Time-Series Anomaly Detection for Secure Edge Model Updates
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Solution Overview
Problem
Existing computing systems face inefficiencies in detecting and adapting to anomalies in time-series data, particularly in edge environments, leading to potential propagation of malicious models and data corruption during attacks like ransomware.
Innovation Solution
Implementing a multivariate time-series based machine learning model for anomaly detection that creates 'back-in-time' snapshots to protect data and prevent model updates from corrupted data, using federated learning to ensure data integrity and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection methods are used in edge environments, then the system can operate with simpler architecture, but the system becomes vulnerable to malicious model propagation and data corruption
Solution Approach 1:
The system performs preliminary anomaly detection on time-series data before model updates are applied to edge devices. By detecting anomalies in advance using a machine learning model that analyzes communication data patterns, the system prevents malicious models from being deployed, thereby maintaining data integrity without requiring complex post-deployment verification mechanisms
Solution Approach 2:
A centralized server acts as an intermediary between model sources and edge devices. The server hosts the machine learning model that detects anomalies in communication data, serving as a gatekeeper that filters out malicious models before they reach edge devices, thus simplifying the edge device architecture while enhancing overall system reliability
2Reliability
If real-time anomaly detection is implemented, then the system can prevent malicious model propagation, but the detection process requires extensive computation on time-series data
Solution Approach 1:
The anomaly detection process is segmented into two parts: a lightweight local component on edge devices that collects and pre-processes time-series communication data, and a more computationally intensive machine learning model that runs on a centralized server. This segmentation allows real-time detection with reduced energy consumption at the edge while maintaining high detection accuracy through server-side processing
Solution Approach 2:
The system applies partial anomaly detection by focusing on specific critical attributes of communication data (such as data size, frequency, and pattern changes) rather than analyzing all possible features. This selective approach maintains high detection accuracy for malicious models while reducing the computational energy required for the detection process
3Adaptability or versatility
If the machine learning model continuously updates with new data, then the model adapts to changing conditions, but the system risks incorporating corrupted data from attacks
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model continuously monitors anomalies in communication data and uses this information to dynamically adjust its updating behavior. When anomalies are detected, the feedback loop prevents model updates from incorporating potentially corrupted data, while still allowing adaptation when data is verified clean, thus balancing adaptability with reliability
Solution Approach 2:
Before the model updates with new communication data, the system performs preliminary anomaly detection to identify and block corrupted data. This preliminary anti-action prevents malicious or corrupted data from entering the model training process, allowing the model to adapt to legitimate changing conditions while protecting against data corruption from attacks
Data Source
AI summary
Systems and methods for time-series based machine learning anomaly detection and prevention are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, where the memory includes program instructions store thereon that, upon execution by the processor, cause the IHS to: obtain communication data associated with the IHS for a plurality of time windows, including a particular time window, and previous time windows before the particular time window; determine, using a machine learning model, that the communication data for the particular time window includes an anomaly; and based on the determination, perform one or more actions. In some embodiments, the program instructions further cause the IHS to: based on the communication data, determine time-series data for a plurality of attributes of the communication data; and determine that an attribute includes an outlier in the particular time window.