in Cyber Security

There are many different use cases of ML



1. Network Traffic Analysis 

ML algorithms can be implemented within network traffic analysis to detect network-based attacks such as DDoS attacks, the attack vector or the attack type (e.g., TCP Flood), which enables SOC teams to take precautions against upcoming cyber threats in the future.

2. Endpoint Fortification 

It is crucial for companies to protect their endpoints against malware and viruses,ransomware, or spyware, helping SOC teams to prioritize and prevent upcoming digital risks.

3.Application Security 

Application security solutions such as Web Application Firewalls (WAFs) implement ML-based learnings to protect servers and systems against cyber-attacks targeting the application layer (Layer 7) of the OSI model.

4. Authentication Security 

ML and AI also come into play in authentication security. Facial and fingerprint recognition software directly utilizes ML algorithms to operate, bringing additional protection to hardware systems such as phones and laptops.

5. Attack Surface Management

An AI-based approach to attack surface management can take the burden off SOC teams and ensure fault-free management and monitoring.

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