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All Journal JAIS (Journal of Applied Intelligent System) Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SISFOTENIKA Infotekmesin Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika: Jurnal Pengembangan IT Advance Sustainable Science, Engineering and Technology (ASSET)
Abdussalam Abdussalam
Dian Nuswantoro University
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Lightweight Dual-Layer Chaotic Image Encryption Using Arnold Cat Map and Henon Zigzag Diffusion Chaerul Umam; Abdussalam Abdussalam; Arif Nursetyo; Bambang Sugiarto; Husain Md Mehedul Islam
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3085

Abstract

Digital image transmission over open networks raises significant security concerns due to the high correlation and predictable statistical properties of image data. Existing chaotic encryption schemes based on Arnold Cat Map (ACM) and Henon mapping often suffer from high computational cost, parameter sensitivity, or reliance on complex multi-stage designs. To address these limitations, this study proposes a lightweight dual-layer chaotic image encryption framework that integrates ACM-based pixel permutation with Henon Zigzag diffusion. The first layer applies ACM to disrupt spatial correlations, while the second layer embeds a Henon-based chaotic sequence into a zigzag traversal to enhance both confusion and diffusion. Experimental results demonstrate that the proposed method achieves strong security performance, with an average PSNR of 8.40 dB for cipher images, UACI of 33.67%, NPCR of 99.71%, and near-zero correlation coefficients across RGB channels, while maintaining a low average execution time of 1.80 s. These results indicate that the method produces highly randomized cipher images with strong resistance to statistical and differential attacks. Furthermore, the reduced computational complexity highlights its suitability as a lightweight and efficient solution for secure multimedia transmission in practical digital communication systems.
Deteksi Serangan Denial of Service (DoS) dan Spoofing pada Internet of Vehicles menggunakan Algoritma K-Nearest Neighbor (KNN) Wildanil Ghozi; Fauzi Adi Rafrastara; Ramadhan Rakhmat Sani; 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

Abstract

The implementation of Internet of Things (IoT) technology in motor vehicles has been increasing over time and is known as the Internet of Vehicles (IoV). IoV is becoming more essential to society as it provides comfort, safety, and efficiency in driving. Unfortunately, the use of internet technology in IoV brings the potential for cyber-attacks, such as Denial of Service (DoS) and Spoofing. Intrusion Detection Systems in IoV have not yet fully matured, as this technology is still relatively new. Therefore, the potential threats and their significant impact make research on this topic urgently needed. This study aims to evaluate the performance of the k-Nearest Neighbor (kNN) classification algorithm in detecting cyber-attacks on IoV. The predicted classes in this study consist of six categories: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, and RPM-Spoofing. These two types of attacks on IoV (DoS and Spoofing) pose risks to the operational safety of vehicles, which can endanger drivers and other road users. The dataset used is a public dataset called CIC IoV2024. The performance of the kNN algorithm is also compared to three other state-of-the-art algorithms, including Naïve Bayes, Deep Neural Network, and Random Forest. The results show that k-Nearest Neighbor (kNN) achieved the best performance with a score of 98.7% for both accuracy and F1-Score metrics. kNN outperformed Naïve Bayes, which ranked second with a score of 98.1% accuracy and 98.0% F1-Score. Thus, the kNN algorithm can be recommended as a classifier in the development of an intrusion detection system for IoV
Co-Authors Ajib Susanto Ariel Ramadhan Diva Aretha Putra Retnawan Arif Nursetyo Bambang Sugiarto Bayu Wicaksono Carlita Masaccio Mauren Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Pramudya Elkaf Rahmawan Pramudya Fauzi Adi Rafrastara Husain Md Mehedul Islam Ibnu Utomo Wahyu Mulyono L. Budi Handoko Muslih Muslih Noor Ageng Setiyanto, Noor Ageng Ramadhan Rakhmat Sani Ratih Hafsarah Maharrani Sudaryanto Sudaryanto Widyatmoko Karis Wildanil Ghozi Wildanil Ghozi