SDN Controller Threat Treatment Model
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Solution Overview
Problem
Modern data centers face increasing cyber threats such as Denial of Service (DOS) attacks, spoofing, and packet eavesdropping, which existing security measures struggle to address effectively in a timely and efficient manner.
Innovation Solution
A software-defined networking (SDN) controller generates optimal threat treatment processes based on historical threat treatment information using machine-learning techniques, creating a threat treatment model that guides users in resolving similar threats, and continuously refines these processes for improved effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to detect and respond to cyber threats, then basic threat detection capability is maintained, but the response time and effectiveness are insufficient against increasing cyber threats
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical threat treatment information before new threats occur. It builds a knowledge base of effective treatments and uses machine learning to predict optimal responses, so when a threat is detected, the system can immediately apply pre-analyzed treatment strategies rather than starting from scratch.
Solution Approach 2:
The system implements feedback by continuously monitoring threat treatment outcomes and using this information to refine its machine learning models. The controller evaluates whether applied treatments were effective and feeds this information back into the system to improve future threat response decisions, creating a continuously improving security system.
2Adaptability or versatility
If manual threat analysis and treatment process development is performed, then customized security responses can be created, but the time and effort required to resolve threats increases significantly
Solution Approach 1:
The system enables self-service by automatically generating treatment processes using machine learning algorithms that analyze historical data. Instead of requiring security personnel to manually develop response strategies, the system autonomously creates customized treatment plans based on patterns learned from previous threat resolutions, significantly reducing human effort while maintaining adaptability.
Solution Approach 2:
The system replaces manual mechanical analysis with automated machine learning processes. The controller uses computational algorithms to analyze threat patterns and generate treatment strategies, substituting human cognitive processes with automated computational systems that can process information faster and scale more efficiently.
3Measurement precision
If historical threat treatment information is collected and analyzed using machine-learning technologies, then optimal treatment processes can be generated, but the complexity of the security system increases
Solution Approach 1:
The system uses an SDN controller as an intermediary between the complex machine learning models and the actual network security devices. The controller handles the complexity of data collection, analysis, and model training, while presenting simplified treatment commands to network devices, thereby isolating complexity in a manageable component.
Solution Approach 2:
The SDN controller serves multiple functions: it acts as a data collection point, a machine learning processing unit, a treatment generation engine, and a communication hub between security devices and network infrastructure. This multi-functionality consolidates complexity into a single universal component rather than requiring separate specialized systems for each function.
Data Source
AI summary
Techniques are disclosed for providing dynamic threat treatment for a software defined networking (SDN) environment. In one example, a software defined networking controller comprises one or more processors, wherein the one or more processors are configured to: determine that a security device of a network has detected a threat; apply the threat to a threat treatment model, wherein the threat treatment model is generated based on threat treatment information that includes one or more steps used to resolve previous instances of the threat or previous instances of similar threats; and generate one or more treatment processes to resolve the threat based on the threat treatment model.