Machine Learning (ML) refers to that branch of computer science and artificial intelligence that works by learning human behaviors. It does so with the help of training models and its accuracy improves gradually with time. Nowadays, ML is also used very extensively in the field of cybersecurity. Today, we will try to shed some light on the top 5 use cases of machine learning in cybersecurity. So, let us take a look at these use cases together.
Machine Learning for Preventing SMS Scams
SMS scams became all the more common during the COVID-19 pandemic. People were actively using sms-based communication methods for interacting with their acquaintances. Many government organizations also started spreading awareness about this pandemic among the public by using the same means. In this situation, some malicious entities also appeared on the scene. In the veil of the pandemic, they started sending malicious messages to people with the intention of exploiting them. Machine learning models have been actively in use for preventing such sms-based scams.
Malware Detection
Machine learning models are also implemented in antivirus and antimalware software programs. These models are capable of detecting malware and viruses with their signatures even before they can start infiltrating your system. In this way, you can detect and prevent all such malicious entities from breaching your privacy. Moreover, you can also keep your sensitive data secure this way.
Machine Learning for Email Monitoring
At times, people find it really hard to differentiate between phishing emails and legitimate ones. Machine learning models are helping email users in identifying phishing emails. Generally, all the suspected phishing emails go to the “Spams” folder of your email inbox hence preventing you from opening up any malicious links accidentally.
Network Threat Analysis
Computer networks are also susceptible to different threats and attacks. It is very important to catch these threats early on before they can destroy the whole network. ML-trained models can actively catch all these threats and attack vectors way before they can penetrate deeper into your network. In this way, you can keep the security of your network intact. Moreover, these models can also help you greatly in performing a thorough analysis of all such potential threats. With the findings of this analysis, you can significantly improve your overall network security posture.
Prevention of Bot-based Attacks
The bot-based attacks particularly the Distributed Denial of Service (DDoS) attacks can bring down an entire server within a few minutes. These attacks work by bombarding a server with fake connection requests hence exploiting it to its full capacity. As a result of this, the server denies the service requests by legitimate users. The ML models can be trained to detect such fraudulent attacks to prevent servers from going down.
Conclusion
Above, we discussed the top 5 applications of machine learning in cybersecurity. By training your ML models intelligently, you can prevent many cybersecurity issues. Also, we highly encourage organizations to build their own ML models for preventing their organizations from cybersecurity attacks. For that, you can always take the services of Folium AI.
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