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The trends of supervisory control and data acquisition security challenges in heterogeneous networks

10.11591/ijeecs.v22.i2.pp874-883
M. Agus Syamsul Arifin , Susanto Susanto , Deris Stiawan , Mohd Yazid Idris , Rahmat Budiarto
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.
Volume: 22
Issue: 2
Page: 874-883
Publish at: 2021-05-01

Mobile communication (2G, 3G & 4G) and future interest of 5G in Pakistan: a review

10.11591/ijeecs.v22.i2.pp1061-1068
Muhammad Saqib Iqbal , Zulhasni Abdul Rahim , Syed Aamer Hussain , Norulhusna Ahmad , Hazilah Mad Kaidi , Robiah Ahmad , Rudzidatul Akmam Dziyauddin
The use of mobile communication is growing radically with every passing year. The new reality is the fifth generation (5G) of mobile communication technology. 5G requires expensive infrastructural adjustment and upgradation. Currently, Pakistan has one of the most significant numbers of biometrically verified mobile users. However, at the same time, the country lags incredibly in the field of mobile internet adoption, with just half of the mobile device owners avail broadband subscription. It is a viable market with a large segment yet to be tapped. With the advancing progression in Pakistan towards the internet of things (IoT) connectivity, i.e., solar-powered home solutions, smart city projects, and on-board diagnostics (OBD), the urgency for speed, bandwidth and reliability are on the rise. In this paper, Pakistan's prevalent mobile communication networks, i.e., second, third and fourth generation (2G, 3G and 4G), were analyzed and examined in light of the country's demographics and challenges. The future of 5G in Pakistan was also discussed. The study revealed that non-infrastructural barriers influence the low adoption rate, which is the main reason behind the spectrum utilization gap, i.e., the use of 3G, and the 4G spectrum is minimal.
Volume: 22
Issue: 2
Page: 1061-1068
Publish at: 2021-05-01

Predicting temperature of Erbil City applying deep learning and neural network

10.11591/ijeecs.v22.i2.pp944-952
Sardar M. R. K. Al- Jumur , Shahab Wahhab Kareem , Raghad Z. Yousif
One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and  generalized regression neural network (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.
Volume: 22
Issue: 2
Page: 944-952
Publish at: 2021-05-01

K-means method for clustering learning classes

10.11591/ijeecs.v22.i2.pp835-841
A. D. Indriyanti , D. R. Prehanto , T. Z. Vitadiar
Learning class is a collection of several students in an educational institution. Every beginning of the school year the educational institution conducts a grouping class test. However, sometimes class grouping is not in accordance with the ability of students. For this reason, a system is needed to be able to see the ability of students according to the desired parameters. Determination of the weight of test scores is done using the K-Means method as a grouping method. Iteration or repetition process in the K-Means method is very important because the weight value is still very possible to change. Therefore, the repetition process is carried out to produce a value that does not change and is used to determine the ability level of students. The results of the class grouping test scores affect the ability of students. Application of K-Means method is used in building an information system grouping student admissions in an educational institution. Acceptance of students will be grouped into 3 groups of learning classes. The results of testing the system that applies K-Means method and based on data on the admission of prospective students from educational institutions have very high accuracy with an error rate of 0.074. 
Volume: 22
Issue: 2
Page: 835-841
Publish at: 2021-05-01

Effect of Covid-19 on the electronic payment system: usage level trust and competence perspectives

10.11591/ijeecs.v22.i2.pp1144-1155
Mahmoud Odeh , Mohammad Yousef
Covid-19 has dramatically spread globally and dramatically made several effects in almost all sectors. Electronic commerce and electronic payment systems are important sectors which affected directly by the Covid-19 pandemic. Online markets and electronic payment systems have been recognized as one of the fastest-growing technology in the last decade even within normal situations. However, several factors may influence such growth, which many consider as barriers or enablers of using the electronic payment system. This study aims to shed the light on the influence of Covid-19 on the electronic payment system from trust and competence perspectives. The study employed both qualitative and quantitative methodological approaches for data collection and analysis. The data was collected from 31 semi-structured interviews, 718 surveys, and annual reports. NVivo, Microsoft Visio, and Microsoft power business intelligence were used for the data analysis process. As a part of this study, a proposed framework has been developed which includes both technical and managerial parts.
Volume: 22
Issue: 2
Page: 1144-1155
Publish at: 2021-05-01

Performance analysis of dual-branch selection combining technique over the generalized Alpha-Mu fading channels

10.11591/ijeecs.v22.i2.pp1024-1031
Hasan Aldiabat , Ahmed Alhubaishi
Inspired by the low-difficulty of implementing a dual-branch selection combining (SC) technique, this research paper presents approximate closed form expressions for the bit error rate (BER) of M-ary phase shift keying (M-PSK) considering the SC technique. In particular, the BER expression is derived over independent and identically distributed (i.i.d) alpha - mu fading channels and is based on the use of Meijer’s G-function. The presented mathematical formulas can be modified to study the performance of different types of fading channels including Weibull, exponential, Nakagami-m, Gamma, and Rayleigh channels. This can be achieved by updating the parameters of the propagation medium nonlinearity (alpha) and the number of multipath clusters (mu). In addition, the paper provides numerical results that demonstrate a close match in the performance of the derived expressions and the simulation findings in terms of BER. Specifically, a very close to a total BER match is achieved using a range of signal to noise ratio (SNR) levels for various selections of the alpha and mu parameters. The obtained closed form BER expression of M-PSK considering the dual-branch SC technique is novel, new, and has never been published in the literature before.
Volume: 22
Issue: 2
Page: 1024-1031
Publish at: 2021-05-01

Robot movement controller based on dynamic facial pattern recognition

10.11591/ijeecs.v22.i2.pp733-743
Siti Nurmaini , Ahmad Zarkasi , Deris Stiawan , Bhakti Yudho Suprapto , Sri Desy Siswanti , Huda Ubaya
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).
Volume: 22
Issue: 2
Page: 733-743
Publish at: 2021-05-01

Performance analysis of patch antenna for underwater wireless communication in seawater

10.11591/ijeecs.v22.i2.pp857-865
Siti Harliza Mohd Razali , Razali Ngah , Yoshihide Yamada , Kamilia Kamardin
Underwater wireless communication in seawater is becoming more interesting and challenging in recent years. The development of antenna for underwater wireless communication in seawater at 900 MHz UHF range frequency is implemented by using patch antenna. In this paper, the antennas were designed using FEKO, an electromagnetic simulation software, and a suitable size for rectangular patch antenna for seawater application was developed to study the relevance between λ0 with W and L in seawater. The difference between the patches in free space and seawater was the L size, which was slightly bigger, about 0.9 mm, than free space size. But the gain for patch antenna in seawater was found at -2.51 dBi, lower than patch antenna in free space, which was 5.76 dBi due to the path loss in seawater. This shows that attenuation happened, and a better antenna will be design. The one that has better gain, which is around above 2 dBi in seawater, in order to get better performance antenna in seawater environment.
Volume: 22
Issue: 2
Page: 857-865
Publish at: 2021-05-01

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

10.11591/ijeecs.v22.i2.pp1177-1190
Ismail Taha Ahmed , Baraa Tareq Hammad , Norziana Jamil
Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.
Volume: 22
Issue: 2
Page: 1177-1190
Publish at: 2021-05-01

A queue theory in the cross-polarization of antenna in satellite communication

10.11591/ijeecs.v22.i2.pp884-892
Rio Mubarak , Setiyo Budiyanto , Putri Wulandari , Fajar Rahayu , Andi Adriansyah , Mudrik Alaydrus
Satellite communication is a telecommunications technique that uses satellites as a connecting component, for example VSAT. In antenna installation, there is an important process which is called the cross-polarization. Cross-polarization is one process that cannot be released inside installation of VSAT antennas for satellite communication. Sometimes, in this process, a user queue will occur. Queuing theory explain the process is done and also calculate the other factors that are in the process. By knowing queuing theory to the cross-polarization, it will be easy to know the efficiency of queuing theory in the cross-polarization. Based on the characteristics of the cross-polarization, user can be known the queuing model that used and performance of the queuing system. The queuing model for the cross-polarization, using Kendall notation, M/M/1. Based on the analysis that has been done; by using 1 server the value of service level (ρ) is 0.67, using 2 servers = 0.33 and 3 servers = 0.22. The waiting time in the queue is longer if using 1 server which is 0.67 hours or 40 minutes. If a satellite operator uses 2 servers, waiting time in the queue is 25 minutes and 3 servers is 2.8 minutes which means that there is almost no waiting time in the queue.
Volume: 22
Issue: 2
Page: 884-892
Publish at: 2021-05-01

Weather prediction using random forest machine learning model

10.11591/ijeecs.v22.i2.pp1208-1215
R. Meenal , Prawin Angel Michael , D. Pamela , E. Rajasekaran
The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.
Volume: 22
Issue: 2
Page: 1208-1215
Publish at: 2021-05-01

Analysis and evaluation of symmetrical key ciphers for internet of things smart home

10.11591/ijeecs.v22.i2.pp1191-1198
Lujain S. Abdulla , Musaria K. Mahmood , Abbas F. Salih , Sulaiman M. Karim
A large number of sensors and intelligent devices are interconnected via the internet to collect data as part of the internet of things (IoT) applications. Data security is one of the important challenges for these applications due to vulnerability of the internet. IoT devices limiting factors, such as delay-sensitivity, restricted memory, and low computing capability, make choosing the appropriate data encryption standard extremely important. The current research focuses on evaluating four data security, block cipher standards for the IoT smart home application. Considering the encryption/decryption speed, DES, TDES, AES, and SAFER+ standards are evaluated by implementing the algorithms with MATLAB to determine the best security solution. The simulation of the four standards shows the superiority of SAFER+ standard in term of encryption speed compared to others added to its capabilities on security, and software implementation opportunity. The use of classical symmetric key standards for real time data security in the IoT application can be validated through the selection of SAFER+.
Volume: 22
Issue: 2
Page: 1191-1198
Publish at: 2021-05-01

Isolated Arabic handwritten words recognition using EHD and HOG methods

10.11591/ijeecs.v22.i2.pp801-808
Mamoun Jassim Mohammed , Suphian Mohammed Tariq , Hayder Ayad
Handwriting recognition is a growing field of study in computer vision, artificial intelligence and pattern recognition technology aimed to recognizing texts and handwritings of hefty amount of produced official documents and paper works by institutes or governments. Using computer to distinguish and make these documents accessible and approachable is the goal of these efforts. Moreover, recognition of text has accomplished practically a major progress in many domains such as security sector and e-government structure and more. A system for recognition text’s handwriting was presented here relied on edge histogram descriptor (EHD), histogram of orientated gradients (HOG) features extraction and support vector machine (SVM) as a classifier is proposed in this paper. HOG and EHD give an optimal features of the Arabic hand-written text by extracting the directional properties of the text. Besides that, SVM is a most common machine learning classifier that obtaining an essential classification results within various kernel functions. The experimental evaluation is carried out for Arabic handwritten images from IESK-ArDB database using HOG, EHD features and proposed work provides 85% recognition rate.
Volume: 22
Issue: 2
Page: 801-808
Publish at: 2021-05-01

Classification of Quranic topics based on imbalanced classification

10.11591/ijeecs.v22.i2.pp678-687
Bassam Sulaiman Arkok , Akram Mohammed Zeki
Imbalanced classification techniques have been applied widely in the field of data mining. It is used to classify the imbalanced classes that are not equal in the number of samples. The problem of imbalanced classes is that the classification performance tends to the class with more samples while the class with few samples will obtain poor performance. This problem can be occurred in the Qur’anic classification due to the different number of verses. Many studies classified Qur’anic verses, which depended on the traditional classification. However, no study classified Qur’anic topics based on the techniques of imbalanced classification. Therefore, this paper aims to apply the methods of imbalanced classification as synthetic minority over-sampling technique (SMOTE), random over sample (ROS), and random under sample (RUS) methods to classify the Qur’anic topics that are imbalanced. Many metrics were used in this research to evaluate the experimental results. These metrics are sensitivity/recall, specificity, overall accuracy, F-Measure, G-mean, and matthews correlation coefficient (MCC). The results showed that the Quranic classification performance improved when imbalanced classification techniques were applied
Volume: 22
Issue: 2
Page: 678-687
Publish at: 2021-05-01

Cloud computing based e-government in Iraq using partial least square algorithm

10.11591/ijeecs.v22.i2.pp953-960
Hayder Salah Hashim , Zainab Amin Al-Sul
The electronic government (e-government) worldwide is slowly progressing. The e-government progress in developing countries, such as Iraq, is slower compared with those of regional countries. Prior research suggested deploying cloud based E-government (CBEG). However, most studies are either review or conceptual. This study aims to examine the CBEG in Iraq and identify the criteria that are required for utilizing this technology. A total of 279 academic staff working in the IT department in Iraqi universities have participated in this study. The findings using matrix and the algorithm of partial least square presented ten criteria. After using the average variance extracted technique, the criteria reduced to seven. Further analysis using smart partial least square was conducted, and this resulted in five important criteria namely, reliability of the service followed by availability, security, trust, and technological competence. Decision makers have to increase the competence of the staff and enhance the CBEG services.
Volume: 22
Issue: 2
Page: 953-960
Publish at: 2021-05-01
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