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All Journal JUITA : Jurnal Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Abdul Fadlil
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Computer Science & IT

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OWASP Framework-based Network Forensics to Analyze the SQLi Attacks on Web Servers Imam Riadi; Abdul Fadlil; Muhammad Amirul Mu'min
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3018

Abstract

One of dangerous vulnerabilities that attack the web is SQLi. With this vulnerability, someone can obtain user data information, then change and delete that data. The solution to this attack problem is that the design website must improve security by paying attention to input validation and installing a firewall. This study's objective is to use network forensic tools to examine the designlink website's security against SQLi attacks, namely Whois, SSL Scan, Nmap, OWASP Zap, and SQL Map. OWASP is the framework that is employed; it is utilized for web security testing. According to the research findings, there are 14 vulnerabilities in the design website, with five medium level, seven low level, and two informational level. When using SQL commands with the SQL Map tool to get username and password information on its web server design. The OWASP framework may be used to verify the security of websites against SQLi attacks using network forensic tools, according to the study's findings. So that information about the vulnerabilities found on the website can be provided. The results of this study contribute to forensic network knowledge against SQLi attacks using the OWASP framework as well as for parties involved in website security.
MAC Address Classification in Privacy Issue Using Gaussian Naïve Bayes Imam Riadi; Abdul Fadlil; Basit Adhi Prabowo
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.22571

Abstract

There have been several initiatives within standards committees to overcome privacy issues, including user tracking activity based on Media Access Control (MAC) addresses. The implementation of randomized MAC addresses on captive portals, with user-specific connection limits to address privacy concerns, introduces some problems. To address this issue, device removal based on OUI classification was proposed. Connection data taken from the RADIUS server were divided into two distinct classes, either random or not. Gaussian Naïve Bayes was utilized to classify the data with 16 distinct thresholds, and the solution with the highest accuracy was selected. The research produced results showing that all classifications had an accuracy above 96%. Values of 6 and 50% for Mac address thresholds and random percentage thresholds gave the highest accuracy of 98.1139%. This indicates that random Mac address classification in the real world can be done using the result.
Co-Authors Basit Adhi Prabowo Imam Riadi Muhammad Amirul Mu'min Muhammad Amirul Mu'min