Machine Learning DNS Detection to Prevent Server Overload
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Solution Overview
Problem
Malicious users can attack computer networks by overwhelming DNS servers with large numbers of requests or transmitting fake domain names, which can overwhelm the DNS system and negatively impact network performance.
Innovation Solution
Implement a network traffic management system using a machine learning model, such as a neural network, to analyze DNS requests and detect potentially malicious requests, blocking them before they affect the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a DNS server processes all incoming DNS requests, then network connectivity is maintained, but the system becomes overwhelmed by malicious requests and experiences degraded performance
Solution Approach 1:
The system performs preliminary analysis of DNS requests using machine learning models before processing them fully. The ML model predicts whether a request is malicious based on features extracted from the request, allowing the system to preemptively block harmful requests and reserve processing capacity for legitimate traffic.
Solution Approach 2:
The patent introduces an intermediary layer between the DNS server and incoming requests - a machine learning-based detection system that acts as a filter. This intermediary analyzes request characteristics and makes decisions about which requests to allow or block, protecting the DNS server from direct exposure to malicious traffic.
2Adaptability or versatility
If traditional DNS servers process all requests without filtering, then all legitimate requests are served, but malicious requests can overwhelm the system
Solution Approach 1:
The patent replaces traditional mechanical filtering methods (such as simple blacklists or static rules) with machine learning-based detection. The ML model learns patterns from legitimate and malicious requests, enabling adaptive detection that can identify sophisticated attacks without manual rule configuration.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model continuously learns from new data about malicious requests. By analyzing the outcomes of blocked requests and updating its models, the system improves its ability to detect evolving attack patterns while maintaining high accuracy in allowing legitimate traffic.
Data Source
AI summary
Technologies related to malicious DNS request detection are disclosed. A DNS server can use a machine learning model to analyze DNS requests and to detect requests that are potentially malicious. The machine learning model can comprise a neural network (such as a convolutional neural network) that is trained using a corpus of known malicious and non-malicious DNS requests. Data included in a DNS request can be provided as input to a machine learning algorithm (such as a neural network algorithm) that uses the input data and the machine learning model to generate a prediction of whether the DNS request is malicious. If the DNS request is determined to likely be malicious then the request can be blocked (for example by providing a fake address in response to the DNS request). If the DNS request is determined to likely be non-malicious, then the DNS request can be allowed.