Through many internal and external feedback sessions, I came up with the design above on the right, where a user can turn on and off different comparison lines to gain further understanding. In a nutshell, I had to find a way to demonstrate that platform looks at both historical trends as well as the most relevant comparable segments that are listed out in order from the most relevant and least relevant (and capped at 10).
#Anomaly 2 ui design how to
In the pictures above, the lofi wireframe on the left hand side demonstrated an early idea as to how to visualize how the platform works and why something could be considered anomalous. Customize the service to detect any level of anomaly. Detect spikes, dips, deviations from cyclic patterns, and trend changes through both univariate and multivariate APIs. In this phase, I needed to find a way to showcase exactly WHY a particular segment was anomalous. Anomaly Detector ingests time-series data of all types and selects the best anomaly detection algorithm for your data to ensure high accuracy. Additional UI, design, and readability adjustments. I did one more round of usability testing with 3 users and incorporated their feedback. Graphics:Heavy Artifacts, Minor Artifacts, Missing Textures, Other. Additionally, keyboard users will find content-skip menus available at any time by clicking Alt+2.
#Anomaly 2 ui design crack
After a few brainstorming sketches that didn't quite crack the code, inspiration struck while checking my Gmail account one night! I emulated a similar structure on being able to mark lines as read or unread, with the ability to remove.Īfter testing, I created a high fidelity prototype that showcased the key features and several future features that would be implemented at a later state. One of the main challenges of this phase was creating a UI framework that seemed familiar and made intuitive sense. I mainly tested internally by conducting several rounds of feedback sessions until things became more polished and could put this in front leadership and clients. Rerun the analysis with certain records excludedīehold! Version 3 of a lofi mockup that all subsequent iterations were based on.Ability to view all the underlying records in an anomaly and mark them as viewed and / or remove them from analysis.ThreatStream automates collection and curation of premium and open-source global intelligence from structured and unstructured data, normalizes it across sources, enriches it with actor, campaign, and TTP information, then de-duplicates it and removes false positives using our patented machine learning algorithm. View list of anomalies with an anomaly score (how anomalous it is), the segment (the combinations of dimension values), the expected and actual values of what happened, and the magnitude of the anomaly. Precision attack detection to cut through the noise.There were several other features that came along for the ride, but these were the ones required for MVP:
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