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Research and Development of Wireless Protocol Automatic Analyzer
Woorim Bang, Youngbae Jeon, Shinwoo Shim, Kwangsoo Kim, Ji Won Yoon
http://doi.org/10.5626/JOK.2019.46.8.852
Automatic Protocol Reverse Engineering (APRE) defines automatic analysis of the format, semantics, and parameters of an unknown protocol. APRE can be used to detect malware that is distributed on the network, or for security and suitability verification of protocols that have been defined own their own. Conventional APRE studies have been conducted mostly on text-based protocols and wired protocols. As the number of wireless devices increases, there is an increasing need for a protocol analyzer for wireless protocols. Therefore, in this paper, research and development of the protocol automatic analyzer were performed by considering the characteristics of the wireless protocols. For the analysis of the wireless protocol, this study analyzed the messages in binary units. We propose a method to calculate the message distance by assigning a weight according to the packet acquisition time interval to perform clustering among similar messages. As a result of collecting and analyzing the messages according to the IEEE 802.11 protocol using the proposed method, we could correctly classify 95.1% message types among 800messages, and the degree of conciseness was 3.6. By using one of the existing APRE tools, Netzob, 92.1% precision was obtained with the conciseness of 3.5. Consequently, the proposed method showed better performance than Netzob.
Ship Detection using CNN based on Contrast Fusion Technique in Satellite Images : Accuracy Enhancement
Sunggyun Im, Youngbae Jeon, Junghwan Hwang, Jiwon Yoon
http://doi.org/10.5626/JOK.2019.46.8.823
The satellite has various missions such as ground/marine observation, communication, broadcasting, etc. Satellite photographs provide information for the maintenance of marine security and traffic control for ship detection. Since satellite photos are taken all over the earth, the memory storage is not sufficient to hold such data with each data being of a high resolution and requiring automatic ship detection using the computer. The existing literature on ship detection employed several deep learning models. However, the problem of processing speed due to the characteristics of satellite photographs leads to the necessity of using a CNN(Convolution Neural Network) model that has a comparably high processing speed. On the contrary, it is difficult to improve the accuracy and performance mostly due to factors such as marina, lighthouses and waves. Therefore, in this paper, we propose a model that improves the accuracy and performance by combining image contrast enhancement with the existing CNN. In addition, we have employed the overlap and rotation functions to increase the amount of data required for ship classification in the learning stage and implement automation detection technology considering window sliding to reduce detection speed in real satellite photographs. Also, the identified ship data has been used as learning data to improve accuracy for the model that can be used in the real industry.
Network Topology Discovery with Load Balancing for IoT Environment
Hyunsu Park, Jinsoo Kim, Moosung Park, Youngbae Jeon, Jiwon Yoon
http://doi.org/10.5626/JOK.2017.44.10.1071
With today"s complex networks, asset identification of network devices is becoming an important issue in management and security. Because these assets are connected to the network, it is also important to identify the network structure and to verify the location and connection status of each asset. This can be used to identify vulnerabilities in the network architecture and find solutions to minimize these vulnerabilities. However, in an IoT(Internet of Things) network with a small amount of resources, the Traceroute packets sent by the monitors may overload the IoT devices to determine the network structure. In this paper, we describe how we improved the existing the well-known double-tree algorithm to effectively reduce the load on the network of IoT devices. To balance the load, this paper proposes a new destination-matching algorithm and attempts to search for the path that does not overlap the current search path statistically. This balances the load on the network and additionally balances the monitor"s resource usage.
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