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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Wulandhari, Lili Ayu
Bina Nusantara University
Computer Science & IT Electrical & Electronics Engineering

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Malware Classification Using Machine Learning and Dimension Reduction Techniques on PE File Data Pradipta, Arif Harsa; Wulandhari, Lili Ayu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5657

Abstract

The digital transformation has enhanced efficiency, transparency, and accessibility but has also led to a notable increase in cyber incidents, including malware attacks. According to the 2022 annual report from the Honeynet Project by the National Cyber and Encryption Agency, Indonesia experienced over 370 million cyber attacks, with 800,000 of these being malware attacks. The increasing complexity of Portable Executable files further complicates accurate classification in machine learning models. This research aims to develop an effective malware detection approach using machine learning classifiers—Random Forest, XGBoost, and AdaBoost—on raw feature dataset and integrated feature dataset. Dimension reduction techniques such as Principal Component Analysis and Linear Discriminant Analysis were utilized to enhance classification efficiency. The results demonstrated that Random Forest and XGBoost consistently outperformed AdaBoost, particularly in classifying ransomware, achieving recall values ranging from 0.72 to 0.85 and F1-scores from 0.74 to 0.81 For the trojan class, both Random Forest and XGBoost achieved recall values ranging from 0.96 to 0.97, with corresponding F1-scores between 0.95 and 0.97. Both classifiers maintained high precision, recall, and F1-scores across all malware classes, even with reduced feature sets.
Cyber Security Threat Prediction using Time-Series Data With LSTM Algorithms Hakim, Lukman; Wulandhari, Lili Ayu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5648

Abstract

Cyber security remains a paramount concern in the digital era, with organizations and individuals increasingly vulnerable to sophisticated cyber-attacks. This study aims to develop and evaluate Long Short-Term Memory (LSTM) regression models to predict three types of cyber attacks: flood, spyware, and vulnerability. The LSTM algorithm is used to construct regression models for spyware, flood, and vulnerabilities within a firewall log dataset. The experiments demonstrate that preprocessing techniques such as normalization and standardization can positively impact model performance by reducing prediction errors and enhancing accuracy. The results of the experiments show that the model developed in this research exhibits potential in predicting cyber attacks. For the flood attack model, the best performance was achieved with an RMSE of 59.8810 and an R-Squared of 0.9214 after data standardization. The spyware attack model's best results were an RMSE of 133.9567 and an R-Squared of 0.7685 after standardization. In contrast, the vulnerability attack model showed limited improvement, with the best RMSE of 503.5521 and an R-Squared of 0.2358 after standardization. Moreover, real-time implementation and testing of these models in live network environments could validate their practical applicability and effectiveness.
Co-Authors Hakim, Lukman Pradipta, Arif Harsa