Enterprise Host Anomaly Detection via Log Clustering
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Solution Overview
Problem
Existing security systems in large enterprises face challenges in detecting suspicious activities due to the manual and inefficient processing of high-volume, heterogeneous security logs from various sources, which are often noisy and lack standardization, making it difficult to identify meaningful security incidents.
Innovation Solution
A behavioral detection method that processes log data from multiple sources, normalizes timestamps and IP addresses, extracts features, and clusters host devices to identify anomalies, using a system with a normalization layer, feature extraction layer, and statistical analysis layer to generate prioritized alerts for security analysts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual forensic analysis of security logs is performed, then detection accuracy can be maintained, but productivity is severely reduced due to high volumes of heterogeneous log data
Solution Approach 1:
The patent introduces an intermediary system comprising automated log normalization, feature extraction, and clustering components that mediate between raw heterogeneous logs and analyst review. This intermediary processing layer handles the bulk of log analysis automatically, enabling high-volume processing while maintaining detection accuracy by pre-filtering and organizing data for targeted human review of suspicious activities.
Solution Approach 2:
The patent segments the log analysis process into distinct automated stages: log normalization (standardizing formats), feature extraction (identifying relevant characteristics), clustering (grouping similar behaviors), and anomaly detection (identifying suspicious patterns). This segmentation enables parallel processing of different log sources and types, dramatically improving productivity while maintaining comprehensive analysis coverage.
2Adaptability or versatility
If multiple security products from different vendors are deployed, then coverage of security threats is improved, but device complexity increases due to heterogeneous log formats and inconsistent administration
Solution Approach 1:
The patent implements a universal log normalization layer that can process logs from multiple different security vendors and product types through a single standardized interface. This normalization component translates diverse log formats into a common structure, enabling the system to handle heterogeneous data sources without requiring separate processing pipelines for each vendor, thus reducing integration complexity while maintaining broad threat coverage.
3Reliability
If traditional signature-based detection is used, then reliability of known threat detection is maintained, but adaptability to new and unknown threats deteriorates
Solution Approach 1:
The patent performs preliminary behavioral baseline establishment by analyzing normal host activities and creating behavioral profiles before threats occur. This preliminary action captures the legitimate behavior patterns of hosts, which then serves as a reference for detecting deviations caused by new or unknown threats. The system continuously updates these baselines, enabling adaptive detection of emerging threats while maintaining reliability through comparison against established normal behavior.
Data Source
AI summary
Methods, apparatus and articles of manufacture for behavioral detection of suspicious host activities in an enterprise are provided herein. A method includes processing log data derived from one or more data sources associated with an enterprise network over a given period of time, wherein the enterprise network comprises multiple host devices; extracting one or more features from said log data on a per host device basis, wherein said extracting comprises: determining a pattern of behavior associated with the multiple host devices based on said processing; and identifying said features representative of host device behavior based on the determined pattern of behavior; clustering the multiple host devices into one or more groups based on said one or more features; and identifying a behavioral anomaly associated with one of the multiple host devices by comparing said host device to the one or more groups across the multiple host devices.