Network Payload Anomaly Detection via N-Gram Statistical Analysis
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Solution Overview
Problem
Conventional systems for detecting malicious data transmissions, such as worms and viruses, rely on signature-based detection methods that are ineffective at the initial appearance of new threats, allowing damage to occur before protection can be implemented, and require complex firewalls to manage numerous signature strings.
Innovation Solution
A statistical analysis method that compares the byte value distribution of network payload data to a model of normal data to identify anomalous transmissions, using techniques like Mahalanobis distance to determine if a payload is potentially harmful, allowing for early detection and prevention of malicious programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection methods are used to detect malicious data transmissions, then detection accuracy for known threats is improved, but the system cannot detect new threats at their initial appearance and requires complex firewall configurations
Solution Approach 1:
The patent transforms the detection approach from signature matching to statistical parameter analysis. Instead of comparing payloads against known signatures, the system analyzes byte value distribution parameters (n-gram frequencies) to detect anomalies. This parameter transformation enables detection of unknown threats while simplifying the firewall rule set, as the system learns normal traffic patterns automatically rather than requiring manual signature configuration.
Solution Approach 2:
The system implements self-learning capability where the firewall automatically builds models of normal traffic patterns without human intervention. The statistical model learns from observed traffic and automatically identifies deviations, eliminating the need for manual signature updates and complex firewall configuration. This self-service approach reduces operational complexity while maintaining high detection accuracy.
2Reliability
If signature-based detection is deployed, then protection against known worms is improved, but damage occurs before protection can be implemented
Solution Approach 1:
The system performs preliminary learning of normal traffic patterns before actual threat detection is needed. By continuously building statistical models of legitimate traffic during normal operation, the firewall is prepared to immediately detect deviations when new threats appear. This preliminary action eliminates the time lag between threat emergence and detection capability, as the system is already equipped with baseline knowledge of normal behavior.
Solution Approach 2:
The system implements continuous feedback loops where detected traffic patterns are fed back into the statistical model for refinement. When anomalies are detected, the system adjusts its understanding of normal versus abnormal behavior, enabling adaptive response to evolving threats. This feedback mechanism ensures the system maintains high reliability while responding rapidly to new threat variants.
3Adaptability or versatility
If multiple signature strings are loaded into firewalls to detect various worms, then detection coverage is improved, but system complexity and processing overhead increase
Solution Approach 1:
The statistical anomaly detection system serves multiple detection functions simultaneously through a single unified mechanism. Rather than requiring separate signature rules for different worm types, the universal statistical model detects all threats that deviate from normal behavior patterns. This multi-functionality approach maintains broad detection coverage while significantly reducing system complexity compared to maintaining multiple specialized signature databases.
Data Source
AI summary
A method, apparatus, and medium are provided for tracing the origin of network transmissions. Connection records are maintained at computer system for storing source and destination addresses. The connection records also maintain a statistical distribution of data corresponding to the data payload being transmitted. The statistical distribution can be compared to that of the connection records in order to identify the sender. The location of the sender can subsequently be determined from the source address stored in the connection record. The process can be repeated multiple times until the location of the original sender has been traced.