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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Annual Research Seminar Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications Jurnal Generic
Deris Stiawan
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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The trend malware source of IoT network Susanto Susanto; M. Agus Syamsul Arifin; Deris Stiawan; Mohd. Yazid Idris; Rahmat Budiarto
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.pp450-459

Abstract

Malware may disrupt the internet of thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.
The trends of supervisory control and data acquisition security challenges in heterogeneous networks M. Agus Syamsul Arifin; Susanto Susanto; Deris Stiawan; Mohd Yazid Idris; Rahmat Budiarto
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp874-883

Abstract

Supervisory control and data acquisition (SCADA) has an important role in communication between devices in strategic industries such as power plant grid/network. Besides, the SCADA system is now open to any external heterogeneous networks to facilitate monitoring of industrial equipment, but this causes a new vulnerability in the SCADA network system. Any disruption on the SCADA system will give rise to a dangerous impact on industrial devices. Therefore, deep research and development of reliable intrusion detection system (IDS) for SCADA system/network is required. Via a thorough literature review, this paper firstly discusses current security issues of SCADA system and look closely benchmark dataset and SCADA security holes, followed by SCADA traffic anomaly recognition using artificial intelligence techniques and visual traffic monitoring system. Then, touches on the encryption technique suitable for the SCADA network. In the end, this paper gives the trend of SCADA IDS in the future and provides a proposed model to generate a reliable IDS, this model is proposed based on the investigation of previous researches. This paper focuses on SCADA systems that use IEC 60870-5-104 (IEC 104) protocol and distributed network protocol version 3 (DNP3) protocol as many SCADA systems use these two protocols.
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).
Robot movement controller based on dynamic facial pattern recognition Siti Nurmaini; Ahmad Zarkasi; Deris Stiawan; Bhakti Yudho Suprapto; Sri Desy Siswanti; Huda Ubaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp733-743

Abstract

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).
Cyberattacks and data breaches in Indonesia by Bjorka: hacker or data collector? Tole Sutikno; Deris Stiawan
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4854

Abstract

Recently, the public has been shocked by the mysterious figure of Hacker Bjorka. Bjorka hacked Indonesian officials. Bjorka leaks Indonesia's General Election Commission (KPU) data. This raises a significant red flag concerning Bjorka's ability to "disrupt" circumstances that are harmful to a large number of individuals, including his alleged action of leaking the personal data of influential state officials. Expert Putra Aji Adhari says Bjorka isn't a hacker. Aji Putra stated that Bjorka is a team. He, who has been invited to communicate with NASA, is sure Bjorka is still in Indonesia. Putra told Bjorka's hacking steps. Ardi Sutedja declared Bjorka isn't a person, his pattern mirrored a hacking group's. Sutedja knew Bjorka was Indonesian. Domestic targets, attacks, and mastery are evidences. On the other hand, Wiryana, as a hacker's handler, said that Bjorka is not a real hacker but rather a data collector. Ismail Fahmi says that a hacker like Bjorka uses a VPN to get to a server without leaving any traces. Bjorka might have come from Indonesia. One sign is that Bjorka's use of English is similar to how most Indonesians talk.
Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3957

Abstract

The facial recognition process is normally used to verify and identify individuals, especially during the process of analyzing facial biometrics. The face detection algorithm automatically determines the presence or absence of a face. It is, however, theoretically difficult to analyze the face of a system with limited resources due to the complex pattern of a face. This implies an embedded platform scheme which is a combination of several learning methods supporting each other is required. Therefore, this research proposed the combination of the Haar Cascade method for the face detection process and the WNNs method for the learning process. The WNNs face recognition Algorithm (WNNs-FRA) uses facial data at the binary level and for binary recognition. Moreover, the sample face data in the binary were compared with the primary face data obtained from a particular camera or image. The parameters tested in this research include detection distance, detection coordinates, detection degree, memory requirement analysis, and the learning process. It is also important to note that the RAM node has 300 addresses divided into three face positions while the RAM discriminator has three addresses with codes (00), (10), and (10). Meanwhile, the largest amount of facial ROI data was found to be 900 pixels while the lowest is 400 pixels. The total RAM requirements were in the range of 32,768 bytes and 128 bytes and the execution time for each face position was predicted to be 33.3% which is an optimization because it is 66.67% faster than the entire learning process
Neural network models selection scheme for health mobile app development Yaya Sudarya Triana; Mohd Azam Osman; Adji Pratomo; Muhammad Fermi Pasha; Deris Stiawan; Rahmat Budiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1191-1203

Abstract

Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.
Co-Authors Abd Rahim, Mohd Rozaini Abdul Hadi Fikri Abdul Hanan Abdullah Abdul Harris Adi Sutrisman Aditya Putra Perdana Prasetyo Aditya Putra Perdana Prasetyo Adji Pratomo Agung Juli Anda Agus Eko Minarno Ahmad Fali Oklilas Ahmad Firdaus Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto Ahmad Heryanto, Ahmad Ahmad Zarkasi Albertus Edward Mintaria Ali Bardadi Bedine Kerim Bedine Kerim Bhakti Yudho Suprapto Bhakti Yudho Suprapto Darmawijoyo, Darmawijoyo Dasuki, Massolehin Desak Putu Dewi Kasih Dewi Bunga Dian Palupi Rini Endang Lestari Ruskan Ermatita Ermatita Erwin, Erwin Fachrudin Abdau Ferdiansyah Ferdiansyah Fikri, Abdul Hadi Firdaus Firdaus Firdaus, Firdaus Firsandaya Malik, Reza Gonewaje gonewaje Habibullah, Nik Mohd Harris, Abdul Huda Ubaya Huda Ubaya I Gede Yusa Idris, Mohd. Yazid Idris, Mohd. Yazid Imam Much Ibnu Subroto John Arthur Jupin Kurniabudi, Kurniabudi Lelyzar Siregar Lina Handayani M. Miftakul Amin M. Ridwan Zalbina Majzoob K. Omer Massolehin Dasuki Mehdi Dadkhah Mintaria, Albertus Edward Mohamed S. Adrees Mohammad Davarpanah Jazi Mohammad Sulkhan Nurfatih Mohammed Y. Alzahrani Mohd Arfian Ismail Mohd Azam Osman Mohd Faizal Ab Razak Mohd Rozaini Abd Rahim Mohd Saberi Mohamad Mohd Yazid bin Idris Mohd Yazid Bin Idris Mohd Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Mohd. Yazid Idris Muhammad Afif Muhammad Fermi Pasha Munawar A Riyadi Munawar Agus Riyadi Ni Ketut Supasti Dharmawan Nik Mohd Habibullah Nur Sholihah Zaini Osama E. Sheta Osvari Arsalan Prabowo, Christian Purnama, Benni Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Rahmat Budiarto Raja Zahilah Md Radzi Reza Firsandaya Malik Riyadi, Munawar A Rizki Kurniati Rossi Passarella Samsuryadi Samsuryadi Saparudin Saparudin Saparudin, Saparudin Sari Sandra Sarmayanta Sembiring Sasut A Valianta Sasut Analar Valianta Shahreen Kasim Sharipuddin Sharipuddin Sharipuddin Sharipuddin Sharipuddin Sharipuddin Sharipuddin Sharipuddin Sharipuddin, Sharipuddin Siti Hajar Othman Siti Nurmaini Sri Arttini Dwi Prasetyawati Sri Desy Siswanti Susanto Susanto Susanto Susanto Susanto, Susanto Sutarno Sutarno Syamsul Arifin, M. Agus tasmi salim Tasmi Salim Tole Sutikno Wan Isni Sofiah Wan Din Yaya Sudarya Triana Yazid Idris, Mohd. Yesi Novaria Kunang Yoga Yuniadi Yundari, Yundari