• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2149-2170
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• 2017

Application-layer Distributed Denial-of-Service (DDoS) attack is one of the leading security problems in the Internet. In recent years, the attack strategies of application-layer DDoS have rapidly developed. This paper introduces a new attack strategy named Path Vulnerabilities-Based (PVB) attack. In this attack strategy, an attacker first analyzes the contents of web pages and subsequently measures the actual response time of each webpage to build a web-resource-weighted-directed graph. The attacker uses a Top M Longest Path algorithm to find M DDoS vulnerable paths that consume considerable resources when sequentially accessing the pages following any of those paths. A detection mechanism for such attack is also proposed and discussed. A finite-state machine is used to model the dynamical processes for the state of the user's session and monitor the PVB attacks. Numerical results based on real-traffic simulations reveal the efficiency of the attack strategy and the detection mechanism.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.26-33
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• 2007

Social cost has been increased by traffic accident and congestion since early 1990s. The construction of roadways and railways has been suggested as countermeasures. However, ITS has finally introduced as a logical solution because the expenses of infrastructures are costly. The data collection field has developed through numerous researches and pilot projects. However the information provision field does need a lot of study. The traffic information broadcasting whether simple traffic information or the value-added information has been available via radio, television and internet which does not require tremendous investment compared with data collection stage. Therefore, this study reviews the suitability of the gadget service usually offered by window vista users which is the result of the development of technology and the changes of internet environment. It also suggests to using the RSS(Really Simple Syndication) manner as a basic method to provide the traffic information based on the needs of user in order to enhance the usability of traffic information. For this, this study analyzes the current methods and techniques of traffic information service which is widely available by local governments and companies and suggest possible changes and methods in order to provide Gadget-based service to the public.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.19-31
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• 2014

In this paper, a parallel backoff delay procedure on multiple IEEE 802.15.4 channels and a channel searching method considering the frequency spectrum of WiFi traffic are studied for IEEE 802.15.4 nodes to avoid the interference from WiFi traffic. In order to search the channels being occupied by WiFi traffic, we analyzed the methods measuring the powers of adjacent channels simultaneously, checking the duration of measured power levels greater than a threshold, and finding the same periodicity of sampled RSSI data as the beacon frame by signal processing. In an wireless channel overlapped with IEEE 802.11 network, the operation of CSMA-CA algorithm for IEEE 802.15.4 nodes is explained. A method to execute a parallel backoff procedure on multiples IEEE 802.15.4 channels by an IEEE 802.15.4 device is proposed with the description of its algorithm. When we analyze the data measured by the experimental system implemented with the proposed method, it is observed that medium access delay times increase at the same time in the associated IEEE 802.15.4 channels that are adjacent each other during the generation of WiFi traffic. A channel evaluation function to decide the interference from other traffic on an IEEE 802.15.4 channel is defined. A channel searching method considering the channel evaluations on the adjacent channels together is proposed in order to search the IEEE 802.15.4 channels interfered by WiFi, and the experimental results show that it correctly finds the channels interfered by WiFi traffic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.203-209
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• 2014

Today, Distributed denial of service (DDoS) attack present a very serious threat to the stability of the internet. The DDoS attack, which is consuming all of the computing or communication resources necessary for the service, is known very difficult to protect. The DDoS attack usually transmits heavy traffic data to networks or servers and they cannot handle the normal service requests because of running out of resources. It is very hard to prevent the DDoS attack. Therefore, an intrusion detection system on large network is need to efficient real-time detection. In this paper, we propose the detection mechanism using analysis of flow information against DDoS attacks in order to guarantee the transmission of normal traffic and prevent the flood of abnormal traffic. The OPNET simulation results show that our ideas can provide enough services in DDoS attack.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1204-1227
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• 2020

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.49-57
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• 2012

The Internet succeeded because of the end-to-end principle which allowed anyone to add functionality to the network. However, as the internet is increasingly becoming the platform for smart IT applications such as VoIP, IPTV, Cloud Computing and Smart Phone, networks are now under increasing strain of traffic congestion and the absence of quality of service insurances. To date, the debate over internet rules has focused on network neutrality rather than network efficiency. This article emphasizes the well-functioning role of market mechanism for the efficient use and further development of the network. To maximize the value of the network, this article proposes a differential treatment to packets based on customer types, and a two-part tariff pricing rule to secure funding to expand and upgrade networks.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.557-573
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• 2015

In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.751-758
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• 2020

As the application fields using various types of communication, including the Internet of Things, have emerged, the form of communication has been diversified. Some applications require fast feedback and the others continue to send data regardless of whether it is received or not. Transmitting data in one way can be acknowledged by the opposite direction response. These information exchanges form a two-way communication. For applications that need to issue commands remotely, such as network control systems, it is important to give a fast response because the sender decides the next action based on the response from the recipient. In this paper, we propose the base station (BS) cooperation to improve the latency performance of the two-way communication in cellular networks. We design the two-way communication strategy utilizing cooperating BSs with the same direction of traffic as well as bidirectional traffic. We show that the proposed scheme improves the latency performance than the previous works.