Network Hop Model Anomaly Detection for Secure Data Transmission
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Solution Overview
Problem
Organizations face challenges in providing improved network security against sophisticated network threats and risks, such as hacking attempts and data breaches, due to increasing connectivity and the evolving nature of cybersecurity threats, which existing technologies struggle to detect and control effectively.
Innovation Solution
A method that utilizes network traffic data to monitor and analyze user behavior, generating a network hop model to identify normal behavior patterns and adjust data transmission operations, employing machine learning algorithms to detect anomalies, and employing IP tunneling and layered encryptions to secure data transmission, thereby controlling communications and preventing unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network monitoring and analysis systems are implemented to detect sophisticated threats, then network security detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments network traffic analysis into multiple components: flow data collection, hop model generation, anomaly detection, and response actions. Each component processes specific aspects of network traffic independently, reducing overall system complexity while maintaining comprehensive threat detection capability through coordinated operation of these segmented modules.
2Speed
If real-time network traffic analysis is performed to detect anomalies, then response time to threats is improved, but computational energy consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-generating hop models from historical flow data and establishing baseline network behavior patterns before threats occur. This allows the anomaly detection component to quickly compare real-time traffic against pre-established models, achieving fast threat response without requiring intensive real-time computational analysis of all traffic parameters.
3Loss of information
If comprehensive network hop modeling is implemented to track data packets, then network visibility and control are improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential hop information from complete network flow data to create condensed hop models. Instead of processing and storing all raw flow data, the system extracts key routing information (source, destination, intermediate hops, timestamps) to build simplified representations that maintain network visibility while significantly reducing data processing and storage requirements.
Data Source
AI summary
A method may include receiving, via a processor, a first set of data packets from a first computing device associated with a user over a first period of time. The method may then involve generating a network hop model indicative of one or more network hops traversed by the first set of data packets. The method may also include receiving a second set of data packets from the first computing device over a second period of time, determining whether the second set of data packets corresponds to the network hop model, and adjusting one or more operations associated with transmitting a third set of data packets to the first computing device in response to determining that the second set of data packets does not match the network hop model.