• Convergence Security Journal
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• v.22 no.4
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• pp.17-24
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• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.1-9
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• 2024

Although federated learning is designed to be safer than centralized methods in terms of security and privacy, it still has many vulnerabilities. An attacker performing an adversarial attack intentionally manipulates the deep learning model by injecting carefully crafted input data, that is, adversarial examples, into the client's training data to induce misclassification. A common defense strategy against this is so-called adversarial training, which involves preemptively learning the characteristics of adversarial examples into the model. Existing research assumes a scenario where all clients are under adversarial attack, but considering the number of clients in federated learning is very large, this is far from reality. In this paper, we experimentally examine aspects of adversarial training in a scenario where some of the clients are under attack. Through experiments, we found that there is a trade-off relationship in which the classification accuracy for normal samples decreases as the classification accuracy for adversarial examples increases. In order to effectively utilize this trade-off relationship, we present a method to perform adversarial training by adaptively selecting a loss function depending on whether the client is attacked.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.273-277
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• 2013

Phishing attacks have been implemented in smarter, more advanced ways with the passage of time. Hackers use intelligent phishing attacks to take over computers and to penetrate internal networks in major organizations. So, in this paper, a model for a prevention of phishing attack spread is conceptual designed in order to protect internal users and sensitive or important information from sophisticated phishing attacks. Internal users simultaneously utilize both external web and organizational mail services. And hackers can take the both side equally as a vector. Thus, packets in each service must be monitored and stored to recognize threatening elements from both sides. The model designed in this paper extends the mail server based security structure used in conventional studies for the protection of Internet mail services accessed by intranet users. This model can build a list of phishing sites as the system checks e-mails compared to that of the method that directly intercepts accesses to phishing sites using a proxy server, so it represents no standby time for request and response processes.

• Asian Journal of Innovation and Policy
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• v.5 no.1
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• pp.78-91
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• 2016

To counter-attack against piracy, the movie industry is continuously developing new technologies for the protection of intellectual properties, only to find them instantly useless especially in the digital age. This study shifts the focus from technology to customer behavior, and analyzes customer behaviors vis-à-vis piracy using economic models. The theoretical model of optimal holdback strategy under the threat of piracy was derived and the result shows that holdback can be used as a tool not only for hedging the loss due to piracy, but also for reducing piracy. Based on the theoretical model, we suggested proper holdback strategy for each type of movie piracy.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.4
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• pp.428-434
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• 2012

Missiles suffer from flight instability problems at high angles of attack, since vortex flow over a fuselage cause lateral force to the body. To overcome this problem at a high angle of attack, the development of a real time vortex controller is needed. In this paper, Proper Orthogonal Decomposition (POD) and feedback controllers are developed for real time vortex control. The POD method is one of the most well known techniques for modeling low order models that represent the original full-order model. An adaptive control algorithm is used for real time control.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.3
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• pp.199-205
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• 2002

In this paper, the optimum shape and arrangement of ribs in the channel of a plate heat exchanger are studied. The following dimensionless geometric parameters of ribs are selected as design variables: rib height ($H_R$), angle of attack ($\beta$), rib pitch ($P_R$), rib distance (L) and aspect ratio of rib (AR). The optimization is performed by minimizing the objective function consisting of the Nusselt number and the friction factor. The optimal values of design variables are as follows: $H_R$=0.263, $\beta$=0.290, $P_R$=3.142, L: 3.954, AR=0.342. The pressure drop and the heat transfer of the optimum model, compared to those of the reference model, are increased by 15.1% and 41.6%, respectively.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.297-319
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• 2016

Football match predictions are of great interest to fans and sports press. In the last few years it has been the focus of several studies. In this paper, we propose the Poisson regression model in order to football match outcomes. We applied the proposed methodology to two national competitions: the 2012-2013 English Premier League and the 2015 Brazilian Football League. The number of goals scored by each team in a match is assumed to follow Poisson distribution, whose average reflects the strength of the attack, defense and the home team advantage. Inferences about all unknown quantities involved are made using a Bayesian approach. We calculate the probabilities of win, draw and loss for each match using a simulation procedure. Besides, also using simulation, the probability of a team qualifying for continental tournaments, being crowned champion or relegated to the second division is obtained.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.873-883
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• 2013

Security vulnerability quantification is the method that identify potential vulnerabilities by scoring vulnerabilities themselves and their countermeasures. However, due to the structural feature of smart grid system, it is difficult to apply existing security threat evaluation schemes. In this paper, we propose a network model to evaluate smartgrid security threat for AMI and derive attack scenarios. Additionally, we show that the result of security threat evaluation for proposed network model and attack scenario by applying MTTC scheme.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.8
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• pp.635-642
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• 2009

The three-dimensional turbulent wingtip vortex flows have been examined in the present study by using the commercial code FLUENT. The standard ${\kappa}-{\varepsilon}$ model is used as a closure relationship. The wing is constructed by using an elliptic body whose aspect ratio is 3.8 and the NACA 16-020 airfoil section. The simulations for various angle attack (${\alpha}=0^{\circ}$, $5^{\circ}$, and $10^{\circ}$) are carried out. The effect of Reynolds number is also investigated in this study. As the angle attack increases, the wingtip vortex becomes stronger. However, the relative vortex strength to inlet velocity decreases as Reynolds number increases.