Title

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

Date of Publication

1-2022

Document Type

Master's Thesis

Degree Name

Master in Information Security

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Technology

Thesis Advisor

Marnel Peradilla

Defense Panel Chair

Arlyn Verina Ong

Defense Panel Member

Katrina Ysabel Solomon
Fritz Kevin Flores

Abstract/Summary

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.

Abstract Format

html

Language

English

Format

Electronic

Physical Description

52 leaves

Keywords

Intrusion detection systems (Computer security); Computer security

Upload Full Text

wf_yes

Embargo Period

2-7-2022

This document is currently not available here.

Share

COinS