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About 2019-A Bi-Objective Hyper-Heuristic Support Vector Machines For Big Data Cyber - Security
This document summarizes a research paper that proposes a novel bi-objective hyper-heuristic framework for optimizing support vector machine (SVM) configurations for big data cybersecurity problems. The framework formulates SVM configuration as a bi-objective optimization problem that considers both accuracy and model complexity. It uses a high-level strategy and low-level heuristics to adaptively search the SVM configuration space and approximate the Pareto set of optimal configurations. The framework is evaluated on two cybersecurity tasks: malware detection and intrusion detection. Results show the framework is effective on these big data problems compared to other algorithms.
Detailed Information
| Author: | ['kota naik'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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