Botnet Detection Using Netflow and Passive DNS Data
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Solution Overview
Problem
Botnets employ DNS techniques such as Fast-Flux and Domain Generation Algorithms to evade detection, making it difficult for existing systems to accurately identify and distinguish malicious activities.
Innovation Solution
A system and method utilizing Netflow data and passive DNS data to generate features, assign weights, and apply a supervised machine learning algorithm to determine the likelihood of a computer being part of a botnet, incorporating domain registration data and idiosyncratic scores to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used, then the system is simple to operate, but the detection precision deteriorates due to botnet evasion techniques
Solution Approach 1:
The patent combines multiple data sources (Netflow data, passive DNS data, domain registration data) and multiple analysis techniques (feature extraction, probability generation, machine learning) into an integrated detection system. This merging of diverse elements enables accurate botnet detection despite the complexity introduced by combining multiple data streams and analytical methods.
Solution Approach 2:
The patent introduces several intermediary components including a feature extraction module that transforms raw data into meaningful features, a probability generation module that quantifies botnet likelihood, and a machine learning algorithm that serves as an intelligent mediator between data and detection results. These intermediaries enable precise detection while managing system complexity through modular architecture.
2Reliability
If botnets use Fast-Flux and DGAs to evade detection, then the botnets can maintain operational security, but the detection precision of existing systems deteriorates
Solution Approach 1:
The patent moves detection from traditional single-dimension approaches to multi-dimensional analysis by incorporating temporal patterns (time-based features), domain registration information (registration date, age), and probabilistic assessments. This dimensional expansion enables detection of Fast-Flux and DGA techniques that operate successfully in traditional detection spaces.
Solution Approach 2:
The patent performs preliminary analysis by pre-processing Netflow and DNS data, extracting relevant features before actual detection occurs. Domain registration data is collected and analyzed in advance, and probability scores are generated beforehand to prepare the system for accurate real-time detection of botnet activities using evasion techniques.
3Measurement precision
If more data sources and analysis methods are integrated, then the detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent segments the detection process into distinct modular components: data collection module, feature extraction module, probability generation module, and machine learning classification module. This segmentation enables parallel processing of different data streams and analytical tasks, reducing overall processing time while maintaining high detection accuracy through coordinated module operation.
Data Source
AI summary
In one example, a system includes a processor, memory, and a botnet detection application stored in memory and executed by the processor and configured to: obtain (i) Netflow data indicating one or more IP addresses accessed by a computer and (ii) passive Domain Name System (DNS) data indicating respective one or more domains associated with each of the one or more IP addresses; generate features associated with the computer based on the Netflow data and passive DNS data; generate probability data based on the Netflow data and passive DNS data, wherein the probability data indicates a probability that the computer accessed the one or more domains; assign weights to the features based on the probability data to provide weighted features; and determine whether the computer is likely to be part of a botnet based on the weighted features.