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Prof. Yangwoo Kim
Dongguk University-Seoul, Korea

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Research Keywords & Expertise

0 Computer Architecture
0 Artifical Intelligence
0 Grid and Cloud Computing
0 machine learning
0 Cloud and Fog computing

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Journal article
Published: 04 December 2020 in Sensors
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The term big data has emerged in network concepts since the Internet of Things (IoT) made data generation faster through various smart environments. In contrast, bandwidth improvement has been slower; therefore, it has become a bottleneck, creating the need to solve bandwidth constraints. Over time, due to smart environment extensions and the increasing number of IoT devices, the number of fog nodes has increased. In this study, we introduce fog fragment computing in contrast to conventional fog computing. We address bandwidth management using fog nodes and their cooperation to overcome the extra required bandwidth for IoT devices with emergencies and bandwidth limitations. We formulate the decision-making problem of the fog nodes using a reinforcement learning approach and develop a Q-learning algorithm to achieve efficient decisions by forcing the fog nodes to help each other under special conditions. To the best of our knowledge, there has been no research with this objective thus far. Therefore, we compare this study with another scenario that considers a single fog node to show that our new extended method performs considerably better.

ACS Style

Motahareh Mobasheri; Yangwoo Kim; Woongsup Kim. Fog Fragment Cooperation on Bandwidth Management Based on Reinforcement Learning. Sensors 2020, 20, 6942 .

AMA Style

Motahareh Mobasheri, Yangwoo Kim, Woongsup Kim. Fog Fragment Cooperation on Bandwidth Management Based on Reinforcement Learning. Sensors. 2020; 20 (23):6942.

Chicago/Turabian Style

Motahareh Mobasheri; Yangwoo Kim; Woongsup Kim. 2020. "Fog Fragment Cooperation on Bandwidth Management Based on Reinforcement Learning." Sensors 20, no. 23: 6942.

Journal article
Published: 22 April 2019 in Symmetry
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With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.

ACS Style

Muhammad Khan; Rezaul Karim; Yangwoo Kim. A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network. Symmetry 2019, 11, 583 .

AMA Style

Muhammad Khan, Rezaul Karim, Yangwoo Kim. A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network. Symmetry. 2019; 11 (4):583.

Chicago/Turabian Style

Muhammad Khan; Rezaul Karim; Yangwoo Kim. 2019. "A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network." Symmetry 11, no. 4: 583.

Journal article
Published: 21 March 2019 in Computer
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Many consider the KDD Cup 99 data sets to be outdated and inadequate. Therefore, the extensive use of these data sets in recent studies to evaluate network intrusion detection systems is a matter of concern. We contribute to the literature by addressing these concerns.

ACS Style

Kamran Siddique; Zahid Akhtar; Farrukh Aslam Khan; Yangwoo Kim. KDD Cup 99 Data Sets: A Perspective on the Role of Data Sets in Network Intrusion Detection Research. Computer 2019, 52, 41 -51.

AMA Style

Kamran Siddique, Zahid Akhtar, Farrukh Aslam Khan, Yangwoo Kim. KDD Cup 99 Data Sets: A Perspective on the Role of Data Sets in Network Intrusion Detection Research. Computer. 2019; 52 (2):41-51.

Chicago/Turabian Style

Kamran Siddique; Zahid Akhtar; Farrukh Aslam Khan; Yangwoo Kim. 2019. "KDD Cup 99 Data Sets: A Perspective on the Role of Data Sets in Network Intrusion Detection Research." Computer 52, no. 2: 41-51.

Journal article
Published: 11 October 2018 in Symmetry
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Every day we experience unprecedented data growth from numerous sources, which contribute to big data in terms of volume, velocity, and variability. These datasets again impose great challenges to analytics framework and computational resources, making the overall analysis difficult for extracting meaningful information in a timely manner. Thus, to harness these kinds of challenges, developing an efficient big data analytics framework is an important research topic. Consequently, to address these challenges by exploiting non-linear relationships from very large and high-dimensional datasets, machine learning (ML) and deep learning (DL) algorithms are being used in analytics frameworks. Apache Spark has been in use as the fastest big data processing arsenal, which helps to solve iterative ML tasks, using distributed ML library called Spark MLlib. Considering real-world research problems, DL architectures such as Long Short-Term Memory (LSTM) is an effective approach to overcoming practical issues such as reduced accuracy, long-term sequence dependency, and vanishing and exploding gradient in conventional deep architectures. In this paper, we propose an efficient analytics framework, which is technically a progressive machine learning technique merged with Spark-based linear models, Multilayer Perceptron (MLP) and LSTM, using a two-stage cascade structure in order to enhance the predictive accuracy. Our proposed architecture enables us to organize big data analytics in a scalable and efficient way. To show the effectiveness of our framework, we applied the cascading structure to two different real-life datasets to solve a multiclass and a binary classification problem, respectively. Experimental results show that our analytical framework outperforms state-of-the-art approaches with a high-level of classification accuracy.

ACS Style

Muhammad Ashfaq Khan; Rezaul Karim; Yangwoo Kim. A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network. Symmetry 2018, 10, 485 .

AMA Style

Muhammad Ashfaq Khan, Rezaul Karim, Yangwoo Kim. A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network. Symmetry. 2018; 10 (10):485.

Chicago/Turabian Style

Muhammad Ashfaq Khan; Rezaul Karim; Yangwoo Kim. 2018. "A Two-Stage Big Data Analytics Framework with Real World Applications Using Spark Machine Learning and Long Short-Term Memory Network." Symmetry 10, no. 10: 485.

Conference paper
Published: 20 December 2017 in Lecture Notes in Electrical Engineering
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In network intrusion detection research, two characteristics are generally considered vital to build efficient intrusion detection systems (IDSs) namely, optimal feature selection technique and robust classification schemes. However, an emergence of sophisticated network attacks and the advent of big data concepts in anomaly detection domain require the need to address two more significant aspects. They are concerned with employing appropriate big data computing framework and utilizing contemporary dataset to deal with ongoing advancements. Based on this need, we present a comprehensive approach to build an efficient IDS with the aim to strengthen academic anomaly detection research in real-world operational environments. The proposed system is a representative of the following four characteristics: It (i) performs optimal feature selection using branch-and-bound algorithm; (ii) employs logistic regression for classification; (iii) introduces bulk synchronous parallel processing to handle computational requirements of large-scale networks; and (iv) utilizes real-time contemporary dataset named ISCX-UNB to validate its efficacy.

ACS Style

Kamran Siddique; Zahid Akhtar; Yangwoo Kim. Intrusion Detection in High-Speed Big Data Networks: A Comprehensive Approach. Lecture Notes in Electrical Engineering 2017, 1364 -1370.

AMA Style

Kamran Siddique, Zahid Akhtar, Yangwoo Kim. Intrusion Detection in High-Speed Big Data Networks: A Comprehensive Approach. Lecture Notes in Electrical Engineering. 2017; ():1364-1370.

Chicago/Turabian Style

Kamran Siddique; Zahid Akhtar; Yangwoo Kim. 2017. "Intrusion Detection in High-Speed Big Data Networks: A Comprehensive Approach." Lecture Notes in Electrical Engineering , no. : 1364-1370.

Journal article
Published: 19 September 2017 in Symmetry
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Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

ACS Style

Kamran Siddique; Zahid Akhtar; Haeng-Gon Lee; Woongsup Kim; Yangwoo Kim. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks. Symmetry 2017, 9, 197 .

AMA Style

Kamran Siddique, Zahid Akhtar, Haeng-Gon Lee, Woongsup Kim, Yangwoo Kim. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks. Symmetry. 2017; 9 (9):197.

Chicago/Turabian Style

Kamran Siddique; Zahid Akhtar; Haeng-Gon Lee; Woongsup Kim; Yangwoo Kim. 2017. "Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks." Symmetry 9, no. 9: 197.

Journal article
Published: 22 November 2016 in IEEE Access
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In today's highly intertwined network society, the demand for big data processing frameworks is continuously growing. The widely adopted model to process big data is parallel and distributed computing. This paper documents the significant progress achieved in the field of distributed computing frameworks, particularly Apache Hama, a top level project under the Apache Software Foundation, based on bulk synchronous parallel processing. The comparative studies and empirical evaluations performed in this paper reveal Hama's potential and efficacy in big data applications. In particular, we present a benchmark evaluation of Hama's graph package and Apache Giraph using PageRank algorithm. The results show that the performance of Hama is better than Giraph in terms of scalability and computational speed. However, despite great progress, a number of challenging issues continue to inhibit the full potential of Hama to be used at large scale. This paper also describes these challenges, analyzes solutions proposed to overcome them, and highlights research opportunities.

ACS Style

Kamran Siddique; Zahid Akhtar; Edward J. Yoon; Young-Sik Jeong; Dipankar Dasgupta; Yangwoo Kim. Apache Hama: An Emerging Bulk Synchronous Parallel Computing Framework for Big Data Applications. IEEE Access 2016, 4, 8879 -8887.

AMA Style

Kamran Siddique, Zahid Akhtar, Edward J. Yoon, Young-Sik Jeong, Dipankar Dasgupta, Yangwoo Kim. Apache Hama: An Emerging Bulk Synchronous Parallel Computing Framework for Big Data Applications. IEEE Access. 2016; 4 ():8879-8887.

Chicago/Turabian Style

Kamran Siddique; Zahid Akhtar; Edward J. Yoon; Young-Sik Jeong; Dipankar Dasgupta; Yangwoo Kim. 2016. "Apache Hama: An Emerging Bulk Synchronous Parallel Computing Framework for Big Data Applications." IEEE Access 4, no. : 8879-8887.

Book chapter
Published: 30 August 2016 in Lecture Notes in Electrical Engineering
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In recent years, the technological advancements have led to a deluge of data from distinctive domains and the need for development of solutions based on parallel and distributed computing has still long way to go. That is why, the research and development of massive computing frameworks is continuously growing. At this particular stage, highlighting a potential research area along with key insights could be an asset for researchers in the field. Therefore, this paper explores one of the emerging distributed computing frameworks, Apache Hama. It is a Top Level Project under the Apache Software Foundation, based on bulk synchronous parallel model. We present an unbiased and critical interrogation session about Apache Hama and conclude research directions in order to assist interested researchers.

ACS Style

Kamran Siddique; Zahid Akhtar; Yangwoo Kim. Researching Apache Hama: A Pure BSP Computing Framework. Lecture Notes in Electrical Engineering 2016, 215 -221.

AMA Style

Kamran Siddique, Zahid Akhtar, Yangwoo Kim. Researching Apache Hama: A Pure BSP Computing Framework. Lecture Notes in Electrical Engineering. 2016; ():215-221.

Chicago/Turabian Style

Kamran Siddique; Zahid Akhtar; Yangwoo Kim. 2016. "Researching Apache Hama: A Pure BSP Computing Framework." Lecture Notes in Electrical Engineering , no. : 215-221.

Conference paper
Published: 01 January 2006 in Computer Vision
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Authentication, Authorization, and Accounting (AAA) for a mobile node should be accomplished by home domain when the mobile need continuous service on a visited network. The related recent studies have shown their drawback in the performance of AAA procedure. This study suggests a novel approach extending to the Fast Handoff scheme, which will shorten authentication delay by using Assertion mechanism. It allows mobile nodes to access visited network resources efficiently. Our model with Assertion process is an efficient approach employing authentication procedure through mutual and secure authentication between the Visit AAA servers. Especially, when the distance or the network delay between V_AAA and Home Agent (HA) become longer, it outperforms rather than the previous approaches. The proposed scheme verifies its significant efficiency in terms of cost analysis through several simulated experiments.

ACS Style

Seung-Yeon Lee; Eui-Nam Huh; Yang-Woo Kim; Kyesan Lee. An Efficient Authentication Mechanism for Fast Mobility Service in MIPv6. Computer Vision 2006, 3981, 905 -914.

AMA Style

Seung-Yeon Lee, Eui-Nam Huh, Yang-Woo Kim, Kyesan Lee. An Efficient Authentication Mechanism for Fast Mobility Service in MIPv6. Computer Vision. 2006; 3981 ():905-914.

Chicago/Turabian Style

Seung-Yeon Lee; Eui-Nam Huh; Yang-Woo Kim; Kyesan Lee. 2006. "An Efficient Authentication Mechanism for Fast Mobility Service in MIPv6." Computer Vision 3981, no. : 905-914.