Article Per Year (5 Year)
p-Index From 2020 - 2025
1.503
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science
Eko Arip Winanto
Dinamika Bangsa University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search

 1 
Intrusion detection with deep learning on internet of things heterogeneous network Sharipuddin Sharipuddin; Benni Purnama; Kurniabudi Kurniabudi; Eko Arip Winanto; Deris Stiawan; Darmawijoyo Hanapi; Mohd. Yazid Idris; Rahmat Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp735-742

Abstract

The difficulty of the intrusion detection system in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed deep learning to intrusion detection system identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an initial experiment of denial of services (DoS) attacks and results. The results of the evaluation showed that deep learning can improve detection accuracy in the heterogeneous internet of things (IoT).
Enhanced Deep Learning Intrusion Detection in IoT Heterogeneous Network with Feature Extraction Sharipuddin Sharipuddin; Eko Arip Winanto; Benni Purnama; Kurniabudi Kurniabudi; Deris Stiawan; Darmawijoyo Hanapi; Mohd Yazid bin Idris; Bedine Kerim; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 3: September 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v9i3.3134

Abstract

Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.
Visualisasi Serangan Remote to Local (R2L) Dengan Clustering K-means eko arip winanto
Annual Research Seminar (ARS) Vol 2, No 1 (2016)
Publisher : Annual Research Seminar (ARS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Visualisasi merupakan salah satu teknik untuk meningkatkan akurasi deteksi serangan yang terjadi di jaringan. Visulisasi bertujuan untuk mempermudah dalam mengenali dan menyimpulkan serangan terjadi. Clustering k-means dapat digunakan untuk mendeteksi paket serngan dan paket normal. Serangan remote to local adalah serngan yang dilakukan oleh attacker untuk mendapatkan akses akun ke sebuah sistem yang sebelumnya tidak memiliki akun ke sistem tersebut. Pola serangan R2L pada dataset DARPA dapat dikenali dengan beberapa paramer seperti source address, destination address, flags, ip length, dan tcp length.
Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA) Sharipuddin Sharipuddin; Benni Purnama; Kurniabudi Kurniabudi; Eko Arip Winanto; Deris Stiawan; Darmawijoyo Hanapi; Mohd. Yazid Idris; Rahmat Budiarto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2098

Abstract

There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent.
Designing consensus algorithm for collaborative signature-based intrusion detection system Eko Arip Winanto; Mohd Yazid Idris; Deris Stiawan; Mohammad Sulkhan Nurfatih
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp485-496

Abstract

Signature-based collaborative intrusion detection system (CIDS) is highly depends on the reliability of nodes to provide IDS attack signatures. Each node in the network is responsible to provide new attack signature to be shared with other node. There are two problems exist in CIDS highlighted in this paper, first is to provide data consistency and second is to maintain trust among the nodes while sharing the attack signatures. Recently, researcher find that blockchain has a great potential to solve those problems. Consensus algorithm in blockchain is able to increase trusts among the node and allows data to be inserted from a single source of truth. In this paper, we are investigating three blockchain consensus algorithms: proof of work (PoW), proof of stake (PoS), and hybrid PoW-PoS chain-based consensus algorithm which are possibly to be implemented in CIDS. Finally, we design an extension of hybrid PoW-PoS chain-based consensus algorithm to fulfill the requirement. This extension we name it as proof of attack signature (PoAS).
Tingkat Kesuksesan E-Learning Edmodo Sebagai Sistem Pembelajaran Online Selama Pandemi Covid 19 Adopsi Model DeLone&Mclean Ibnu Sani Wijaya; Dodi Sandra; Khairuldi Khairuldi; Eko Arip Winanto; Sharipuddin Sharipuddin
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 3 (2022): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i3.1333

Abstract

Edmodo merupakan platform pembelajaran online yang saat ini termasuk banyak digunakan di perguruan tinggi di Indonesia selama pandemic Covid19 salah satunya di Univeristas Dinamika Bangsa (UNAMA) Jambi. Dalam penelitian ini dilakukan pengevaluasian terhadapa kualitas kesuksesan LMS Edmodo pada platform-platform edmodo tersebut dengan mengadopsi model Delone And Mclean dengan 6 variabel yaitu Information Quality, System Quality, Service Quality, Use, User Statisfaction dan Net Benefit. Untuk data analisis menggunak Structural Equation Mode (SEM). Responden di penelitian ini adalah para dosen dan mahasiswa di UNAMA Jambi yang sebagai pengguna edmodo. Adapun tujuan penelitian ini untuk membuktikan sejauh mana kesuksesan yang penerapan Edmodo di universitas dinamika bangsa jambi. Responden pada penelitian ini sebanyak 166 responden. Data dikumpulkan dengan cara metode survey. Hasil dari penelitian ini menunjukkan nilai R2 dengan variabel information quality dan system quality memiliki nilai 0.339 dikategorikan tingkat moderat/sedang. Artinya kedua variabel dependen memberikan pengaruh dan tinkat moderat/sedang terhadap variabel dependen. Untuk R2 variabel independent use dan user satisfaction memiliki substansial/kuat dengan nilai 0.707, artinya kedua variabel independen memberikan pengaruh dan tingkat Substansial/kuat terhadap variabel dependen
Deteksi Serangan pada Jaringan Kompleks IoT menggunakan Recurrent Neural Network Eko Arip Winanto; Kurniabudi Kurniabudi; Sharipuddin Sharipuddin; Ibnu Sani Wijaya; Dodi Sandra
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5298

Abstract

The complex network in the Internet of Things is challenging to maintain network security. With network complexity including data, protocols, sizes, communications, standards, and more, it becomes difficult to implement an intrusion detection system (IDS). One way to improve IDS on complex IoT networks is by using deep learning to detect attacks that occur on complex IoT networks. Recurrent neural network (RNN) is a deep learning method that enhances the detection of complex IoT networks because it takes into account the current input as well as what has been learned from previously received inputs. When making decisions about RNNs, consider current information as well as what has been learned from previous input. Therefore, this study proposes the RNN method to improve the performance of attack detection systems on complex IoT networks. The results of this experiment show satisfactory results by increasing the performance of the accuracy detection system in complex IoT networks which reaches 87%.
Improvement detection system on complex network using hybrid deep belief network and selection features Sharipuddin Sharipuddin; Eko Arip Winanto; Zulwaqar Zain Mohtar; Kurniabudi Kurniabudi; Ibnu Sani Wijaya; Dodi Sandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp470-479

Abstract

The challenge for intrusion detection system on internet of things networks (IDS-IoT) as a complex networks is the constant evolution of both large and small attack techniques and methods. The IoT network is growing very rapidly, resulting in very large and complex data. Complex data produces large data dimensions and is one of the problems of IDS in IoT networks. In this work, we propose a dimensional reduction method to improve the performance of IDS and find out the effect of the method on IDS-IoT using deep belief network (DBN). The proposed method for feature selection uses information gain (IG) and principle component analysis (PCA). The experiment of IDS-IoT with DBN successfully detects attacks on complex networks. The calculation of accuracy, precision, and recall, shows that the performance of the combination DBN with PCA is superior to DBN with information gain for Wi-Fi datasets. Meanwhile, the Xbee dataset with information gain is superior to using PCA. The final result of measuring the average value of accuracy, precision, and recall from each IDSDBN test for IoT is 99%. Other results also show that the proposed method has better performance than previous studies increasing by 4.12%.
Co-Authors Bedine Kerim Darmawijoyo, Darmawijoyo Deris Stiawan Dodi Sandra Dodi Sandra Ibnu Sani Wijaya Idris, Mohd. Yazid Khairuldi Khairuldi Kurniabudi, Kurniabudi Mohd Yazid bin Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Sulkhan Nurfatih Purnama, Benni Rahmat Budiarto Sharipuddin, Sharipuddin Yundari, Yundari Zulwaqar Zain Mohtar