IP Traffic Anomaly Detection via Packet Header Aggregation
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Solution Overview
Problem
Current network monitoring tools face challenges in efficiently analyzing vast amounts of packet header data, leading to storage inefficiencies, incomplete data capture, and difficulty in detecting anomalies and network attacks in real-time, especially in complex IP networks.
Innovation Solution
A computer-based method and system that extract unique packet headers from IP networks, analyze them for anomalous conversations based on uniqueness, time, and bandwidth usage, and provide alerts, using a security monitoring infrastructure system (SMIS) to automate the detection and tracking of network threats and multicast data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all packet headers are stored individually in a database, then complete network traffic data is captured, but storage space and analysis time are excessively consumed
Solution Approach 1:
The patent merges multiple identical or similar packet headers into a single consolidated record. Instead of storing each packet header individually, the system combines packets with the same conversation ID, source IP, destination IP, source port, and destination port into one aggregated record, significantly reducing storage requirements while preserving essential network traffic information.
Solution Approach 2:
The patent creates a universal conversation ID that can identify and group multiple packets across different protocols and traffic types. This single identifier enables the system to handle and aggregate diverse packet types (TCP, UDP, ICMP, etc.) using a unified approach, making the storage system more efficient and versatile.
2Quantity of substance
If packet headers are aggregated by conversation, then storage efficiency is improved, but individual packet details may be lost
Solution Approach 1:
The patent creates a simplified copy of packet header information that retains the essential identifying characteristics (source IP, destination IP, ports, protocol type) while omitting redundant data. This copied representation is sufficient for anomaly detection purposes and enables efficient storage without significant information loss.
Solution Approach 2:
The patent extracts only the critical fields from packet headers that are necessary for anomaly detection (conversation identifiers, IP addresses, ports, protocol types) and stores these extracted elements. Non-essential packet details are excluded from storage, reducing data volume while maintaining detection capability.
3Measurement precision
If signature-based intrusion detection is used, then known attack patterns are identified, but novel or subtle attacks are missed
Solution Approach 1:
The patent implements dynamic anomaly detection that adapts to changing network conditions and traffic patterns. Instead of relying on static signature databases, the system continuously learns normal network behavior and dynamically adjusts detection thresholds, enabling it to identify both known and novel attacks by detecting deviations from established baselines.
Solution Approach 2:
The patent performs preliminary baseline establishment by analyzing normal network traffic patterns before attempting to detect attacks. This preliminary action creates a reference model of legitimate network behavior, which is then used to identify anomalies. The system proactively prepares detection criteria rather than reactively matching signatures.
4Measurement precision
If manual log file review is performed, then detailed attack analysis is possible, but real-time detection and response are prevented
Solution Approach 1:
The patent implements automated anomaly detection that performs detailed attack analysis without human intervention. The system automatically compares network traffic against established baselines, identifies deviations, investigates potential attacks, and generates alerts. This self-service capability eliminates the need for manual log review while maintaining detailed analysis quality and enabling real-time response.
Solution Approach 2:
The patent incorporates feedback loops where detection results are continuously refined based on analyst responses and confirmed anomalies. The system learns from positive and negative feedback to improve its detection accuracy over time, adjusting its analysis depth and alert generation based on the nature and severity of detected anomalies.
Data Source
AI summary
A computer-based method for detecting anomalies in the traffic passing through an internet protocol (IP) network is described. The method includes extracting, from a database, a single instance of each unique packet header associated with a plurality of IP-to-IP packets, the IP-to-IP packets having been transmitted across the IP network over a predefined period of time, analyzing the packet headers to identify anomalous conversations based on at least one of a conversation uniqueness, a time of week uniqueness, and a data quantity uniqueness, and providing alerts corresponding to detected anomalous conversations.