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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi
Hazna At Thooriqoh
Institut Teknologi Sepuluh Nopember
Computer Science & IT Dentistry Education Library & Information Science

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SELENIUM FRAMEWORK FOR WEB AUTOMATION TESTING: A SYSTEMATIC LITERATURE REVIEW Hazna At Thooriqoh; Tiara Nur Annisa; Umi Laili Yuhana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1021

Abstract

Software Testing plays a crucial role in making high-quality products. The process of manual testing is often inaccurate, unreliable, and needed more than automation testing. One of these tools, Selenium, is an open-source framework that used along with different programming languages: (python, ruby, java, PHP, c#, etc.) to automate the test cases of web applications. The purpose of this study is to summarize the research in the area of selenium automation testing to benefit the readers in designing and delivering automated software testing with Selenium. We conducted the standard systematic literature review method employing a manual search of 2408 papers, and applying a set of inclusion/exclusion criteria the final literature included 16 papers published between 2009 and 2020. The result is using Selenium as a UI for web automation, not only all of the app functionality that has been tested, But also it can be applied with added some method or other algorithms like data mining, artificial intelligence, and machine learning. Furthermore, it can be implemented for security testing. In the future research for selenium framework automation testing, the implementation should more focus on finding effective and maintainability on the application of Selenium in other methodologies and is applied with the better improvement that can be matched for web automation testing.
MALICIOUS TRAFFIC DETECTION IN DNS INFRASTRUCTURE USING DECISION TREE ALGORITHM Hazna At Thooriqoh; M. Naufal Azzmi; Yoga Ari Tofan; Ary Mazharuddin Shiddiqi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1054

Abstract

Domain Name System (DNS) is an essential component in internet infrastructure to direct domains to IP addresses or conversely. Despite its important role in delivering internet services, attackers often use DNS as a bridge to breach a system. A DNS traffic analysis system is needed for early detection of attacks. However, the available security tools still have many shortcomings, for example broken authentication, sensitive data exposure, injection, etc. This research uses DNS analysis to develop anomaly-based techniques to detect malicious traffic on the DNS infrastructure. To do this, We look for network features that characterize DNS traffic. Features obtained will then be processed using the Decision Tree algorithm to classifyincoming DNS traffic. We experimented with 2.291.024 data traffic data matches the characteristics of BotNet and normal traffic. By dividing the data into 80% training and 20% testing data, our experimental results showed high detection aacuracy (96.36%) indicating the robustness of our method.
Co-Authors Ary Mazharuddin Shiddiqi M. Naufal Azzmi Tiara Nur Annisa Yoga Ari Tofan Yuhana, Umi Laili