Watch Now: Machine Learning in an Ever-Evolving Security Landscape
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to Jordan Silva and Matteo Rebeschini
5 Ways in Which Machine Learning Improved Cybersecurity
Machine learning has revolutionized the field of cybersecurity by enabling a new generation of automated tools and techniques that can help detect and prevent cyber threats. Here are some of the ways in which machine learning has contributed to cybersecurity:
- Malware detection: Machine learning algorithms can analyze large volumes of data to identify patterns that are characteristic of malware. These patterns can then be used to develop models that can detect new instances of malware.
- Intrusion detection: With the help of machine learning security teams can quickly detect anomalous behavior in network traffic and flag potential security breaches.
- Fraud detection: Machine learning helps identify patterns of fraudulent activity in financial transactions, like credit card fraud or money laundering.
- User authentication: Machine learning is used to build models that can detect anomalies in user behavior, such as unusual login patterns or access to unauthorized resources.
- Threat intelligence: Machine learning can be used to analyze large amounts of data from various sources to identify new and emerging threats. This can help security teams stay ahead of attackers and take proactive measures to prevent attacks.
Hawaiian Telcom has been helping many customers improve their cybersecurity posture. Solutions like Email Security, Endpoint Protection, or Managed IT, can greatly lessen the risk of a cyber attack.