• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.743-751
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• 2017

As smart phones are becoming popular, an operating system is being used at wearable devices, home appliances and smart TVs. A user is able to use various applications on devices with operating system, but there is an increased threat of hacker. Thus, the technology for detecting the forgery of applications is becoming more important on operating system. To detect the forgery of the application, a digital signature technology is used on the filed of application digital signature. According to W3C recommendation, the signing process of application digital signature must be performed at least twice, and the applications which are signed by the application digital signature have to be validated for all signature files when the application is installed in the operating system. Hence, the performance of the application digital signature validation system is closely related to the installer performance on the operating system. Existing validation system has performance degradation due to redundancy of integrity verification among application components. This research was conducted to improve the performance of the application digital signature validation system. The proposal of validation system which is applied proxy system shows a performance improvement compared to the existing verification system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.39-44
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• 2020

Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.

• The KIPS Transactions:PartC
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• v.10C no.1
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• pp.17-26
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• 2003

Mobile agent system comes into the spotlight since it contributes largely to mobile computing on distributed network environment. However, this system has a number of significant security Problems. In this Paper, we analyze suity attacks to mobile agent system Presented by NIST[3]. In order to protect this system from them, we suggest a security protocol for mobile agent system by employing R based key distribution and digital multi-signature scheme. To solve these problems described in NIST, securities for mobile agent and agent platform shouid be accomplished. Comparing with other protocols, our protocol performs both of these securities, while other protocols mentioned only one of them. Proposed Protocol satisfies simplicity of key management, providing security service such as confidentiality, integrity, authentication and preventing reputation, liveness guarantee, protection of excution-result data and preventing replay attack. Furthermore, it is designed to detect message modification immediately by verifying each step of agent execution at a corresponding server.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.637-645
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• 2022

Cloud computing has been gaining popularity over decades, and container, a technology that is primarily used in cloud native applications, is also drawing attention. Although container technologies are lighter and more capable than conventional VMs, there are several security threats, such as sharing kernels with host systems or uploading/downloading images from the image registry. one of which can refer to the integrity of container images. In addition, runtime security while the container application is running is very important, and monitoring the behavior of the container application at runtime can help detect abnormal behavior occurring in the container. Therefore, in this paper, first, we implement a signing checker that automatically checks the signature of an image based on the existing Docker Content Trust (DCT) technology to ensure the integrity of the container image. Next, based on falco, an open source project of Cloud Native Computing Foundation(CNCF), we introduce newly created image for the convenience of existing falco image, and propose implementation of docker-compose and package configuration that easily builds a monitoring system.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.33-38
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• 2018

In this study, we try to detect new attacks for vehicle by learning only one class. We use Car-Hacking dataset, an intrusion detection dataset, which is used to evaluate classification performance. The dataset are created by logging CAN (Controller Area Network) traffic through OBD-II port from a real vehicle. The dataset have four attack types. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve high efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data, which are new attacks. In this study, we use one class classifier to detect new attacks that are difficult to detect using signature-based rules on network intrusion detection system. The proposed method suggests a combination of parameters that detect all new attacks and show efficient classification performance for normal dataset.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.53-63
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• 2019

To provide a hand gesture interface through deep learning in mobile environments, research on the light-weighting of networks is essential for high recognition rates while at the same time preventing degradation of execution speed. This paper proposes a method of real-time recognition of written characters in the air using a finger on mobile devices through the light-weighting of deep-learning model. Based on the SSD (Single Shot Detector), which is an object detection model that utilizes MobileNet as a feature extractor, it detects index finger and generates a result text image by following fingertip path. Then, the image is sent to the server to recognize the characters based on the learned OCR model. To verify our method, 12 users tested 1,000 words using a GALAXY S10+ and recognized their finger with an average accuracy of 88.6%, indicating that recognized text was printed within 124 ms and could be used in real-time. Results of this research can be used to send simple text messages, memos, and air signatures using a finger in mobile environments.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.411-418
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• 2020

In the field of information security, IDS(Intrusion Detection System) is normally classified in two different categories: signature-based IDS and anomaly-based IDS. Many studies in anomaly-based IDS have been conducted that analyze network traffic data generated in cyberspace by machine learning algorithms. In this paper, we studied pre-processing methods to overcome performance degradation problems cashed by rare classes. We experimented classification performance of a Machine Learning algorithm by reconstructing data set based on rare classes and semi rare classes. After reconstructing data into three different sets, wrapper and filter feature selection methods are applied continuously. Each data set is regularized by a quantile scaler. Depp neural network model is used for learning and validation. The evaluation results are compared by true positive values and false negative values. We acquired improved classification performances on all of three data sets.

• Smart Media Journal
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• v.5 no.2
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• pp.77-83
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• 2016

Today, the great revolution in all financial industrial such as bank have been progressing through the utilization of IT technology actively, it is called the fintech. In this paper, we draw the draft design of NFC-based electronic payment and coupon system using FIDO framework to apply the 2 factor authentication technique for strength security. In detailed, we will study that the terminal device in front-end will be applied the 2 factor authentication and electric signature, and cloud-based payment gateway in back-end will be applied malicious code detection technique of distributed avoidance type.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1199-1206
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• 2021

Accordingly, attention is also being paid to the agricultural use of remote sensing technique that non-destructively and continuously detects the growth and physiological status of crops. However, when remote sensing techniques are used for crop monitoring, it is possible to continuously monitor the abnormality of crops in real time. For this, standard growth information of crops is required and relative growth considering the cultivation environment must be identified. With the relationship between GDD (Growing Degree Days), which is the cumulative temperature related to crop growth obtained from ideal cultivation management, and the vegetation index as standard growth information, compared with the vegetation index observed with the spectralreflectance sensor(SRS_NDVI & SRS_PRI) in each rice paddy treated with standard cultivation management and non-fertilized, it was quantitatively identified as a time series. In the future, it is necessary to accumulate a database targeting various climatic conditions and varieties in the standard cultivation management area to establish a more reliable standard growth information.