A case study on attack detection capabilities between open-source intrusion detection systems

Clicks: 1
ID: 285541
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #2,670 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
As the pandemic hits the world on 2020, most of the employees worldwide are forced to work from home. This gives a way for the attackers to have a higher attack surface which suggests that businesses need to improve their cybersecurity. Having intrusion detection is one way to improve cybersecurity as it plays an important role in catching attacks on an early stage. In contrast as most businesses decline, the budget for their cybersecurity declines as well. Using Open-Source tools for cybersecurity would greatly help these businesses without costing a lot. Suricata and Snort are two of the most used Open-Source Network Intrusion Detection Systems. This study evaluates the detection accuracy and detection rate of the two Intrusion Detection Systems by testing them against CICIDS-2017 Intrusion Dataset and the most common malwares in 2020. This will help the readers to choose which Network Intrusion Detection System best fits their environment.
Reference Key
persistent_1760655906_68f17a2287eca Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ascan, Adrian Giovanni G.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_comtech/1
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.