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Efficient Method of Collecting Network Traces for Generating Network Topology
http://doi.org/10.5626/JOK.2018.45.12.1319
Network topology information is critical in cyber security for designing security architecture and threat analysis as well as for network management and diagnosis. Numerous approaches have been proposed for obtaining information about network topology. In particular, graph analytical methods for inferring network topology are intensively researched. These methods collect path traces via traceroute and analyze them using graph theoretical methods for inferring network topology. However, there exist few research reports on choosing destinations and deployment locations of trace collectors which have the potential of significantly affecting network overhead and discovery time. This paper proposes a novel method of choosing destinations and determining trace collectors for the efficient collection of network traces. In the present work, we have also implemented a prototype of the proposed methods and experimentally validated their performance.
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.
Active Vision from Image-Text Multimodal System Learning
In image classification, recent CNNs compete with human performance. However, there are limitations in more general recognition. Herein we deal with indoor images that contain too much information to be directly processed and require information reduction before recognition. To reduce the amount of data processing, typically variational inference or variational Bayesian methods are suggested for object detection. However, these methods suffer from the difficulty of marginalizing over the given space. In this study, we propose an image-text integrated recognition system using active vision based on Spatial Transformer Networks. The system attempts to efficiently sample a partial region of a given image for a given language information. Our experimental results demonstrate a significant improvement over traditional approaches. We also discuss the results of qualitative analysis of sampled images, model characteristics, and its limitations.
Scalable RDFS Reasoning Using the Graph Structure of In-Memory based Parallel Computing
MyungJoong Jeon, ChiSeoung So, Batselem Jagvaral, KangPil Kim, Jin Kim, JinYoung Hong, YoungTack Park
In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.
Software Product Line Development and Test Process Based on CVL
Eunyoung Cheon, Yongjin Seo, Ju Seok Lee, Su Ji Kim, Jin-a Kim, Hyeon Soo Kim
Software Product Line Engineering is a collection of techniques that analyze the commonalities and variabilities of the products within a product family and produce products using such information. In Software Product Line Engineering, construction of the correct core assets is very important. To accomplish this, the commonalities and variabilities must first be definitively identified, both to provide traceability between the core assets, and to guarantee the reliability of the products. This paper suggests software product line development and test processes based on CVL for the differentiation of commonalities and variabilities. The proposed approach enables correct building of the core assets through procedures to keep traceability and guarantee the reliability of the products.
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