Hello
Hierarchical Temporal Memory (HTM) has been widely explored for anomaly detection in streaming data; particularly in time-series forecasting. While applications in industrial monitoring & financial fraud detection have been discussed; there is a growing need to apply HTM to real-time cybersecurity threat detection. 
Traditional rule-based and AI-driven security systems often struggle with detecting novel attack patterns and zero-day exploits. Could HTM’s ability to learn temporal patterns & detect deviations make it a game-changer for cybersecurity? 
One challenge in applying HTM to cybersecurity is the sheer scale & variety of network traffic. Unlike sensor data or stock market trends, network data is highly unpredictable, with shifting baselines and multiple influencing factors.
Would HTM require modifications to handle multiple data streams efficiently? Additionally, how well can it differentiate between benign anomalies (such as new software updates causing unusual traffic) and genuine security threats? Some researchers argue that combining HTM with other AI models, like deep learning or reinforcement learning, could create a more robust hybrid approach.
If anyone has experience applying HTM to cybersecurity or related fields, what are the key challenges and potential solutions? Checked https://www.numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%20Anomaly%20Detection.pdf/AWS Online Training guide related to this and found it quite informative.
Are there any existing frameworks or case studies where HTM has been successfully integrated into threat detection systems? I’d love to hear insights from the community on whether HTM can evolve into a reliable tool for cybersecurity professionals.
Thank you !! 