IP Address Validation Using Machine Learning and Deep Packet Inspection
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Solution Overview
Problem
Deep packet inspection (DPI) is an expensive and resource-intensive method for validating IP addresses, limiting its application to a small number of potentially malicious IP addresses due to scalability and cost constraints.
Innovation Solution
The use of machine learning applications to analyze network flow traffic and identify potentially malicious IP addresses, followed by deep packet inspection on a curated subset of these addresses, allows for a more efficient and scalable validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection is used to validate IP addresses, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent segments the IP address validation process into multiple stages: initial filtering using lightweight methods (connection state analysis, port validation), intermediate screening using heuristic rules and machine learning models, and final deep packet inspection only for suspicious addresses. This multi-tiered segmentation allows the system to maintain high validation accuracy while processing large volumes of IP addresses efficiently.
Solution Approach 2:
The patent applies partial deep packet inspection rather than exhaustive inspection to all IP addresses. By using preliminary filtering mechanisms to identify only the most suspicious addresses, the system performs deep packet inspection on a small subset (partial action) while using lighter validation methods for the majority, thereby resolving the contradiction between validation precision and processing throughput.
2Reliability
If deep packet inspection is used to validate IP addresses, then reliability is improved, but use of energy worsens
Solution Approach 1:
The patent performs preliminary actions using low-cost validation methods before committing to energy-intensive deep packet inspection. Connection state verification, port availability checks, and machine learning-based suspiciousness scoring are executed first to pre-filter the candidate set, ensuring that expensive DPI resources are only consumed when truly necessary for reliable validation.
Solution Approach 2:
The patent introduces intermediary validation layers (heuristic rules, machine learning models, connection state analysis) that act as mediators between the initial IP address pool and deep packet inspection. These intermediaries screen and prioritize addresses, reducing the burden on energy-intensive DPI while maintaining validation reliability through multiple layers of verification.
3Use of energy by moving object
If a small number of IP addresses are selected for deep packet inspection, then use of energy is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where results from preliminary validation stages inform subsequent inspection depth. Addresses flagged as suspicious by connection state analysis or machine learning models receive deeper inspection, while clean addresses are quickly dismissed. This feedback-driven adaptive inspection ensures high detection precision for malicious addresses while minimizing energy consumption on benign traffic.
Solution Approach 2:
The patent dynamically changes inspection parameters (depth, intensity, methods applied) based on the suspiciousness score assigned to each IP address. High-scoring addresses undergo full deep packet inspection with maximum parameter intensity, while low-scoring addresses receive minimal validation, optimizing the balance between detection precision and resource consumption.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.