Endpoint MITM Detection via Machine Learning and Modular Tests
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Solution Overview
Problem
Complex networked computing environments are susceptible to man-in-the-middle (MITM) attacks, which can go unnoticed for long periods, allowing attackers to intercept sensitive information.
Innovation Solution
A first node in a networked computing environment initiates multiple MITM detection tests, including HTTP compression, interception, authentication, and communication protocol tests, to determine if communications have been intercepted by a third node, utilizing machine learning models to characterize the likelihood of interception and taking corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple MITM detection tests are implemented, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The detection system is divided into multiple independent detection tests (HTTP compression test, HTTP interception test, HTTP authentication test, communication protocol test, gateway fingerprint test, TTY test, mDNS test, update mechanism test). Each test operates as a separate module that can be independently executed and evaluated, allowing the system to achieve high detection reliability through multiple checks while managing complexity through modular design.
Solution Approach 2:
The first node is designed to perform multiple different types of detection tests through a unified architecture. The same node executes various protocols and tests (HTTP, cryptographic hashing, public key comparison, cipher suite verification) using common infrastructure for sending requests, receiving responses, and evaluating results, reducing overall system complexity while maintaining comprehensive detection capabilities.
2Measurement precision
If machine learning models are used for detection, then measurement precision is improved, but use of energy increases
Solution Approach 1:
Machine learning models are trained offline with historical network traffic data before deployment. The training process, which is energy-intensive, is performed in advance rather than during runtime detection. During actual MITM detection, the pre-trained models perform classification with high precision but consume minimal energy, as the heavy computational work has already been completed during the preliminary training phase.
Solution Approach 2:
The machine learning model creates a simplified representation of complex network traffic patterns through training. Instead of performing complex real-time analysis of all network packets, the model learns characteristic patterns during training and then uses these learned patterns for efficient runtime detection, reducing energy consumption while maintaining high detection precision.
Data Source
AI summary
A first node of a networked computing environment initiates each of a plurality of different types of man-in-the middle (MITM) detection tests to determine whether communications between first and second nodes of a computing network are likely to have been subject to an interception or an attempted interception by a third node. Thereafter, it is determined, by the first node, that at least one of the tests indicate that the communications are likely to have been intercepted by a third node. Data is then provided, by the first node, data that characterizes the determination. In some cases, one or more of the MITM detection tests utilizes a machine learning model. Related apparatus, systems, techniques and articles are also described.