Network Traffic Detection Service for Bot and C2 Activity
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Solution Overview
Problem
Existing cybersecurity systems struggle to effectively detect command-and-control (C2) attacks on web applications due to the conflation of malicious C2 traffic with normal network traffic, particularly on ports 80 and 443, which are commonly used for Hypertext Transfer Protocol (HTTP) and HTTP Secure (HTTPS), making it difficult to distinguish between benign and malicious activities.
Innovation Solution
A detection service that analyzes network traffic data from web applications, such as social media platforms, using multi-protocol analysis rules to identify bot and C2 activity by examining DNS, SSL/TLS, and HTTP traffic, leveraging expert knowledge and curated data on API endpoints, suspicious patterns, and decryption capabilities to differentiate between benign and malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If C2 attacks use commonly used ports and protocols (HTTP/HTTPS on ports 80/443), then the malicious traffic can easily conflate with normal benign network traffic, but this makes detection of C2 attacks difficult
Solution Approach 1:
The patent segments the analysis of network traffic into multiple independent analysis rules covering different protocols (DNS, SSL/TLS, HTTP). Each rule set examines specific characteristics of traffic for that protocol, allowing the system to differentiate malicious C2 traffic from benign traffic by analyzing multiple segmented aspects rather than relying on a single detection method.
Solution Approach 2:
The patent transitions from traditional single-protocol detection to multi-protocol analysis, adding dimensional depth to traffic examination. By analyzing DNS queries, SSL/TLS handshakes, and HTTP traffic simultaneously, the system creates a multi-dimensional view that reveals C2 attack patterns hidden within normal traffic on ports 80 and 443.
2Measurement precision
If the detection service analyzes multiple communication protocols (DNS, SSL/TLS, HTTP), then the detection capability improves, but the complexity of the detection system increases
Solution Approach 1:
The detection service is segmented into separate analysis rule sets for different protocols (DNS analysis rules, SSL/TLS analysis rules, HTTP analysis rules). Each rule set independently analyzes specific protocol characteristics, making the overall complex system manageable through modular organization and reducing the complexity burden on any single component.
Solution Approach 2:
The detection service is designed as a universal system that handles multiple communication protocols through a unified architecture. The same detection service infrastructure processes DNS, SSL/TLS, and HTTP traffic using different analysis rules, eliminating the need for separate detection systems for each protocol and reducing overall system complexity.
Data Source
AI summary
A service detects bot activity or malicious C2 activity based on data known to be associated with bot and C2 activity for supported web applications and that can be identified from network traffic corresponding to the web application for multiple communication protocols. Such data can include API endpoints known to be associated with C2 activity, malicious users/accounts associated with the web application, and malicious activity patterns. Whether the detection service can detect bot activity that may potentially correspond to C2 activity or malicious C2 activity itself is dependent on whether the obtained network traffic data are decrypted. When network traffic data are encrypted, detection service can detect bot activity by analyzing DNS and SSL/TLS traffic. When network traffic data are decrypted, the detection service can obtain the decrypted network traffic data comprising HTTP traffic and perform further analysis for detection of C2 activity reflected in HTTP traffic.