Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics International Journal of Engineering, Science and Information Technology
Ary Bima Kurniawan
Gunadarma University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Advanced content-based retrieval for digital correspondence documents with ontology classification Rifiana Arief; Suryarini Widodo; Ary Bima Kurniawan; Hustinawaty Hustinawaty; Faisal Arkan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3376

Abstract

The growth of digital correspondence documents with various types, different naming rules, and no sufficient search system complicates the search process with certain content, especially if there are unclassified documents, the search becomes inaccurate and takes a long time. This research proposed archiving method with automatic hierarchical classification and the content-based search method which displays ontology classification information as the solution to the content-based search problems. The method consists of preprocessing (creation of automatic hierarchical classification model using a combination of convolutional neural network (CNN) and regular expression method), archiving (document archiving with automatic classification), and retrieval (content-based search by displaying ontology relationships from the document classification). The archiving of 100 documents using the automatic hierarchical classification was found to be 79% accurate as indicated by the 99% accuracy for CNN and 80% for Regex. Moreover, the search results for classified content-based documents through the display of ontology relationships were discovered to be 100% accurate. This research succeeded in improving the quality of search results for digital correspondence documents as indicated by its higher specificity, accuracy, and speed compared to conventional methods based on file names, annotations, and unclassified content.
Implementation of Intrusion Detection System Using Snort and Log Visualization Using ELK Stack Fatih Dien Robbani; Emy Haryatmi; Tri Agus Riyadi; Riza Adrianti Supono; Ary Bima Kurniawan; Rosdiana Rosdiana
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.901

Abstract

Cyber threats like malware, ransomware, and DDoS attacks demand fast and integrated detection systems. Traditional network monitoring tools often struggle to identify complex real-time attack patterns. This study evaluates the integration of Snort, an Intrusion Detection System (IDS), with the ELK Stack (Elasticsearch, Logstash, Kibana) to detect and visualize cyberattacks effectively. The system was tested against three attack scenarios: a Windows ping flood, port scanning using Zenmap, and SSH brute force attacks via Nmap Scripting Engine (NSE). Wireshark was employed as a supporting tool to monitor raw network traffic. The results indicate that Snort detected all simulated attacks in real time, and the ELK Stack efficiently processed and visualized the alert data. However, limitations in Kibana's dashboard refresh rate slightly hindered real-time monitoring capabilities. Overall, the integration of Snort and the ELK Stack proves effective for network threat detection and analysis, with room for future improvements in visualization performance and automated response mechanisms.
Co-Authors Emy Haryatmi Faisal Arkan Fatih Dien Robbani Hustinawaty Hustinawaty, Hustinawaty Rifiana Arief Riza Adrianti Supono Rosdiana Rosdiana Suryarini Widodo Tri Agus Riyadi