• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.639-653
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• 2013

Smart grid is a network of computers and power infrastructure that monitor and manage energy usage efficiently. Recently, the smart grid demonstration projects around the world, including the United States, Europe, Japan, and the technology being developed. The protection of the many components of the grid against cyber-threats has always been critical, but the recent Smart grid has been threatened by a variety of cyber and physical attacks. We model and analyze advanced metering infrastructure(AMI) in smart grid. Using attack countermeasure tree(ACT) we show qualitative and probabilistic security analysis of AMI. We implement using SHARPE(Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool and calculate probability, ROA, ROI, Structure Importance, Birnbaum Importance.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1184-1192
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• 2000

Multicast has communication mechanism that is able to transfer voice, video for only the specific user group. As compared to unicast, multicast is more susceptive to attack such as masquerading, malicious replay, denial of service, repudiation and traffic observation, because of the multicast has much more communication links than unicast communication. Multicast-specific security threats can affect not only a group's receivers, but a potentially large proportion of the internet. In this paper, we proposed the multicast security model that is able to secure multi-group communication in CBT(Core Based Tree), which is multicast routing. And designed the multicast key distribution protocol that can offer authentication, user privacy using core (be does as Authentication Server) in the proposed model.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.324-330
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• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5260-5275
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• 2019

In the process of constructing the traditional offensive and defensive game theory model, these are some shortages for considering the dynamic change of security risk problem. By analysing the critical indicators of the incomplete information game theory model, incomplete information attack and defense game theory model and the mathematical engineering method for solving Bayes-Nash equilibrium, the risk-averse income function for information assets is summarized as the problem of maximising the return of the equilibrium point. To obtain the functional relationship between the optimal strategy combination of the offense and defense and the information asset security probability and risk probability. At the same time, the offensive and defensive examples are used to visually analyse and demonstrate the incomplete information game and the Harsanyi conversion method. First, the incomplete information game and the Harsanyi conversion problem is discussed through the attack and defense examples and using the game tree. Then the strategy expression of incomplete information static game and the engineering mathematics method of Bayes-Nash equilibrium are given. After that, it focuses on the offensive and defensive game problem of unsafe information network based on risk aversion. The problem of attack and defense is obtained by the issue of maximizing utility, and then the Bayes-Nash equilibrium of offense and defense game is carried out around the security risk of assets. Finally, the application model in network security penetration and defense is analyzed by designing a simulation example of attack and defense penetration. The analysis results show that the constructed income function model is feasible and practical.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.209-215
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• 2018

In this paper, we propose a method to detect and block Meltdown malicious code which is increasing rapidly using dynamic sandbox tool. Although some patches are available for the vulnerability of Meltdown attack, patches are not applied intentionally due to the performance degradation of the system. Therefore, we propose a method to overcome the limitation of existing signature detection method by using machine learning method for infrastructures without active patches. First, to understand the principle of meltdown, we analyze operating system driving methods such as virtual memory, memory privilege check, pipelining and guessing execution, and CPU cache. And then, we extracted data by using Linux strace tool for detecting Meltdown malware. Finally, we implemented a decision tree based dynamic detection mechanism to identify the meltdown malicious code efficiently.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.1
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• pp.27-34
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• 2018

Various messages generated in vehicles are transmitted based on the wireless telecommunication which is a core technology of vehicle to everything (V2X). However, the hackers attack them upon penetration to the system and network to cause the generation of users' inconveniences for vehicular communication. Moreover, huge damage could be occurred in terms of physical and materialistic areas if the users in the vehicles were attacked in the communication environment. Therefore, this study was to design the safe communication protocol using hash tree technique in the V2X environments. Using hash tree technique, processes of issuing certificate and registration and communication protocol were designed, and safety analysis was performed on the attacking technique which is occurred in the existing vehicles. Approximately 62% of decrease in the capacity analysis was found upon comparative analysis of telecommunication processes with the system to issue the certificate which is used in the existing vehicles.

• Journal of the Korean Wood Science and Technology
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• v.46 no.6
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• pp.748-755
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• 2018

Timber from fast-growing tree species is susceptible to by biodeterioration attack, particularly subterranean termites. Impregnation with methyl methacrylate (MMA) potentially increases wood resistance to subterranean termite attack. Four wood species, namely sengon (Falcataria moluccana), jabon (Anthocephalus cadamba), mangium (Acacia mangium), and pine (Pinus merkusii), were impregnated with MMA, and samples of untreated and imidacloprid-preserved wood were prepared for comparison purposes. Small stakes, sized 0.8 cm by 2 cm in cross section by 20 cm in the longitudinal direction, were inserted into the ground for 3 months, and the weight loss of each specimen was determined at the end of the test period. A factorial $4{\times}3$ completely randomized design was used for data analysis; the first factor was wood species, and the second factor was treatment. The results showed that MMA polymer loadings were 27.88%, 24.91%, 14.14%, and 17.81% for sengon, jabon, mangium, and pine, respectively, and amounts of imidacloprid retention were $7.56kg/m^3$, $5.98kg/m^3$, $5.34kg/m^3$, and $9.53kg/m^3$, respectively. According to an analysis of variance, wood species, treatment, and interaction of both factors significantly affected the weight loss of wood specimens. Mangium had the smallest weight loss, followed by pine, sengon, and jabon. MMA impregnation into the wood increased the resistance of wood samples to subterranean termite attack during in-ground testing, but the resistance level was lower than that of imidacloprid-preserved wood. Except for mangium wood, the MMA treatment did not significantly affect resistance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.839-850
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• 2014

As the social and financial damages caused by APT attack such as 3.20 cyber terror are increased, the technical solution against APT attack is required. It is, however, difficult to protect APT attack with existing security equipments because the attack use a zero-day malware persistingly. In this paper, we propose a host based anomaly detection method to overcome the limitation of the conventional signature-based intrusion detection system. First, we defined 39 features to identify between normal and abnormal behavior, and then collected 8.7 million feature data set that are occurred during running both malware and normal executable file. Further, each process is represented as 83-dimensional vector that profiles the frequency of appearance of features. the vector also includes the frequency of features generated in the child processes of each process. Therefore, it is possible to represent the whole behavior information of the process while the process is running. In the experimental results which is applying C4.5 decision tree algorithm, we have confirmed 2.0% and 5.8% for the false positive and the false negative, respectively.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.226-233
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• 2016

Cloud computing is a distributed computing model that has lot of drawbacks and faces difficulties. Many new innovative and emerging techniques take advantage of its features. In this paper, we explore the security threats to and Risk Assessments for cloud computing, attack mitigation frameworks, and the risk-based dynamic access control for cloud computing. Common security threats to cloud computing have been explored and these threats are addressed through acceptable measures via governance and effective risk management using a tailored Security Risk Approach. Most existing Threat and Risk Assessment (TRA) schemes for cloud services use a converse thinking approach to develop theoretical solutions for minimizing the risk of security breaches at a minimal cost. In our study, we propose an improved Attack-Defense Tree mechanism designated as iADTree, for solving the TRA problem in cloud computing environments.