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The speaker emphasizes the need for organizations to adopt machine learning (ML) and deep learning (DL) models to enhance their security measures. Traditional signature-based approaches are no longer effective in blocking new threats, and ML/DL can help identify patterns and behaviors to improve protection.
The contrast between ML and DL is highlighted: ML is trained on specific data sets using supervised or unsupervised methods, while DL is trained on entire data sets to identify differences in behaviors and patterns.
The speaker provides examples of how ML can be used to improve security:
- Blocking phishing attacks by analyzing new URLs with precision algorithms.
- Stopping exploitation attacks against known vulnerabilities using ML-based packet analysis.
It’s stressed that the earlier an organization detects threats, the better it is for security outcomes. Therefore, incorporating ML/DL into security strategies can help prevent and detect attacks more effectively.
A real-world example from Palter Networks is shared, showcasing how they use a combination of technologies with ML/DL to reduce their attack surface and respond to incidents accurately. They process 36 billion events daily, bringing them down to 133 incidents that require response, with most being partially or fully automated.
The speaker concludes by highlighting the importance of integrating ML/DL into security strategies and invites questions on how they can help organizations improve their cybersecurity measures.