Machine Learning Deep Packet Inspection for Non-Standard Traffic
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Solution Overview
Problem
Existing cybersecurity systems struggle to effectively detect and mitigate cyber threats in networks with non-standard configurations and protocols, leading to false positives and negatives due to reliance on hardcoded assumptions and static port configurations.
Innovation Solution
The DPD manager employs a DPD ML engine to adaptively parse IT network traffic by learning the actual port configurations and protocols used in a network through unsupervised machine learning, performing deep packet inspection to identify both standard and non-standard setups, and utilizing an RDA ML module to analyze remote desktop activity for potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardcoded assumptions and static port configurations are used for network traffic analysis, then the system structure is simple and easy to implement, but the system cannot detect non-standard configurations and protocols, leading to false positives and negatives
Solution Approach 1:
The patent implements dynamic port configuration detection by using machine learning models that adapt to the actual network environment. Instead of relying on static, hardcoded port configurations, the system dynamically learns and identifies port mappings based on observed network traffic patterns and service behaviors, allowing it to accurately detect both standard and non-standard configurations.
Solution Approach 2:
The system performs self-learning by automatically analyzing network traffic to discover port configurations and protocols without requiring manual configuration or predefined rules. The machine learning models train themselves on observed data to identify patterns and characteristics of network services, enabling the system to adapt to new and non-standard configurations autonomously.
2Reliability
If standard protocol assumptions are made for network traffic, then the analysis process is fast and efficient, but the system fails to identify non-standard protocols and configurations
Solution Approach 1:
The system performs preliminary learning by training machine learning models on network traffic data before actual threat detection. This preliminary action enables the system to pre-establish knowledge about port configurations and protocols specific to the network environment, so that during actual analysis, the system can quickly recognize patterns without needing to perform exhaustive protocol validation for each packet.
Solution Approach 2:
The system changes the approach from fixed protocol parameters to adaptive parameters learned from data. Instead of assuming standard protocol behaviors, the system learns actual protocol implementations and variations present in the network, adjusting its detection parameters based on observed traffic patterns to accurately identify both standard and non-standard configurations.
3Reliability
If deep packet inspection is performed on all network traffic, then threat detection capability is enhanced, but system resource consumption and processing overhead increase significantly
Solution Approach 1:
The patent applies deep packet inspection selectively rather than uniformly across all traffic. The machine learning models identify suspicious traffic patterns and target only those specific flows for intensive inspection, while allowing standard, clean traffic to pass through with minimal processing. This localized application of deep inspection maintains high detection capability for threats while preserving overall network throughput.
Solution Approach 2:
The system performs partial deep packet inspection by applying analysis at appropriate depths based on risk assessment. For low-risk traffic, minimal inspection is performed, while for suspicious traffic identified by the machine learning models, more extensive inspection is applied. This partial action approach balances detection capability with performance, avoiding the excessive resource consumption of universal deep inspection.
Data Source
AI summary
The DPD manager adaptively parses IT network traffic with a DPD ML engine based upon determining a port configuration setting in a network server in an IT network and a protocol utilized by IT network traffic. The DPD manager can detect a non-standard configuration set up for IT network traffic to be processed by a port on the network server, a non-standard protocol utilized by the IT network traffic, and any combination of both, and then completes a deep packet inspection upon the IT network traffic that has the non-standard configuration set up and/or the non-standard protocol utilized by the IT network traffic.