Traffic Pattern Analysis for Infectious Message Detection
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Solution Overview
Problem
Current anti-virus systems are ineffective during the 'time zero' window when a new virus emerges, as they rely on pre-existing virus signatures, leading to vulnerability and high rates of false positives in detecting infectious messages, especially when attackers disguise executable files or exploit non-traditional file types.
Innovation Solution
Implementing a message analysis system that evaluates individual message characteristics using a combination of tests such as signature matching, file name, character, bit pattern, N-gram, and probabilistic finite state automata tests, along with traffic analysis to classify messages as legitimate, suspicious, or infectious, and prevent the spread of infectious messages by quarantining or deleting them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virus signature-based detection is used, then detection accuracy for known viruses is improved, but vulnerability during time zero window increases
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing traffic patterns before a virus outbreak occurs. It establishes baseline communication patterns, file transfer frequencies, and email volumes during normal operation, enabling detection of anomalies that indicate emerging threats without requiring pre-existing virus signatures.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network traffic and comparing current patterns against historical baselines. When deviations exceed thresholds, the system generates alerts and can automatically respond by blocking suspicious traffic, creating a closed-loop system that adapts to new threats in real-time.
2Reliability
If content filter is used to identify executable attachments, then time zero detection capability is improved, but false positive rate increases
Solution Approach 1:
The system segments the detection process into multiple independent analysis components: traffic volume analysis, pattern matching, statistical deviation detection, and behavioral analysis. Each component evaluates specific aspects of network traffic separately, and only when multiple segments indicate suspicion does the system flag a message, reducing false positives while maintaining detection capability.
Solution Approach 2:
The system dynamically adjusts detection parameters such as threshold values, analysis windows, and sensitivity levels based on current network conditions and historical data. During normal operation, thresholds are set to minimize false positives; during suspected outbreak periods, sensitivity increases to catch emerging threats, allowing the system to adapt its detection criteria rather than using fixed rules.
3Productivity
If attachment nature is disguised, then virus transmission is improved, but detection accuracy deteriorates
Solution Approach 1:
The system introduces intermediary analysis layers that examine the relationship between message metadata, attachment properties, and communication patterns. Rather than relying solely on file extensions or content signatures, it analyzes the intermediary context such as sender-recipient relationships, timing patterns, and network flow characteristics to detect disguised threats.
Solution Approach 2:
The system adds another dimension to detection by moving from static file analysis to dynamic behavioral analysis. Instead of examining only the attachment's inherent properties, it analyzes the attachment in the context of network traffic flows, communication patterns, and temporal sequences, detecting threats through their behavioral signature rather than their apparent form.
Data Source
AI summary
Managing electronic messages comprises receiving a message, forwarding the message, determining that the forwarded message is infectious after the message has been forwarded and preventing the infectious forwarded message from spreading.