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Artificial Intelligence in Road Traffic Accident Prediction Siswanto, Joko; Syaban, Alfath Satria Negara; Hariani, Hariani
Jambura Journal of Informatics VOL 5, NO 2: OCTOBER 2023
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v5i2.22037

Abstract

The rapid development of AI shows its power and great development potential in practical engineering applications. Critical issues and potential solutions can reduce road traffic accidents and application of AI in road accident prediction. The published use of AI for road accident prediction is reviewed, presented, and represented as the main objective. The methods are collecting article data, quotations, presentation, and representation. The article data collection was obtained from 671 conference and journal articles in 2019-2023, but the suitability of articles that can be used is 69. Quotation produces a grouping of approaches, models, predictions, and benefits. The presentation showed that most approaches used were machine learning, the most used model was random forest, the prediction was mostly about severity, and the most benefit was about number reduction. Representation produces road accidents and related factors into factors in road accident predictions using artificial intelligence, so strategies and anticipation can be made to overcome them to improve road safety. AI in road accident prediction plays an important role in building predictive models with the hope that road accidents can be identified early, risk factors can be reduced, and effective preventive measures can be taken to improve road safety.
Investigating LOLBAS-Based Malware Using Hybrid Analysis: A Case Study of PowerShell-Driven Fileless Execution Rosmiati, Rosmiati; Amar, Muh. Ikhsan; Arsyad, Muhammad Arham; Hariani, Hariani
Journal of System and Computer Engineering Vol 7 No 2 (2026): JSCE: April 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i2.2623

Abstract

This study aims to identify and understand the technical characteristics of the malware output.exe, obtained from the MalwareBazaar repository, through a hybrid reverse engineering approach. This method combines static and dynamic analyses to provide a comprehensive understanding of the malware’s internal structure, execution behavior, and evasion techniques. Static analysis revealed the invocation of system functions such as CreateProcessW and RegSetValueExA, as well as the use of syscall to execute PowerShell commands directly, indicating the implementation of the LOLBAS (Living off the Land Binaries and Scripts) technique. Dynamic analysis using CAPE Sandbox confirmed the malware’s actual behavior, including process injection into legitimate processes such as svchost.exe, launching powershell.exe for data compression, and establishing network communication via Discord Webhook for data exfiltration. Integration of both analyses shows that output.exe functions as an information stealer with fileless execution and advanced persistence mechanisms. These findings demonstrate that the hybrid analysis approach is effective in identifying modern malware that leverages legitimate system components to evade traditional signature-based detection methods.