• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1307-1323
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• 2014

With the exhaustion of global IPv4 addresses, IPv6 technologies have attracted increasing attentions, and have been deployed widely. Meanwhile, new applications running over IPv6 networks will change the traditional traffic characteristics obtained from IPv4 networks. Traditional models obtained from IPv4 cannot be used for IPv6 network monitoring directly and there is a need to investigate those changes. In this paper, we explore the flow features of IPv6 traffic and compare its difference with that of IPv4 traffic from flow level. Firstly, we analyze the differences of the general flow statistical characteristics and users' behavior between IPv4 and IPv6 networks. We find that there are more elephant flows in IPv6, which is critical for traffic engineering. Secondly, we find that there exist many one-way flows both in the IPv4 and IPv6 traffic, which are important information sources for abnormal behavior detection. Finally, in light of the challenges of analyzing massive data of large-scale network monitoring, we propose a group flow model which can greatly reduce the number of flows while capturing the primary traffic features, and perform a comparative measurement analysis of group users' behavior dynamic characteristics. We find there are less sharp changes caused by abnormity compared with IPv4, which shows there are less large-scale malicious activities in IPv6 currently. All the evaluation experiments are carried out based on the traffic traces collected from the Northwest Regional Center of CERNET (China Education and Research Network), and the results reveal the detailed flow characteristics of IPv6, which are useful for traffic management and anomaly detection in IPv6.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.63-70
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• 2008

The authors, Tom Karygiannis of NIST, and Bernard Eydt, Greg Barber, Lynn Bunn, and Ted Phillips of Booz Allen Hamilton, wish to thank Steven Fick, Rick Korchak, Kate Remley, Jeff Guerrieri, Dylan Williams, Karen Scarfone, and Tim Grance of NIST, and Kenneth Waldrop and Beth Mallory of Booz Allen Hamilton. These individuals reviewed drafts of this document and contributed to its technical content. The authors would also like to express their thanks to several experts for their critical review and feedback on drafts of the publication. These experts include V.C. Kumar of Texas Instruments; Simson Garfinkel of the Naval Postgraduate School; Peter Sand of the Department of Homeland Security; Erika McCallister of MITRE; and several professionals supporting Automatic Identification Technology(AIT) program offices within the Department of Defense(DoD), especially Nicholas Tsougas, Fred Naigle, Vince Pontani, Jere Engelman, and Kathleen Smith. During the public comment period we received helpful comments from the following Federal Government agencies: the US Departments of Defense, Health and Human Services, Homeland Security, Labor, and State; the Office of the Director of National Intelligence; the Office of Management and Budget; and the General Services Administration. We also received several helpful contributions from commercial industry, including comments from EPCglobal, VeriSign, and Priway. Finally, the authors wish to thank the following individuals for their comments and assistance: Brian Tiplady, Daniel Bailey, Paul Dodd, Craig K. Harmon, William MacGregor, Ted Winograd, Russell Lange, Perry F. Wilson, John Pescatore, Ronald Dugger, Stephan Engberg, Morten Borup Harning, Matt Sexton, Brian Cute, Asterios Tsibertzopoulos, Mike Francis, Joshua Slob in, Jack Harris, and Judith Myerson.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.605-610
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• 2020

Software Defined Networking (SDN) is setting the standard for the management of networks due to its scalability, flexibility and functionality to program the network. The Distributed Denial of Service (DDoS) attack is most widely used to attack the SDN controller to bring down the network. Different methodologies have been utilized to detect DDoS attack previously. In this paper, first the dataset is obtained by Kaggle with 84 features, and then according to the rank, the 20 highest rank features are selected using Permutation Importance Algorithm. Then, the datasets are trained and tested with Convolution Neural Network (CNN) classifier model by utilizing deep learning techniques. Our proposed solution has achieved the best results, which will allow the critical systems which need more security to adopt and take full advantage of the SDN paradigm without compromising their security.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• Genomics & Informatics
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• v.20 no.4
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• pp.45.1-45.7
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• 2022

Food security will be affected by climate change worldwide, particularly in the developing world, where the most important food products originate from plants. Plants are often exposed to environmental stresses that may affect their growth, development, yield, and food quality. Auxin is a hormone that plays a critical role in improving plants' tolerance of environmental conditions. Auxin controls the expression of many stress-responsive genes in plants by interacting with specific cis-regulatory elements called auxin-responsive elements (AuxREs). In this work, we performed an in silico prediction of AuxREs in promoters of five auxin-responsive genes in Zea mays. We applied a data fusion approach based on the combined use of Dempster-Shafer evidence theory and fuzzy sets. Auxin has a direct impact on cell membrane proteins. The short-term auxin response may be represented by the regulation of transmembrane gene expression. The detection of an AuxRE in the promoter of prolyl oligopeptidase (POP) in Z. mays and the 3-fold overexpression of this gene under auxin treatment for 30 min indicated the role of POP in maize auxin response. POP is regulated by auxin to perform stress adaptation. In addition, the detection of two AuxRE TGTCTC motifs in the upstream sequence of the bx1 gene suggests that bx1 can be regulated by auxin. Auxin may also be involved in the regulation of dehydration-responsive element-binding and some members of the protein kinase superfamily.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.235-245
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• 2023

Smart applications based on the Network of Everything also known as Internet of Everything (IoE) are increasing popularity as network connectivity requires rise further. As a result, there will be a greater need for developing 6G technologies for wireless communications in order to overcome the primary limitations of visible 5G networks. Furthermore, implementing neural networks into 6G will bring remedies for the most complex optimizing networks challenges. Future 6G mobile phone networks must handle huge applications that require data and an increasing amount of users. With a ten-year time skyline from thought to the real world, it is presently time for pondering what 6th era (6G) remote correspondence will be just before 5G application. In this article, we talk about 6G dreams to clear the street for the headway of 6G and then some. We start with the conversation of imaginative 5G organizations and afterward underline the need of exploring 6G. Treating proceeding and impending remote organization improvement in a serious way, we expect 6G to contain three critical components: cell phones super broadband, very The Web of Things (or IoT and falsely clever (artificial intelligence). The 6G project is currently in its early phases, and people everywhere must envision and come up with its conceptualization, realization, implementation, and use cases. To that aim, this article presents an environment for Presented Distributed Artificial Intelligence as-a-Services (DAIaaS) supplying in IoE and 6G applications. The case histories and the DAIaaS architecture have been evaluated in terms of from end to end latency and bandwidth consumption, use of energy, and cost savings, with suggestion to improve efficiency.

• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.79-87
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• 2005

In e-commerce applications, a public key cryptosystem is an important and indispensible element for the basic security operations such as authentication, digital signaturing, and key distribution. In wired network environments, the public key infrastructure certificate, which is based on X.509 specification, has been widely used. On the other hand, it still remains difficult to use the certificate information in wireless network environments due to the inherent limitations of the hand-held devices such as low computational power and short battery life. In this paper, we facilitate a codesign approach by implementing a software public-key cryptosystem and classifying its internal computation overheads quantitatively using a software profiling technique. Moreover, we propose a method to analyze the profiled data and apply it to the problem of software/hardware partitioning in a codesign approach. As an illustrative example, we analyze the computational overheads of an EC-Elfagamal application and examine a critical computational path.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5150-5169
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• 2015

Mobile Ad hoc NETworks (MANETs) are the best choice when mobility, scalability, and decentralized network infrastructure are needed. Because of critical mission applications of MANETs, network security is the vital requirement. Most routing protocols in MANETs assume that every node in the network is trustworthy. However, due to the open medium, the wide distribution, and the lack of nodes' physical protection, attackers can easily compromise MANETs by inserting misbehaving nodes into the network that make blackhole attacks. Previous research to detect the misbehaving nodes in MANETs used the overhearing methods, or additional ACKnowledgement (ACK) packets to confirm the reception of data packets. In this paper a special lightweight acknowledgement-based method is developed that, contrary to existing methods, it uses ACK packets of MAC layer instead of adding new ACK packets to the network layer for confirmations. In fact, this novel method, named PIGACK, uses ACK packets of MAC 802.11 to piggyback confirmations from a receiver to a sender in the same transmission duration that the sender sends a data packet to the receiver. Analytical and simulation results show that the proposed method considerably decreases the network overhead and increases the packet delivery ratio compared to the well-known method (2ACK).

• Korean Institute of Interior Design Journal
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• v.22 no.1
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• pp.85-96
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• 2013

This study relates to meta-phenomena in architectural design. Among others, this study aims to interpret and demonstrate the cognitive thoughts and methodological systems in 'autonomy and instrumentality' presented in works of art by positivist architects focusing on diagrams after the second modernity in addition to earlier formal experiments by John Hejduk, Peter Eisenman, Bernard Tschumi, and Daniel Libeskind. In order to achieve these aims, this study approached the mimetic concepts developed by Walter Benjamin and Theodor Adorno. Especially, meta-phenomena in the methods of architectural design were connected to productivity in Adorno's mimetic concepts. Also, in terms of formation and creation of works of art, the mimetic backgrounds of Adorno's theories on aesthetics were identified from features of concepts on the part of formal experiments. The results were systemized methodologically based on meta-phenomena appearing in pure arts and overall architectural design. These were presented as a framework to interpret 'autonomy and instrumentality' that exist in the working of negativity and mimesis. In this way, logics and intuition in architectural design as well as methodological systemization of convergent creativity were proved valid. In conclusion, Adorno's mimetic concepts systemized based on negativity and critical awareness may lead to new concepts. It has been proved that it is valid for security of meta-phenomena of architectural design as a production of autonomous spaces for differences and creation.