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All Journal ELKHA : Jurnal Teknik Elektro Semantik Proceeding SENDI_U JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Pelatihan Basic Cyber Security untuk Siswa SMA/Sederajat di Kabupaten Batang Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 3 (2024): SEPTEMBER 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i3.2470

Abstract

Teknologi informasi telah menjadi bagian dari kehidupan masyarakat modern dengan pertumbuhan pengguna yang pesat. Setiap orang menggunakan gadget untuk berkomunikasi dan mengakses berbagai informasi setiap hari. Kejahatan siber merupakan salah satu dampak negatif yang paling berbahaya yang menargetkan para pengguna teknologi informasi dan dapat menargetkan individu menjadi korban. Pemerintah memiliki tanggung jawab untuk melindungi masyarakat terhadap kejahatan siber yang menargetkan individu. Oleh karena itu, pemerintah perlu memahami berbagai skema serangan dan trend perkembangan kejahatan siber. Dengan pengetahuan skema-skema serangan yang mungkin digunakan pada kejahatan siber, maka pemerintah dapat memberikan edukasi yang tepat bagi masyarakat. Balai Pengembangan Sumber Daya Manusia dan Penelitian (BPSDMP) Komunikasi dan Informatika Yogyakarta merupakan salah satu lembaga pemerintah yang bertanggung jawab dalam meningkatkan kemampuan masyarakat dalam pemanfaatan teknologi di wilayah Jawa Tengah dan Yogyakarta termasuk di Kabupaten Batang. Universitas Dian Nuswantoro sebagai institusi pendidikan yang unggul dalam bidang teknologi informasi dan komunikasi, berkolaborasi dengan BPSDMP KOMINFO Yogyakarta untuk memberikan pelatihan basic cyber security untuk siswa SMA/sederajat di Kabupaten Batang. Pelatihan berupa penjelasan materi teori dan praktik implementasi keamanan siber. Pada akhir sesi pelatihan, sebanyak 37 dari 39 siswa peserta pelatihan dinyatakan berhasil.
Random Under Sampling for Performance Improvement in Attack Detection on Internet of Vehicles Using Machine Learning Anargya, Muhammad Alden Nayef; Ghozi, Wildanil; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.8034

Abstract

The Internet of Vehicles (IoV) technology is one of the advancements derived from the Internet of Things (IoT) in the transportation sector, benefiting its users. However, the development of this technology cannot be separated from various security threats, particularly Denial of Service (DoS) and spoofing attacks. Given these threats, it is crucial to continuously develop methods used for detecting attacks on IoV systems. Several researchers have conducted research related to attacks and threats on IoV systems, and one such study resulted in a dataset called CICIoV2024. This dataset has an imbalanced class distribution. This study aims to examine the implementation of Random Under-Sampling to improve the performance of classification algorithms in detecting attacks on IoV systems. The algorithms used in this study include Decision Tree, K-Nearest Neighbors (KNN), and Random Forest. The test results show that the Random Forest algorithm achieved the best results with an accuracy of 98.5% and an F1-Score of 98.5%.
Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection Rosyada, Salma; Rafrastara, Fauzi Adi; Ramadhani, Arsabilla; Ghozi, Wildanil; Yassin, Warusia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2293

Abstract

The increasing prevalence of malware poses significant risks, including data loss and unauthorized access. These threats manifest in various forms, such as viruses, Trojans, worms, and ransomware. Each continually evolves to exploit system vulnerabilities. Ransomware has seen a particularly rapid increase, as evidenced by the devastating WannaCry attack of 2017 which crippled critical infrastructure and caused immense economic damage. Due to their heavy reliance on signature-based techniques, traditional anti-malware solutions struggle to keep pace with malware's evolving nature. However, these techniques face limitations, as even slight code modifications can allow malware to evade detection. Consequently, this highlights weaknesses in current cybersecurity defenses and underscores the need for more sophisticated detection methods. To address these challenges, this study proposes an enhanced malware detection approach utilizing Extreme Gradient Boosting (XGBoost) in conjunction with Chi-Squared Feature Selection. The research applied XGBoost to a malware dataset and implemented preprocessing steps such as class balancing and feature scaling. Furthermore, the incorporation of Chi-Squared Feature Selection improved the model's accuracy from 99.1% to 99.2% and reduced testing time by 89.28%, demonstrating its efficacy and efficiency. These results confirm that prioritizing relevant features enhances both the accuracy and computational speed of the model. Ultimately, combining feature selection with machine learning techniques proves effective in addressing modern malware detection challenges, not only enhancing accuracy but also expediting processing times.             
Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset Latifah, Ines Aulia; Rafrastara, Fauzi Adi; Bintoro, Jevan; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2294

Abstract

Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or polymorphic malware. Therefore, advanced malware detection methods are increasingly needed to counter these evolving threats. This study aims to compare the performance of various feature selection methods combined with the XGBoost algorithm for malware detection using the Drebin dataset, and to identify the best feature selection method to enhance accuracy and efficiency. The experimental results show that XGBoost with the Information Gain method achieves the highest accuracy of 98.7%, with faster training times than other methods like Chi-Squared and ANOVA, which each achieved an accuracy of 98.3%. Information Gain yielded the best performance in accuracy and training time efficiency, while Chi-Squared and ANOVA offered competitive but slightly lower results. This study highlights that appropriate feature selection within machine learning algorithms can significantly improve malware detection accuracy, potentially aiding in real-world cybersecurity applications to prevent harmful cyberattacks.
Deteksi Serangan Denial of Service (DoS) dan Spoofing pada Internet of Vehicles menggunakan Algoritma k-Nearest Neighbor (kNN) Ghozi, Wildanil; Rafrastara, Fauzi Adi; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11309

Abstract

Implementasi teknologi Internet of Things pada kendaraan bermotor mengalami peningkatan dari waktu ke waktu dan dikenal dengan istilah Internet of Vehicle (IoV). IoV semakin dibutuhkan masyarakat karena dapat menghadirkan kenyamanan, keamanan, dan efisiensi dalam berkendara. Sayangnya, penggunaan teknologi internet pada IoV justru memunculkan potensi serangan siber, seperti Denial of Service (DoS) dan Spoofing. Intrusion Detection System pada IoV belum sepenuhnya berjalan dengan baik mengingat teknologi ini juga tergolong baru. Oleh karena itu, dengan adanya potensi ancaman sekaligus dampak yang dihasilkan menjadikan penelitian tentang hal ini menjadi urgent untuk dilakukan. Penelitian ini bertujuan untuk mengevaluasi performa algoritma machine learning k-Nearest Neighbor (kNN) dalam mendeteksi serangan siber pada IoV. Kelas yang diprediksi pada penelitian ini berjumlah enam, yaitu: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, dan RPMSpoofing. Dua jenis serangan pada IoV tersebut (DoS dan Spoofing) beresiko menghadirkan gangguan operasional pada kendaraan yang dapat membahayakan pengemudi dan pengguna jalan lainnya. Dataset yang digunakan adalah dataset publik bernama CIC IoV2024. Performa algoritma kNN tersebut juga dibandingkan dengan tiga algoritma lain sebagai state-of-the-arts, seperti Naïve Bayes, Deep Neural Network, dan Random Forest. Hasilnya, k-Nearest Neighbor (kNN) mendapatkan performa terbaik dengan skor 98.7% untuk metrik akurasi maupun F1- Score. kNN mengungguli Naïve Bayes yang berada di urutan ke-dua, dengan skor 98.1% untuk akurasi dan 98.0% untuk F1-Score. Selanjutnya, algoritma kNN dapat direkomendasikan sebagai classifier dalam pengembangan intrusion detection system pada IoV.
Model Hybrid Random Forest dan Information Gain untuk Meningkatkan Performa Algoritma Machine Learning pada Deteksi Malicious Software Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat; Handoko, L. Budi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i2.11216

Abstract

Evolusi malware atau perangkat lunak berbahaya semakin meningkatkan kekhawatiran, menyerang tidak hanya komputer tetapi juga perangkat lain seperti smartphone. Malware kini tidak hanya berbentuk monomorfik, tetapi telah berkembang menjadi bentuk polimorfik, metamorfik, hingga oligomorfik. Dengan perkembangan massif ini, perangkat lunak antivirus konvensional tidak akan mampu mengatasinya dengan baik. Hal ini disebabkan oleh kemampuan malware untuk menyebarkan dirinya dengan pola sidik jari dan perilaku yang berbeda. Oleh karena itu, diperlukan antivirus cerdas berbasis machine learning yang mampu mendeteksi malware berdasarkan perilaku bukan sidik jari. Penelitian ini berfokus pada implementasi model machine learning dalam deteksi malware dengan menggunakan algoritma ensemble dan seleksi fitur untuk mencapai kinerja yang baik. Algoritma ensemble yang digunakan adalah Random Forest, dievaluasi dan dibandingkan dengan k-Nearest Neighbor dan Decision Tree sebagai state-of-the-art. Untuk meningkatkan kinerja klasifikasi dalam hal kecepatan proses, metode seleksi fitur yang diterapkan adalah Information Gain dengan 22 fitur. Hasil tertinggi dicapai dengan menggunakan algoritma Random Forest dan metode seleksi fitur Information Gain, mencapai skor 99.0% untuk akurasi dan F1-Score. Dengan mengurangi jumlah fitur, kecepatan pemrosesan dapat ditingkatkan hingga hampir 5 kali lipat.
OPTIMIZING ANDROID MALWARE DETECTION USING NEURAL NETWORKS AND FEATURE SELECTION METHOD Bintoro, Jevan; Rafrastara, Fauzi Adi; Latifah, Ines Aulia; Ghozi, Wildani; Yassin, Warusia
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3898

Abstract

Malware poses a serious threat to Android security systems. In recent years, Android malware has rapidly evolved, employing obfuscation techniques such as polymorphic and metamorphic. Unfortunately, signature-based malware detection cannot identify modern variants of Android malware. This study aims to compare various feature selection methods and machine learning algorithms to identify the most effective and efficient combination for classifying Android malware. The dataset used in this research is the Drebin dataset. Four classification algorithms are used in this comparison: Naive Bayes, Logistic Regression, Neural Network, and Random Forest. The best-performing algorithm is then implemented in three different scenarios: without feature selection, with Information Gain, and with Chi-Squared (X²). In the latter two scenarios, the appropriate number of features was selected using the backward elimination method. Both feature selections achieved the same performance, but Information Gain required fewer features. The evaluation metrics used in this study include AUC, accuracy, F1-score, training time, and testing time. Measuring training and testing time benefits the model by making it more efficient, thus allowing for faster detection in real-world applications. The results show that the combination of the Information Gain feature selection method and the Neural Network algorithm achieves the highest performance, with an accuracy and F1-Score of 98.6%. Additionally, this combination achieves a training time of 81.135 seconds and a testing time of 1.095 seconds. Compared to the Neural Network algorithm without feature selection, this combination results in a 17.7597 % reduction in training time and a 57.9977 % reduction in testing time while maintaining the same performance values. This research contributes to improving the speed and accuracy of malware detection systems, enhancing mobile security.
IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING Ramadhani, Arsabilla; Rafrastara, Fauzi Adi; Rosyada, Salma; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3903

Abstract

Malware attacks pose a serious threat to digital systems, potentially causing data and financial losses. The increasing complexity and diversity of malware attack techniques have made traditional detection methods ineffective, thus AI-based approaches are needed to improve the accuracy and efficiency of malware detection, especially for detecting modern malware that uses obfuscation techniques. This study addresses this issue by applying ensemble-based machine learning algorithms to enhance malware detection accuracy. The methodology used involves Random Forest, Gradient Boosting, XGBoost, and AdaBoost, with feature selection using Information Gain. Datasets from VirusTotal and VxHeaven, including both goodware and malware samples. The results show that Gradient Boosting, strengthened with Information Gain, achieved the highest accuracy of 99.1%, indicating a significant improvement in malware detection effectiveness. This study demonstrates that applying Information Gain to Gradient Boosting can improve malware detection accuracy while reducing computational requirements, contributing significantly to the optimization of digital security systems.
Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features Sani, Ramadhan Rakhmat; Rafrastara, Fauzi Adi; Ghozi, Wildanil
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2051

Abstract

The rapid advancement of malware poses a significant threat to devices, like personal computers and mobile phones. One of the most serious threats commonly faced is malicious software, including viruses, worms, trojan horses, and ransomware. Conventional antivirus software is becoming ineffective against the ever-evolving nature of malware, which can now take on various forms like polymorphic, metamorphic, and oligomorphic variants. These advanced malware types can not only replicate and distribute themselves, but also create unique fingerprints for each offspring. To address this challenge, a new generation of antivirus software based on machine learning is needed. This intelligent approach can detect malware based on its behavior, rather than relying on outdated fingerprint-based methods. This study explored the integration of machine learning models for malware detection using various ensemble algorithms and feature selection techniques. The study compared three ensemble algorithms: Gradient Boosting, Random Forest, and AdaBoost. It used Information Gain for feature selection, analyzing 21 features. Additionally, the study employed a public dataset called ‘Malware Static and Dynamic Features VxHeaven and VirusTotal Data Set’, which encompasses both static and dynamic malware features. The results demonstrate that the Gradient Boosting algorithm combined with Information Gain feature selection achieved the highest performance, reaching an accuracy and F1-Score of 99.2%.
Impact of SMOTE for Imbalance Class in DDoS Attack Detection Using Deep Learning MLP Ilma, Zidni; Ghozi, Wildanil; Rafrastara, Fauzi Adi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6727

Abstract

DDoS attacks, which are becoming increasingly complex and frequent, pose significant challenges to network security, particularly with the rise of cyber exploitation of infrastructure. A major issue in detecting these attacks is the imbalance between normal traffic and attack data, which causes machine learning models to be biased toward the majority class. To address this, this study proposes the use of the Synthetic Minority Over-sampling Technique (SMOTE) to balance the CIC-DDoS2019 dataset, successfully enhancing the performance of a Multi-Layer Perceptron (MLP) in detecting various types of attacks. Analysis results indicate that, on the original dataset without SMOTE, the model achieved high accuracy but low F1-Score for minority classes, highlighting difficulties in recognizing underrepresented attack patterns. After applying SMOTE, the F1-Score significantly improved for minority classes, demonstrating the model's enhanced ability to identify attack patterns. All dataset subsets showed improved performance across key evaluation metrics, indicating that SMOTE effectively expanded the model's decision boundary for minority classes, enabling MLP to detect DDoS attacks more accurately in previously challenging data patterns. This approach illustrates increased model sensitivity to minority feature distributions without significantly compromising performance on majority classes.
Co-Authors Abdollah, Mohd. Faizal Abdussalam Abdussalam, Abdussalam Adhitya Nugraha Adhitya Nugraha Ahmed, Foez Al Fahreza, Muhammad Daffa Amalia, Syafira Rosa Amiral, Afinzaki Anargya, Muhammad Alden Nayef Astuti, Yani Parti Bintoro, Jevan Catur Supriyanto Catur Supriyanto Catur Supriyanto Cinantya Paramita Cinantya Paramita Dewi Pergiwati Dwi Eko Waluyo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Firmansyah, Muhammad David Foez Ahmed Ghozi, Wildani Hayu Wikan Kinasih Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ilma, Zidni Kencana, Lisdi Inu L. Budi Handoko Latifah, Ines Aulia Lisdi Inu Kencana Mahendra, Syafrie Naufal Megantara, Rama Aria Nohan, Rajendra Osman, Waleed Mahgoub Paramita, Cinantya Pergiwati, Dewi Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Sudibyo, Usman T. Sutojo Usman Sudibyo Widyatama, Thierry Wildanil Ghozi Yani Parti Astuti Yani Parti Astuti Yassin, Warusia