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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences Journal of Nonformal Education Telematika Jurnal Transformatika JUITA : Jurnal Informatika Jurnal The Messenger Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Pengembangan Rekayasa dan Teknologi KOMPUTIKA - Jurnal Sistem Komputer RANDANG TANA - Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Jurnal Teknologi Informasi dan Komunikasi Information Science and Library Bulletin of Computer Science Research Jurnal Teknologi Informasi Cyberku SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Tematik International Journal of Information Technology and Business Jurnal DIMASTIK
April Firman Daru
Universitas Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Transportation Other

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Journal : International Journal of Electrical and Computer Engineering

 1 
A new approach of scalable traffic capture model with Pi cluster Kristoko Dwi Hartomo; April Firman Daru; Hindriyanto Dwi Purnomo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2186-2196

Abstract

The development of the internet of things (IoT), which functions as servers, device monitors, and controllers of several peripherals inside the smart home, eased workload in many sectors. Most devices are accessible through the internet because they communicate with wired or wireless interfaces. However, this feature makes them prone to the risk of being exposed to the public. The exposed devices are an easy target for the third party to launch a flooding attack through the network. This attack overloads the system due to the low processing capability, thereby interrupting any running process and harming the device. Therefore, this study proposed a scalable network capturing model that utilized multiple Raspberry Pi boards in parallel to monitor the network traffics simultaneously. An isolated experiment was used for evaluation by running simultaneous flooding attacks on each device. The result showed that the model consumed 30.44% more memory with 14.66% lower central processing unit (CPU) usage and 3.63% faster execution time. This means that this model is better in terms of performance and effectiveness than the single capture model.
IPv6 flood attack detection based on epsilon greedy optimized Q learning in single board computer April Firman Daru; Kristoko Dwi Hartomo; Hindriyanto Dwi Purnomo
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5782-5791

Abstract

Internet of things is a technology that allows communication between devices within a network. Since this technology depends on a network to communicate, the vulnerability of the exposed devices increased significantly. Furthermore, the use of internet protocol version 6 (IPv6) as the successor to internet protocol version 4 (IPv4) as a communication protocol constituted a significant problem for the network. Hence, this protocol was exploitable for flooding attacks in the IPv6 network. As a countermeasure against the flood, this study designed an IPv6 flood attack detection by using epsilon greedy optimized Q learning algorithm. According to the evaluation, the agent with epsilon 0.1 could reach 98% of accuracy and 11,550 rewards compared to the other agents. When compared to control models, the agent is also the most accurate compared to other algorithms followed by neural network (NN), K-nearest neighbors (KNN), decision tree (DT), naive Bayes (NB), and support vector machine (SVM). Besides that, the agent used more than 99% of a single central processing unit (CPU). Hence, the agent will not hinder internet of things (IoT) devices with multiple processors. Thus, we concluded that the proposed agent has high accuracy and feasibility in a single board computer (SBC).
Co-Authors Agus Hartanto Alauddin Maulana Hirzan Alaudin Maulana Hirzan Amri, Saeful Aria Hendrawan, Aria Arif Kurniawan Ashadi, Slamet Astrid Novita Putri, Astrid Atmoko Nugroho Basworo Ardi Pramono Bilqisth, Shona Chayy Cahyono, Agus Edy Cholil, Saifur Rohman Christioko, B. Very Darmawan, Heddy Tyas Didin Herlinudinkhaji, Didin Dyan Sinung Prabowo Edi Widodo Edi Widodo, Edi Erlangga, Erwin Febrian Wahyu Christanto Galet Guntoro Setiaji Handayani, Sri Hanif, Muhammad Burhan Hindriyanto Dwi Purnomo Irwan Desyantoro Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Kristoko Dwi Hartomo Lenny Margaretta Huizen Mohammad Burhan Hanif Muhammad Bahrul Ulum Nilasari Yunantara, Chandra nofiati, nofiati Nurtriana Hidayati Panata, Helmi Prasetio Pratama, Dominicus Ferdian Wendy Putriniantami, Bertha Sonia Rachman, Farizal Rastri Prathivi Saifur Rohman Cholil Sara Sharifzadeh Stefanus Santosa Sugiarti, Rini Surono Surono Susanto Susanto Vensy Vydia Veronika, Nofita Whisnumurti Adhiwibowo Widodo, Muhammad Faizal Agung