Adaptive Traffic Sniffing for Multi-Channel Threat Detection
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Solution Overview
Problem
Existing network security measures, such as firewalls and secure communication channels, are inadequate in protecting against cyberattacks that exploit combinations of ports or protocols not filtered by the firewall, and secure channels do not protect against attacks using other channels.
Innovation Solution
Implementing a brokered network traffic sniffing framework with adaptive sniffers that dynamically adjust settings based on configuration inputs and traffic analysis, allowing monitoring at multiple network ingress or egress points without breaking security, and providing selective packet capture and analysis using a statistical protocol for improved network traffic telemetry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If firewalls are used to filter network traffic, then network security is improved, but attacks using combinations of ports or protocols not filtered by the firewall can still penetrate the network
Solution Approach 1:
The patent implements dynamic traffic analysis that adapts to changing attack patterns. The system continuously monitors network traffic and automatically adjusts filtering rules based on detected threats, moving from static firewall configurations to dynamic, responsive security measures that can identify and block sophisticated multi-protocol attacks.
Solution Approach 2:
The system changes multiple parameters simultaneously including port combinations, protocol types, traffic volume, and timing patterns to identify attacks. By analyzing combinations of these parameters rather than single fixed rules, the system detects threats that evade traditional firewall filtering.
2Reliability
If secure communication channels are established, then point-to-point security is improved, but attacks using other channels can still compromise the network
Solution Approach 1:
The patent creates a universal monitoring system that protects across multiple communication channels simultaneously. Rather than relying on individual secure channels, the system monitors all network traffic including encrypted channels, detecting attacks that attempt to bypass established secure communications through alternative paths.
Solution Approach 2:
The system introduces an intermediary monitoring layer that observes traffic between users and their secure channels. This intermediary can detect anomalies and potential attacks on secure channels without breaking the encryption, providing an additional layer of security that complements point-to-point secure communication.
3Reliability
If network traffic monitoring is implemented, then security threat detection is improved, but privacy intrusions increase
Solution Approach 1:
The system extracts only the specific information needed for security analysis from network traffic, such as metadata about communication patterns and anomaly indicators, while leaving the actual content of private communications untouched. This selective extraction enables threat detection without requiring comprehensive inspection of all user data.
Solution Approach 2:
The system uses feedback mechanisms to adjust monitoring intensity based on detected threats. During normal operation, minimal monitoring preserves privacy, but when suspicious patterns are detected, the system intensifies analysis only in relevant areas, balancing privacy protection with security needs dynamically.
Data Source
AI summary
The subject matter described herein provides technical solutions for technical problems facing computing network security. Technical solutions described herein include adaptive sniffing of networking traffic, such as using a brokered network traffic sniffing framework. A brokered sniffing framework may be used to provide dynamic adjustment of network access points and network traffic sampling queries, such as by providing dynamic adjustment in response to changes to the network topology or network traffic. The brokered sniffing framework may provide improved statistical sampling of network traffic using improved network traffic telemetry, such as by modifying a statistical profile of network traffic contents that are collected. The network traffic telemetry may be used to identify various changes in network traffic, such as by identifying statistically significant changes in latencies, bandwidths, or other data center performance metrics.