comes in handy.

Fight Advanced Cyber Threats By Machine Learning

Machine learning is getting a lot of attention as there is more conversation about how artificial intelligence (AI) will play a big role in future computing. From a cybersecurity perspective, machine learning can be very helpful in detecting previously unknown threats and predicting attacks.

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But for machine learning to be effective, it needs to devour and process an unimaginable amount of information. Machine learning uses a very complex algorithm that compares data samples to determine if a particular sample is malicious.

The algorithm is trained to look for patterns, characteristics, and anomalies that may indicate the presence of malware.

To achieve this, machine learning model-based malware detection focuses on two main things. The appearance and behavior of the code sample.

If the sample shows the characteristics found in other malware,  further analysis will be triggered to determine if there is any malicious one. In this case, the sample is quarantined before it causes any harm.

The behavior component handles the process initiated by the code sample. The model takes into account the source of the code and what the code is trying to perform (modify or delete files, attempt to communicate with the network, or other actions that should not be performed).

If the sample behavior exceeds a certain threshold, it will be flagged as malicious.

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