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

• Main Author: Ascan, Adrian Giovanni G.
• Format: text
• Published: Animo Repository 2022
• Subjects: Intrusion detection systems (Computer security); Computer security; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/9107; https://animorepository.dlsu.edu.ph/context/faculty_research/article/9101/viewcontent/Hiring_of_Mr._Adrian_Giovanni_Ascan.pdf
• Institution: De La Salle University

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.