Summary of Feature Selection Using the Concept Of Peafowl Mating in Ids, by Partha Ghosh et al.
Feature Selection using the concept of Peafowl Mating in IDS
by Partha Ghosh, Joy Sharma, Nilesh Pandey
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel Intrusion Detection System (IDS) that leverages the mating behavior of peafowl in an optimization algorithm for feature selection. The algorithm is designed to reduce the massive size of cloud data, allowing the IDS to efficiently detect intrusions on cloud-based infrastructure. By incorporating this unique approach, the proposed model demonstrates improved performance and efficiency when tested with NSL-KDD and Kyoto datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-powered computer that can help keep your online information safe from bad guys. That’s basically what this paper is all about. It creates a special system called an Intrusion Detection System (IDS) that can detect when someone tries to hack into the cloud. The IDS uses a clever trick, borrowed from how peafowl birds choose their mates, to help it work more efficiently and accurately. This new approach has been tested on real data sets and shows promise in keeping our online world safer. |
Keywords
* Artificial intelligence * Feature selection * Optimization
