How ML technology and tools are used in cybersecurity?

Anomaly Detection: ML models can be trained to recognize patterns in network traffic and system behavior. When there is a deviation from the norm, the system can raise alerts. This is particularly useful in identifying new and previously unknown threats.

Malware Detection: ML algorithms can analyze the features and behavior of files to identify malicious software. They can be trained on known malware samples to detect new variants.

User and Entity Behavior Analytics (UEBA): ML models can establish baseline behavior for users and entities within a network. Any unusual behavior, such as unauthorized access or data exfiltration, can be detected and flagged.

Predictive Analysis: ML can analyze historical data to identify trends and predict potential security threats. This proactive approach allows organizations to mitigate risks before they materialize.

Phishing Detection: ML models can scan emails and websites to identify phishing attempts by recognizing characteristic language and patterns used by attackers.

Natural Language Processing (NLP): NLP-based ML models can analyze text and conversation content to identify potentially malicious or suspicious communications.