- United Kingdom
Rahmat Budiarto
Albaha University, Computer Science, Faculty Member
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... more
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 datas...
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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... more
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 malw...
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Dragon Nest is one of Massively Multiplayer Online Role-playing Game (MMORPG online games. It has become the most popular online game played by people around the world. This work observes two examples of the MMORPG online games: the... more
Dragon Nest is one of Massively Multiplayer Online Role-playing Game (MMORPG online games. It has become the most popular online game played by people around the world. This work observes two examples of the MMORPG online games: the Dragon Nest INA and the Legend DN II. The purpose is to analyze the traffic data of the Dragon Nest to find and discern the patterns of behavior of the Dragon Nest INA and the Legend DN II using Deep Packet Inspection (DPI). A dataset is constructed by capturing traffic data from the testbed environment. Then feature extraction, feature selection, and visualization are performed during the experiments. Experiment results shows the traffic data of the Dragon Nest INA is higher than the Legend DN II. It is because of the difference in the number of entries in the game. Then, the Bloom filter method is used as a tool to check the existence of a pattern of the Dragon Nest in the dataset. The false positive rate of matching is 0.399576%.
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The Internet of Things (IoT) is transforming the agriculture industry and enables farmers to deal with the vast challenges in the industry. Internet of Farming (IoF) applications increases the quantity, quality, sustainability as well as... more
The Internet of Things (IoT) is transforming the agriculture industry and enables farmers to deal with the vast challenges in the industry. Internet of Farming (IoF) applications increases the quantity, quality, sustainability as well as cost effectiveness of agricultural production. Farmers leverage IoF to monitor remotely, sensors that can detect soil moisture, crop growth and livestock feed levels, manage and control remotely the smart connected harvesters and irrigation equipment, and utilize artificial intelligence based tools to analyze operational data combined with 3rd party information, such as weather services, to provide new insights and improve decision making. The Internet of Farming relies on data gathered from sensor of Wireless Sensor Network (WSN). The WSN requires a reliable connectivity to provide accurate prediction of the farming system. This chapter proposes a strategy that provides always best connectivity (ABC). The strategy considers a routing protocol to su...
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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... more
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 fu...
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Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the... more
Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which r...
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The paper provides an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interaction that, in part, elucidate evolving... more
The paper provides an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interaction that, in part, elucidate evolving structures and impromptu topologies of networks. This paper, therefore, models an open source project as an organizational network. It provides definitions of social capital for this organizational network and formulation of the mechanism to optimize the social capital for achieving its goal that is optimized productivity. A case study of an open source Apache-Hadoop project is considered and empirically evaluated. An analysis of how social capital can be created within this type of organizations and driven to a measurement for its value is provided. Finally, a verification on whether the social capital of the organizational network is proportional towards optimizing their productivity is considered.
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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... more
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).
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Granblue Fantasy is one of Role Playing Games (RPG). It’s a video role-playing game developed by Cygames. This research to observes the Granblue Fantasy Game. The purpose is to analyze the traffic data of the Granblue Fantasy to find the... more
Granblue Fantasy is one of Role Playing Games (RPG). It’s a video role-playing game developed by Cygames. This research to observes the Granblue Fantasy Game. The purpose is to analyze the traffic data of the Granblue Fantasy to find the pattern using Deep Packet Inspection (DPI), Capturing the Data Traffic, Feature Extraction Process and Visualize the Pattern. The Pattern are Gacha, Solo Raid, Casino and Multiraid. This research demonstrate that Multiraid battle has more data than other pattern with TTL 237.
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Mixed IPv4/IPv6 networks will continue to use mobility support over tunneling mechanisms for a long period of time until the establishment of IPv6 end-to-end connectivity. Encapsulating IPv6 traffi c within IPv4 increases the level of... more
Mixed IPv4/IPv6 networks will continue to use mobility support over tunneling mechanisms for a long period of time until the establishment of IPv6 end-to-end connectivity. Encapsulating IPv6 traffi c within IPv4 increases the level of hiding internal contents. Thus, mobility in mixed IPv4/IPv6 networks introduces new security vulnerabilities. One of the most critical vulnerabilities associated with the IPv6 protocol is the routing header that potentially may be used by attackers to bypass the network security devices. This paper proposes an algorithm (V6HAPA) for protecting home agent clients from the routing header vulnerability, considering that the home agents reside behind an IPv4 Network Address Translation (NAT) router. The experimental results show that the V6HAPA provides enough confidence to protect the home agent clients from attackers.
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Due to the complexity of an open multi-agent system, agents’ interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one’s current capabilities. Repeated... more
Due to the complexity of an open multi-agent system, agents’ interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one’s current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those agents self-organize themselves for a long-term objective. This paper, therefore, aims to provide an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interactions that, in part, elucidate evolving structures and impromptu topologies of networks. We model an open source project as an organizational network and provide definitions and formulations to correlate the proposed mechanism of social capital with the achievement of an organizational charter, for example, optimized productivity. To empirically evaluate our model, we conducted a case study of an open s...
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Compression, in general, aims to reduce file size, with or without decreasing data quality of the original file. Digital Imaging and Communication in Medicine (DICOM) is a medical imaging file standard used to store multiple information... more
Compression, in general, aims to reduce file size, with or without decreasing data quality of the original file. Digital Imaging and Communication in Medicine (DICOM) is a medical imaging file standard used to store multiple information such as patient data, imaging procedures, and the image itself. With the rising usage of medical imaging in clinical diagnosis, there is a need for a fast and secure method to share large number of medical images between healthcare practitioners, and compression has always been an option. This work analyses the Huffman coding compression method, one of the lossless compression techniques, as an alternative method to compress a DICOM file in open PACS settings. The idea of the Huffman coding compression method is to provide codeword with less number of bits for the symbol that has a higher value of byte frequency distribution. Experiments using different type of DICOM images are conducted, and the analysis on the performances in terms of compression r...
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Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenario normal, attack and normal-attack. The process of... more
Focus of this research is TCP FIN flood attack pattern recognition in Internet of Things (IoT) network using rule based signature analysis method. Dataset is taken based on three scenario normal, attack and normal-attack. The process of identification and recognition of TCP FIN flood attack pattern is done based on observation and analysis of packet attribute from raw data (pcap) using a feature extraction and feature selection method. Further testing was conducted using snort as an IDS. The results of the confusion matrix detection rate evaluation against the snort as IDS show the average percentage of the precision level.
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In this article we share a teaching CS subject experience at college of CS&IT, Albaha university with involving the students in our research activities.
Research Interests: Computer Science and IEEE
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Abstract: Some Mobile Learning methods which are mostly at the experimental level are available. In addition, sharing information among the tutors and the learners which is a reason for using the Mobile Grids as the infrastructure of such... more
Abstract: Some Mobile Learning methods which are mostly at the experimental level are available. In addition, sharing information among the tutors and the learners which is a reason for using the Mobile Grids as the infrastructure of such solutions is a significant ...
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Internet of Things (IoT) devices may transfer data to the gateway/application server through File Transfer Protocol (FTP) transaction. Unfortunately, in terms of security, the FTP server at a gateway or data sink very often is improperly... more
Internet of Things (IoT) devices may transfer data to the gateway/application server through File Transfer Protocol (FTP) transaction. Unfortunately, in terms of security, the FTP server at a gateway or data sink very often is improperly set up. At the same time, password matching/theft holding is among the popular attacks as the intruders attack the IoT network. Thus, this paper attempts to provide an insight of this type of attack with the main aim of coming up with attack patterns that may help the IoT system administrator to analyze any similar attacks. This paper investigates brute force attack (BFA) on the FTP server of the IoT network by using a time-sensitive statistical relationship approach and visualizing the attack patterns that identify its configurations. The investigation focuses on attacks launched from the internal network, due to the assumption that the IoT network has already installed a firewall. An insider/internal attack launched from an internal network endang...
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Residents who live in rural area normally have limited access to Internet, poor living condition and limited access to healthcare services. Besides, medical specialists are not available every day. As such, quality of healthcare services... more
Residents who live in rural area normally have limited access to Internet, poor living condition and limited access to healthcare services. Besides, medical specialists are not available every day. As such, quality of healthcare services in rural area is extremely low. In Saudi Arabia, there are approximately 5.69 million people living in rural area (resource-poor settings). This work proposes a framework to develop mobile health application to aid the resource poor settings community health. The mobile health application makes use of the pre-trained neural network model to provide result of disease prediction for user. The application performance in term of accuracy, precision, recall and F1 score are measured using an existing diseases dataset and considering nine common diseases in the community. Measurement results show that the health mobile app developed thru the proposed framework is promising.
The network traffic of the Internet became huge and more complex due to the expansion of the Internet technology in supporting the convergence of IP networks, Internet of Things, and social networks. As a consequence, a more sophisticated... more
The network traffic of the Internet became huge and more complex due to the expansion of the Internet technology in supporting the convergence of IP networks, Internet of Things, and social networks. As a consequence, a more sophisticated network monitoring tool is desired in order to prevent an enterprise network from malware attacks, to maintain its availability as high as possible at any time, and to maintain the network's healthiness. This chapter offers a development of real-time network monitoring tool platform. The research component of this chapter attempts to answer the challenges of making the monitoring tool become smarter and more accurate by applying artificial intelligence techniques. In addition, a research on buffering techniques to speed up the traffic data acquisition process and micro-controller unit design for sensor-based applications are also carried out. In the development component, some ground works has already been done such as network traffic packets capturing modules, and packets decoding modules. The system development uses Java Eclipse platform.
A robust increasing on smart sensors in Internet of Thing (IoT) results huge and heterogenous data and becomes a challenge in data prepocessing and analysis for anomaly detection. The lack of IoT publicly available dataset is one issue in... more
A robust increasing on smart sensors in Internet of Thing (IoT) results huge and heterogenous data and becomes a challenge in data prepocessing and analysis for anomaly detection. The lack of IoT publicly available dataset is one issue in anomaly detection research. To resolve that problem, a testbed topology is proposed in this research. In addition, a high-dimensionality data analysis faces a computational complexity. The purpose of this study is to presents a global framework for anomaly detection in IoT and proposes a distributed preprocessing framework. Unsupervised learning approach has been chosen to reduce dimensionality of IoT data traffic.
Ransomware is a malware that represents a serious threat to a user’s information privacy. By investigating howransomware works, we may be able to recognise its atomic behaviour. In return, we will be able to detect theransomware at an... more
Ransomware is a malware that represents a serious threat to a user’s information privacy. By investigating howransomware works, we may be able to recognise its atomic behaviour. In return, we will be able to detect theransomware at an earlier stage with better accuracy. In this paper, we propose Control Flow Graph (CFG) asan extracting opcode behaviour technique, combined with 4-gram (sequence of 4 “words”) to extract opcodesequence to be incorporated into Trojan Ransomware detection method using K-Nearest Neighbors (K-NN)algorithm. The opcode CFG 4-gram can fully represent the detailed behavioural characteristics of Trojan Ransomware.The proposed ransomware detection method considers the closest distance to a previously identifiedransomware pattern. Experimental results show that the proposed technique using K-NN, obtains the best accuracyof 98.86% for 1-gram opcode and using 1-NN classifier.
Research Interests: Computer Networks and IPV
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— The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The... more
— The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The summary status visualization is indispensable to depict all devices/sensors that are most indicative of a pending failure and a predictive power/energy. Thus, this paper proposes a multi-platform monitoring and anomaly detection system that supports heterogeneous devices. The proposed system addresses the problems of: (i) how to monitor the network to prevent device failures and (ii) how to design a comprehensive feature for the early anomaly detection of IoT communication.
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Abstract: Neural networks have always been a popular approach for intelligent machine development and knowledge discovery. Although, reports have featured successful neural network implementations, problems still exists with this... more
Abstract: Neural networks have always been a popular approach for intelligent machine development and knowledge discovery. Although, reports have featured successful neural network implementations, problems still exists with this approach, particularly its excessive training time. In this paper, we propose a Gene-Regulated Nested Neural Network (GRNNN) model as an improvement to existing neural network models to solve the excessive training time problem. We use a gene regulatory training engine to control and distribute the genes that regulate the proposed nested neural network. The proposed GRNNN is evaluated and validated through experiments to classify accurately the 8 bit XOR parity problem. Experimental results show that the proposed model does not require excessive training time and meets the required objectives. Keywords: Neural networks, gene regulatory network, artificial intelligence, bio-inspired computing