Dynamic Packet Capture for Anomaly Detection
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Solution Overview
Problem
Current network security measures are inadequate in detecting distributed Denial of Service (DoS) and fast flux service network (FFSN) attacks, which involve complex patterns and dynamic behavior, making it difficult to distinguish malicious traffic from legitimate traffic, especially in low-power and lossy networks (LLNs) and Internet of Things (IoT) environments.
Innovation Solution
A machine learning-based anomaly detection system that dynamically selects packets for deep packet inspection using a capture control mechanism, adjusting criteria based on real-time traffic metrics and deep packet inspection results to identify anomalies, thereby enhancing the precision of anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection is performed on all network traffic, then anomaly detection precision is improved, but network resource consumption and processing time increase
Solution Approach 1:
The system dynamically adjusts packet capture criteria based on real-time network conditions and anomaly detection needs. The packet capture rate is modified from static to dynamic, allowing the system to inspect packets at varying rates depending on traffic patterns and suspected anomaly levels, thus balancing detection precision with processing throughput
Solution Approach 2:
Instead of performing deep packet inspection on all traffic, the system applies partial inspection to selected packets based on capture criteria. This partial action approach focuses inspection resources on packets most likely to contain anomaly information while accepting that not all packets are inspected, maintaining a balance between detection precision and processing capacity
2Reliability
If packet capture rate is increased for better anomaly detection, then detection capability is improved, but network bandwidth consumption and processing load increase
Solution Approach 1:
The system uses feedback from initial packet inspection results and traffic metrics to dynamically adjust the packet capture rate. When anomalies are detected or suspected patterns emerge, the capture rate increases to gather more evidence. When traffic is normal, the rate decreases to conserve bandwidth, creating a closed-loop control system that adapts to actual detection needs
3Adaptability or versatility
If dynamic packet capture criteria are used, then adaptability to different attack patterns is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically adjusting its own packet capture criteria based on observed traffic patterns and detection results. The capture control mechanism uses self-generated feedback from the anomaly detection process to modify its behavior, eliminating the need for external manual configuration and reducing operational complexity despite the dynamic nature of the system
Data Source
AI summary
In one embodiment, a device in a network captures a first set of packets based on first packet capture criterion. The captured first set of packets is provided for deep packet inspection and anomaly detection. The device receives a second packet capture criterion that differs from the first packet capture criterion. The device captures a second set of packets based on the second packet capture criterion. The device provides the captured second set of packets for deep packet inspection and anomaly detection. The anomaly detection of the captured first and second sets of packets is performed by a machine learning-based anomaly detector configured to generate anomaly detection results based in part on one or more traffic metrics gathered from the network and based further in part on deep packet inspection results of packets captured in the network.