Traffic Simulation for Malicious Activity Detection
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Solution Overview
Problem
Current systems lack effective methods to detect and analyze malicious activity within computer networks, providing insufficient evidence for infection identification and understanding the severity of threats, and struggle to discover precursors and implications of such activity.
Innovation Solution
The system monitors network traffic, captures and indexes metadata, compares it to simulated data, and reexamines historical data to identify malicious activity, determine the probability of maliciousness, and automate the detection of suspicious patterns, using techniques like deep packet inspection and domain reputation scoring to annotate infection cycles and severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current detection systems are used to monitor network traffic, then basic malicious activity detection is achieved, but detection precision and evidence quality are insufficient
Solution Approach 1:
The patent segments the detection process into multiple independent analysis components: traffic monitoring, metadata capture, simulation comparison, and historical data reexamination. Each component processes specific aspects of network traffic separately, then integrates results to achieve comprehensive detection with high precision and reliable evidence.
Solution Approach 2:
The patent introduces simulated data as an intermediary between actual network traffic and detection analysis. By comparing captured metadata against simulated malicious traffic patterns, the system enhances detection precision and generates reliable evidence without directly exposing the actual network to test vectors.
2Reliability
If comprehensive network traffic monitoring is implemented, then malicious activity detection capability is improved, but system complexity increases
Solution Approach 1:
The system divides complex network monitoring into modular functional segments: traffic capture module, metadata extraction module, simulation comparison module, and historical analysis module. Each module performs a specific function independently, reducing overall system complexity while maintaining comprehensive detection capability.
Solution Approach 2:
The patent uses simulated copies of malicious traffic patterns instead of analyzing every actual network packet in detail. By creating and comparing against simulated attack scenarios, the system achieves reliable malicious activity detection without processing the full complexity of real-time network traffic.
3Reliability
If historical network data is reexamined to discover precursors, then detection capability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary capture and indexing of network metadata during normal operations, organizing data for future analysis. When detection is needed, the system reexamines pre-processed historical data rather than analyzing raw traffic from scratch, significantly reducing processing time while maintaining detection capability.
Solution Approach 2:
The system replaces time-intensive manual analysis of historical network data with automated computational methods. By using algorithms to efficiently search and compare indexed metadata against known malicious patterns, the system achieves reliable precursor detection without the time penalty of manual review.
Data Source
AI summary
Systems and methods may simulate traffic to identify malicious activity. A dynamic analysis system comprising a processor in communication with a network may receive a copy of a malware program and load the copy of the malware program into a simulated endpoint. The system may monitor simulated endpoint network traffic to or from the simulated endpoint, assess the simulated endpoint network traffic to determine a source and/or destination for the simulated endpoint network traffic and/or content of the simulated endpoint network traffic, and capture and store metadata associated with the simulated endpoint network traffic. A comparison system may compare simulated network traffic metadata to observed network traffic metadata to determine whether the metadata are statistically similar. When the metadata are not statistically similar, the system may generate a low infection confidence score. When the metadata are statistically similar, the system may generate a high infection confidence score.