Journal of ICT Research and Applications https://journals.itb.ac.id/index.php/jictra <p><img class="imgdesc" src="https://lppm.itb.ac.id/wp-content/uploads/sites/55/2021/08/JICTRA_ITB_small.png" alt="" /></p> <p style="text-align: justify;"><em>Journal of ICT Research and Applications</em> welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless &amp; Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.</p> <p style="text-align: justify;">Abstracts and articles published on Journal of ICT Research and Applications are available online at ITB Journal and indexed by <a href="https://www.scopus.com/sourceid/21100268428?origin=resultslist">Scopus</a>, <a href="https://scholar.google.co.id/citations?user=kv2tyQIAAAAJ&amp;hl=id">Google Scholar</a>, <a href="https://doaj.org/toc/2338-5499?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22index.issn.exact%22%3A%222338-5499%22%7D%7D%2C%7B%22term%22%3A%7B%22_type%22%3A%22article%22%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22match_all%22%3A%7B%7D%7D%7D%7D%2C%22from%22%3A0%2C%22size%22%3A100%7D">Directory of Open Access Journals</a>, <a href="https://rzblx1.uni-regensburg.de/ezeit/detail.phtml?bibid=AAAAA&amp;colors=7&amp;lang=en&amp;jour_id=167017">Electronic Library University of Regensburg</a>, <a href="https://atoz.ebsco.com/Titles/SearchResults/8623?SearchType=Contains&amp;Find=journal+of+ICT+RESEARCH+AND+APPLICATIONS&amp;GetResourcesBy=QuickSearch&amp;resourceTypeName=allTitles&amp;resourceType=&amp;radioButtonChanged=">EBSCO Open Science Directory</a>, <a href="https://iamcr.org/open-access-journals">International Association for Media and Communication Research (IAMCR)</a>, <a href="https://miar.ub.edu/issn/2337-5787">MIAR: Information Matrix for the Analysis of Journals Universitat de Barcelona</a>, Cabells Directories, <a href="https://www.jdb.uzh.ch/id/eprint/18193/">Zurich Open Repository and Archive Journal Database</a>, <a href="https://oaji.net/journal-detail.html?number=4569">Open Academic Journals Index</a>, Indonesian Publication Index and ISJD-Indonesian Institute of Sciences. The journal is under reviewed by Compendex, Engineering Village.</p> <p>ISSN: <a href="https://issn.brin.go.id/terbit/detail/1356660436">2337-5787</a> E-ISSN: <a href="https://issn.brin.go.id/terbit/detail/1372766667">2338-5499</a></p> <p>Reg. No. 691-SIC-UPPGT-SIT-1963, <a title="Accreditation Certificate" href="https://drive.google.com/file/d/1z2S39iDtp_BBRbz9JaCWSaShvACA7qGg/view?usp=share_link">Accreditation No. 164/E/KPT/2021</a></p> <p>Published by the Directorate for Research and Community Services, Institut Teknologi Bandung.</p> <p><span style="text-decoration: underline;"><strong>Publication History</strong></span></p> <p><strong>Formerly known as:</strong></p> <ul> <li>ITB Journal of Information and Communication Technology (2007 - 2012)</li> </ul> <p>Back issues can be read online at: https://journal.itb.ac.id</p> <p><span style="text-decoration: underline;"><strong>Scimago Journal Ranking</strong></span></p> <p><a title="SCImago Journal &amp; Country Rank" href="https://www.scimagojr.com/journalsearch.php?q=21100268428&amp;tip=sid&amp;clean=0"><img src="https://www.scimagojr.com/journal_img.php?id=21100268428&amp;title=true" alt="SCImago Journal &amp; Country Rank" border="0" /></a></p> en-US jictra@itb.ac.id (Dr. tech. Wikan Danar Sunindyo, S.T., M.Sc. ) jictra@itb.ac.id (Yuliah Qotimah, SSos., MT.) Fri, 28 Feb 2025 09:51:57 +0700 OJS 3.2.1.0 http://blogs.law.harvard.edu/tech/rss 60 Securing IoT-Cloud Applications with AQ-KGMO-DMG Enhanced SVM for Intrusion Detection https://journals.itb.ac.id/index.php/jictra/article/view/22396 <p>In contemporary society, the Internet has evolved into an indispensable facet of daily life, serving myriad functions across various domains. Intrusion detection, as a cornerstone of information security, plays a pivotal role in fortifying networks against potential threats, emphasizing the necessity for robust and reliable methods capable of discerning and mitigating network vulnerabilities effectively. In this work, a pioneering network intrusion detection model is introduced, leveraging Adaptive Quantum-Inspired KGMO with Dynamic Molecular Grouping (AQ-KGMO-DMG) for feature selection and employing Simplified Support Vector Machines (SVM) for the classification of intrusion data. The utilization of the UNSW-NB15 dataset serves as the litmus test for evaluating the efficacy of the developed intrusion detection model. Notably, this approach enhances the accuracy in categorizing classes with minimal instances while concurrently mitigating the false alarm rate (FAR). A notable innovation in this methodology involves the transformation of raw traffic vector data into a visual representation, thereby reducing computational costs significantly. To reduce the computation cost further, the raw traffic vector data is converted into picture format. The experimental findings showed that the proposed model performed better than conventional techniques in terms of FAR, accuracy, and computation cost.</p> Konduru Siva Naga Narasimharao, P. V. Lakshmi Copyright (c) 2025 Journal of ICT Research and Applications https://journals.itb.ac.id/index.php/jictra/article/view/22396 Fri, 28 Feb 2025 00:00:00 +0700 Enhancing Security of Databases through Anomaly Detection in Structured Workloads https://journals.itb.ac.id/index.php/jictra/article/view/23386 <p>In today’s world, the protection of databases in any global organization has become paramount due to the rapid growth of data and the new generations of cyber threats. This highlights the need for more enhanced security precautions to secure these databases containing sensitive information. One of the most advanced ways of enhancing database security is using an anomaly detection system, especially for structured workloads. Structured workloads typically exhibit predictable patterns of data access and usage, making them susceptible to displaying anomalies that may indicate unauthorized access, data manipulation, or other security breaches. Anomaly detection methods can identify patterns that are unusual, an indication of malicious activity, or a data security breach. The present research utilized the Isolation Forest algorithm to detect outliers in high-dimensional data sets. The main contribution and novelty of this research lies in leveraging the Isolation Forest algorithm for structured database workloads to proactively identify and mitigate potential security threats. Our study showed that the proposed model, with an accuracy of 85%, outperformed various state-of-the-art methods. Furthermore, anomaly detection systems powered by advanced algorithms and machine learning enable real-time database activities analysis, addressing challenges like preprocessing, model training and scalability.</p> Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh , Kushagra Anil Kumar Singh Copyright (c) 2025 Journal of ICT Research and Applications https://journals.itb.ac.id/index.php/jictra/article/view/23386 Fri, 28 Feb 2025 00:00:00 +0700