Histogram Bin Segmentation for Network Anomaly Detection

The patent segments the histogram bins into different groups based on their frequency characteristics. High-frequency bins are further divided into smaller intervals to capture detailed behavior patterns, while low-frequency bins are grouped into larger intervals. This selective segmentation maintains measurement precision for important behaviors while reducing the total number of bins to lower computational burden.

The patent applies different bin width qualities to different regions of the histogram based on local data characteristics. Regions with high frequency values use narrower bins for precise measurement, while regions with low frequency values use wider bins. This local adaptation optimizes the balance between accuracy and computational efficiency by concentrating processing resources where they are most needed.

Instead of uniformly reducing all bins, the patent segments bins by frequency magnitude and applies different reduction strategies. High-frequency bins maintain finer granularity to preserve important behavior patterns, while low-frequency bins are coarsened more aggressively. This selective approach reduces overall computational burden while preserving measurement precision for critical behaviors.

The patent implements local quality by applying different bin widths to different frequency regions. Narrow bins are retained where data variability is high and precision is critical, while wider bins are used where data is more uniform. This ensures that reducing the number of bins does not uniformly degrade precision, but rather optimizes it locally based on data characteristics.

The patent segments the histogram into regions with different bin widths based on the underlying data distribution. Instead of using a single uniform bin width, the histogram is constructed with multiple segments where each segment has bin widths optimized for its local characteristics. This maintains relative simplicity in construction while dramatically improving precision by adapting to data variability.

The patent applies local quality by making bin widths variable rather than uniform. Each region of the histogram uses bin widths tailored to the local data density and variability. This approach maintains ease of construction through systematic methods while achieving superior measurement precision compared to equal-width bins, as the bin structure adapts to capture important behavioral variations.