Databricks > 实例探究 > Barracuda Networks:在 Databricks 上使用机器学习来防止大规模电子邮件网络钓鱼攻击

Barracuda Networks:在 Databricks 上使用机器学习来防止大规模电子邮件网络钓鱼攻击

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技术
  • 分析与建模 - 机器学习
  • 平台即服务 (PaaS) - 应用开发平台
适用行业
  • 教育
  • 零售
适用功能
  • 采购
用例
  • 预测性维护
  • 零售店自动化
服务
  • 数据科学服务
关于客户
Barracuda Networks 是安全、应用程序交付和数据保护解决方案领域的全球领导者。它们帮助客户大规模抵御网络钓鱼攻击,并提供全面的电子邮件安全保护。
挑战
由于当今攻击者创建恶意电子邮件的复杂程度很高,因此处理网络钓鱼电子邮件非常困难。 Barracuda Networks 使用机器学习来评估和识别恶意消息并保护其客户。
解决方案
Barracuda 利用 Databricks Lakehouse 平台上的机器学习,特别是使用 Databricks 特征存储和托管 MLflow,来改进 ML 流程并更快地部署质量更高的模型。
运营影响
  • The integration of Databricks Lakehouse Platform into Barracuda Networks' operations has resulted in a more efficient and effective phishing attack detection and prevention system. The use of the Databricks Feature Store and Managed MLflow has simplified the process of feature engineering and model deployment, allowing the team to move faster and block more malicious emails. The system's improved efficiency has also resulted in a higher detection rate, leading to improved customer protection and satisfaction. The team is now able to publish a new table frequently in Delta, update the features every day, and use these to determine whether an incoming email is an attack or not. The team is looking forward to continuing to implement new Databricks features to enhance their customers' experience further.
数量效益
  • Barracuda is now blocking tens of thousands of malicious emails daily from reaching millions of mailboxes across thousands of customers.
  • The team can now move much faster with the help of Databricks Lakehouse Platform.
  • The use of Databricks Feature Store and Managed MLflow has greatly reduced the time the team spends developing ML models.

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