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Ramot Argenta Pasaribu, Fabian
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Computer Science & IT

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ANALISIS PERFORMA INTRUSION DETECTION SYSTEM SNORT DAN SURICATA TERHADAP SERANGAN SQL INJECTION Ramot Argenta Pasaribu, Fabian; Maslan, Andi
Computer Science and Industrial Engineering Vol 13 No 2 (2025): Comasie Vol 13 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v13i2.10403

Abstract

Web application security is becoming increasingly important due to the rise of threats such as SQL Injection, which exploits vulnerabilities to access sensitive data. As one of the most severe types of attacks, SQL Injection compromises the confidentiality, integrity, and access control of a system. Intrusion Detection Systems such as Snort and Suricata are used to detect and mitigate this. This study compares the effectiveness of Snort and Suricata in detecting SQL Injection using an experimental setup. The vulnerable web application (DVWA) was installed on Ubuntu, while attacks were launched from Kali Linux. Both IDS tools were configured to monitor network traffic and detect intrusions based on predefined rules. Performance was evaluated using accuracy, precision, recall, and F1 score. Suricata outperformed Snort in all metrics, Suricata also demonstrated faster detection. These results indicate that Suricata is more accurate and efficient at detecting SQL injection attacks in the test environment.
Co-Authors Maslan, Andi