Active Cyber Defense System for Real-Time Malicious Traffic Detection
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Solution Overview
Problem
Current methods for detecting and blocking network-based cyber attacks are inadequate, as they fail to provide sufficient protection against modern threats, are limited to specific platforms and operating systems, and struggle with real-time detection and adaptation to changing threats, particularly due to reliance on signature-based detection and sandbox technologies that can be evaded by polymorphic malware and rootkits.
Innovation Solution
An Active Cyber Defense System utilizing machine learning classifiers for real-time inspection of network traffic, capable of detecting and blocking malicious activities across various platforms and operating systems, employing Natural Language Processing and Visual Analysis to classify potentially malicious traffic sessions and automatically block malicious communications, while also extracting forensic data covertly to identify malicious servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection and sandbox technologies are used, then protection against certain types of malware is improved, but the system becomes vulnerable to polymorphic malware and rootkits that can evade detection
Solution Approach 1:
The patent implements dynamic analysis by executing suspicious files in a sandbox environment and monitoring their runtime behavior. This dynamic approach allows the system to detect polymorphic malware that changes its code structure, as the malware's actual behavior is observed during execution rather than relying on static code analysis. The system adapts to new threats by learning from observed behaviors and updating detection models in real-time.
Solution Approach 2:
The patent replaces traditional signature-based mechanical detection with machine learning-based behavioral analysis. Instead of matching fixed patterns, the system uses supervised learning algorithms to classify malware based on observed behaviors, enabling detection of previously unseen polymorphic threats that do not match known signatures.
2Speed
If real-time network traffic inspection is performed, then detection speed is improved, but system resource consumption increases
Solution Approach 1:
The patent segments network traffic analysis into multiple stages: initial filtering, suspicious pattern identification, and detailed sandbox analysis. By dividing the inspection process, the system可以快速处理大部分正常流量而不消耗过多资源，仅对可疑流量进行深度分析。
Solution Approach 2:
The patent applies partial analysis to all traffic and excessive (full sandbox) analysis only to suspicious cases. This selective approach ensures real-time performance for common traffic while maintaining high detection accuracy for threats, optimizing the balance between speed and resource usage.
3Reliability
If sandbox-based web MPS is used, then protection against certain malware is improved, but the system cannot detect attacks delivered through encrypted protocols or social engineering
Solution Approach 1:
The patent creates a universal detection system that combines multiple detection methods: sandbox execution for malware, SSL/TLS inspection for encrypted traffic, and natural language processing for social engineering. This multi-functional approach enables the system to detect diverse attack types including encrypted C2 communications and phishing attempts that single-method systems miss.
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between network traffic and the sandbox environment. This intermediary performs preliminary analysis including protocol identification, encryption detection, and behavioral pattern recognition, preparing traffic for appropriate sandbox analysis while detecting attacks that wouldn't reach the sandbox in the first place.
4Reliability
If next generation firewalls are deployed, then network security is improved, but the system lacks the ability to detect and block sophisticated cyber attacks in real-time
Solution Approach 1:
The patent implements continuous feedback loops where sandbox analysis results, machine learning classifications, and threat intelligence are fed back into the detection system in real-time. This feedback mechanism enables the system to adapt to new threats immediately and improve detection accuracy continuously, allowing sophisticated attack detection without compromising real-time response capability.
Data Source
AI summary
An Active Cyber Defense method and system is provided for detecting and stopping malicious cyber activity including for example Drive-By Exploits, Malicious Binaries, Data Exfiltration, Social Engineering and Credential Stealing Attacks. The system disclosed herein can be configured to detect and block multi protocol network-based cyber attacks targeting different platforms or operating systems. The system can also be configured to be scalable. The system as disclosed herein can conduct real time inspection of network traffic and can self-learn and adapt as needed to a changing cyber threat landscape.