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All Journal Sistemasi: Jurnal Sistem Informasi Cyber Security dan Forensik Digital (CSFD) Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat
Chazar, Chalifa
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Journal : Cyber Security dan Forensik Digital (CSFD)

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Deteksi Serangan Pada Jaringan Internet Of Things Medis Menggunakan Machine Learning Dengan Algoritma XGBoost: Attack Detection On Internet Medical Of Things Using Machine Learning With Xgboost Algorithm Diash Firdaus; Afin, Afin; Sumardi, Idi; Chazar, Chalifa
Cyber Security dan Forensik Digital Vol. 8 No. 1 (2025): Edisi Mei 2025
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/csecurity.2025.8.1.5036

Abstract

Internet of Things (IoT) telah memberikan dampak besar pada sektor kesehatan, memungkinkan pengumpulan data pasien secara real-time dan meningkatkan efisiensi layanan kesehatan. Namun, adopsi perangkat IoT medis juga membawa tantangan baru terkait keamanan, terutama serangan Distributed Denial of Service (DDoS) yang dapat mengganggu layanan kritis. Penelitian ini melakukan deteksi terhadap lima jenis serangan, yaitu ARP Spoofing, Recon Attack, MQTT Attack, TCP/IP DoS, dan DDoS, menggunakan model machine learning dengan algoritma XGBoost. Dataset yang digunakan adalah CICIoMT2024, yang dirancang khusus untuk menilai keamanan perangkat medis terhubung, melibatkan 40 perangkat IoMT. XGBoost menunjukkan performa terbaik dengan akurasi, recall, presisi, dan F1-score yang unggul, mencapai akurasi 99.8%, presisi 92.4%, recall 96%, dan F1-score 93.8%. Sebelumnya, algoritma lain seperti Logistic Regression dan Naive Bayes menunjukkan akurasi masing-masing sebesar 79% dan 92% dalam mendeteksi serangan serupa, hal ini menunjukan keterbatasan dalam menangani pola yang lebih kompleks. Hasil ini menegaskan efektivitas XGBoost dalam mendeteksi ancaman keamanan dalam ekosistem IoT medis, memberikan perlindungan lebih baik terhadap potensi gangguan pada layanan kesehatan kritis. Kata kunci: Machine Learning, Keamanan Siber, xgboost, deteksi, Internet Medical of Things ------------------------- Abstract The Internet of Things (IoT) has significantly impacted the healthcare sector, enabling real-time patient data collection and enhancing service efficiency. However, the adoption of medical IoT devices also introduces new security challenges, particularly Distributed Denial of Service (DDoS) attacks that can disrupt critical services. This study detects five types of attacks: ARP Spoofing, Recon Attack, MQTT Attack, TCP/IP DoS, and DDoS, using machine learning models with the XGBoost algorithm. The dataset used is CICIoMT2024, specifically designed to assess the security of connected medical devices, involving 40 IoMT devices. XGBoost demonstrated the best performance with superior accuracy, recall, precision, and F1-score, achieving 99.8% accuracy, 92.4% precision, 96% recall, and 93.8% F1-score. Previously, other algorithms such as Logistic Regression and Naive Bayes showed accuracies of 79% and 92% respectively in detecting similar attacks, but with limitations in handling more complex patterns. These results underscore the effectiveness of XGBoost in detecting security threats in the medical IoT ecosystem, providing enhanced protection against potential disruptions to critical healthcare services.   Keywords: Machine Learning, Cybersecurity, xgboost, detection, Internet Medical of Things
Image-Based Malware Multiclass Classification Using Vision Transformer Architecture: Multiclass Klasifikasi Malware Berbasis Gambar Menggunakan Vision Transformer Architecture Diash Firdaus; Sumardi, Idi; Chazar, Chalifa; Dafy, Muhamad Zufar
Cyber Security dan Forensik Digital Vol. 8 No. 1 (2025): Edisi Mei 2025
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/csecurity.2025.8.1.5107

Abstract

Perkembangan malware yang semakin canggih telah menjadi ancaman serius bagi keamanan siber global, mengakibatkan kerugian finansial yang signifikan. Metode deteksi tradisional seperti deteksi berbasis tanda tangan dan analisis dinamis memiliki keterbatasan dalam mendeteksi varian malware baru. Sebagai solusi inovatif, analisis malware berbasis gambar mengubah file biner malware menjadi representasi gambar, memanfaatkan pemrosesan citra digital dan pembelajaran mesin untuk identifikasi yang lebih efisien. Penelitian ini menggunakan arsitektur Vision Transformer (ViT) untuk klasifikasi malware multikelas berbasis gambar, menawarkan pendekatan yang lebih efektif dibandingkan CNN tradisional seperti EfficientNet dan VGG16. ViT muncul sebagai pendekatan baru yang menarik karena fleksibilitasnya dalam memahami hubungan objek dalam gambar dan mendeteksi pola penting. Dengan kemampuannya mempelajari hubungan jangka panjang antar data, ViT dapat mendeteksi perbedaan halus antara berbagai jenis malware dan mencapai akurasi lebih tinggi. Dataset yang digunakan adalah Malimg, yang merupakan hasil konversi malware biner menjadi format gambar. Hasil penelitian menunjukkan Vision Transformers mencapai akurasi pelatihan 99.96%, validasi 98.05%, dan pengujian 97.49%, meningkatkan akurasi dibandingkan CNN. Keberhasilan ini menunjukkan kemajuan signifikan dalam akurasi deteksi, mengindikasikan arah menjanjikan untuk penelitian dan aplikasi keamanan siber di masa depan. Studi ini menekankan pentingnya teknik pembelajaran mesin yang canggih untuk meningkatkan deteksi malware. Kata kunci: Vision Transformers, Klasifikasi Malware, Deep learning ------------------------- The increasing sophistication of malware has become a serious threat to global cybersecurity, resulting in significant financial losses for individuals and organizations. Traditional detection methods, such as signature-based detection and dynamic analysis, face limitations in identifying new or modified malware variants. As an innovative solution, image-based malware analysis converts malware binary files into image representations, leveraging digital image processing and machine learning for safer and more efficient identification. This study employs the Vision Transformer (ViT) architecture for multiclass image-based malware classification, offering a more effective approach compared to traditional CNNs. The Vision Transformer (ViT) has emerged as an exciting new approach, gaining attention for its flexibility in understanding object relationships within images and detecting important patterns. ViT, with its ability to learn long-range relationships between data, can detect subtle differences between various types and subtypes of malware, achieving higher classification accuracy. The results of this study show that Vision Transformers achieve the highest training accuracy of 99.96%, the highest validation accuracy of 98.05%, and a testing accuracy of 97.49%. The success of Vision Transformers in malware classification indicates significant advancements in detection accuracy, suggesting a promising direction for future research and applications in cybersecurity. This study underscores the importance of leveraging advanced machine learning techniques to enhance malware detection capabilities Keywords: Vision Transformers, Malware Classification, Deep learning  
Co-Authors Afin, Afin Budiraharjo, Raden Dafy, Muhamad Zufar Diash Firdaus Faa’iz, Prayoga Anwar Idi Sumardi, Idi Jasman Pardede Milenio, Rizka Milandga Radja, Carrisa Adnyana Putri Rawosi, Muhammad Fadlansyah Zikri Akhiruddin Rivano, Kevin Faza Setyaningrum, Anisa Putri Weninggalih, Sanjaya Raga Wijaya, Freza Taruna Nugraha