• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.248-254
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• 2022

Educational innovations are first created, improved or applied educational, didactic, educative, and managerial systems and their components that significantly improve the results of educational activities. The development of pedagogical technology in the global educational space is conventionally divided into three stages. The role of innovative technologies in Higher School practice is substantiated. Factors of effectiveness of the educational process are highlighted. Technology is defined as a phenomenon and its importance is emphasized, it is indicated that it is a component of human history, a form of expression of intelligence focused on solving important problems of being, a synthesis of the mind and human abilities. The most frequently used technologies in practice are classified. Among the priority educational innovations in higher education institutions, the following are highlighted. Introduction of modular training and a rating system for knowledge control (credit-modular system) into the educational process; distance learning system; computerization of libraries using electronic catalog programs and the creation of a fund of electronic educational and methodological materials; electronic system for managing the activities of an educational institution and the educational process. In the educational process, various innovative pedagogical methods are successfully used, the basis of which is interactivity and maximum proximity to the real professional activity of the future specialist. There are simulation technologies (game and discussion forms of organization); technology "case method" (maximum proximity to reality); video training methodology (maximum proximity to reality); computer modeling; interactive technologies; technologies of collective and group training; situational modeling technologies; technologies for working out discussion issues; project technology; Information Technologies; technologies of differentiated training; text-centric training technology and others.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.545-552
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• 2012

The rapid development of information technology is making large changes in our lives today. Also the infrastructure and services are combinding with information technology which predicts another huge change in our environment. However, the development of information technology brings various types of side effects and these side effects not only cause financial loss but also can develop into a nationwide crisis. Therefore, the detection and quick reaction towards these side effects is critical and much research is being done. Intrusion detection systems can be an example of such research. However, intrusion detection systems mostly tend to focus on judging whether particular traffic or files are malicious or not. Also it is difficult for intrusion detection systems to detect newly developed malicious codes. Therefore, this paper proposes a method which determines whether the present network model is normal or abnormal by comparing it with past network situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.309-317
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• 2015

Recently many companies began to realize their visions for the sustainable growth with the advent of CSV(Creating Shared Values). Michael E. Porter, a Harvard Professor, claims that placing social value creation at the core of business strategy has the potential to uncover big opportunities for individual companies and that shared value can play a significant role in increasing competitive advantages while fostering social prosperity. In consequence, the various researches have illustrated how to get the opportunity for competitive advantages from building a social value proposition into corporate strategy, and considerable studies have been promoted heavily from the managerial perspective. However, due to the lack of capability converging information technology with business strategy, any research effort to identify technological or Internet-related issues and to link the issues to CSV does not exist. With Korean being a Internet leading country, the demands of researches analyzing core technology, information, and service utilizing Internet are rapidly growing. The study aims to find out Internet-related enablers for CSV. This paper describes the concepts and features of CSV, identifies emerging Internet-related issues toward the opportunity for competitive advantage, and then depicts the rigorous research endeavors in the areas of Internet information, technology, and services. As a result, 11 papers presented and selected as the outstanding papers at APIC-IST 2014 handle the issues to be brought together, which include: Wireless and Sensor Network, Image Processing and HCI, Big Data and Business Intelligence, Security & Privacy in Internet, SNS & Communication, Smart-Learning and e-Learning, and Internet Business Strategy. The study finally recommends indispensible terms for substantially vitalizing CSV.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.361-367
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• 2021

Information search in video streaming services such as YouTube is replacing traditional information search services. To find desired detailed information in such a video, users should repeatedly navigate several points in the video, resulting in a waste of time and network traffic. In this paper, we propose a method to assist users in searching for information in a video by using DBSCAN clustering and LSTM. Our LSTM model is trained with a dataset that consists of user search sequences and their final target points categorized by DBSCAN clustering algorithm. Then, our proposed method utilizes the trained model to suggest an expected category for the user's desired target point based on a partial search sequence that can be collected at the beginning of the search. Our experiment results show that the proposed method successfully finds user destination points with 98% accuracy and 7s of the time difference by average.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.847-856
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• 2013

We propose a modified learning process for generalized neural network using a learning algorithm by Liu et al. (2001). We consider the effect of initial weights, training results and learning errors using a modified learning process. We employ an incremental training procedure where training patterns are learned systematically. Our algorithm starts with a single training pattern and a single hidden layer neuron. During the course of neural network training, we try to escape from the local minimum by using a weight scaling technique. We allow the network to grow by adding a hidden layer neuron only after several consecutive failed attempts to escape from a local minimum. Our optimization procedure tends to make the network reach the error tolerance with no or little training after the addition of a hidden layer neuron. Simulation results with suitable initial weights indicate that the present constructive algorithm can obtain neural networks very close to minimal structures and that convergence to a solution in neural network training can be guaranteed. We tested these algorithms extensively with small training sets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.93-107
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• 2016

Game bot playing is one of the main risks in Massively Multi-Online Role Playing Games(MMORPG) because it damages overall game playing environment, especially the balance of the in-game economy. There have been many studies to detect game bot. However, the previous detection models require continuous maintenance efforts to train and learn the game bots' patterns whenever the game contents change. In this work, we have proposed a machine learning technique using the self-similarity property that is an intrinsic attribute in game bots and automated maintenance system. We have tested our method and implemented a system to major three commercial games in South Korea. As a result, our proposed system can detect and classify game bots with high accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.371-379
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• 2022

Darknet is based on the characteristics of anonymity and security, and this leads darknet to be continuously abused for various crimes and illegal activities. Therefore, it is very important to detect and classify darknet traffic to prevent the misuse and abuse of darknet. This work proposes a novel approach, which uses the Gradient Boosting techniques for darknet traffic detection and classification. XGBoost and LightGBM algorithm achieve detection accuracy of 99.99%, and classification accuracy of over 99%, which could get more than 3% higher detection accuracy and over 13% higher classification accuracy, compared to the previous research. In particular, LightGBM algorithm could detect and classify darknet traffic in a way that is superior to XGBoost by reducing the learning time by about 1.6 times and hyperparameter tuning time by more than 10 times.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1171-1181
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• 2022

Front Collision-Avoidance Assist (FCA) or Smart Cruise Control (SCC) is installed in a modern vehicle, and the amount of data exchange between ECUs increases rapidly. Therefore, Automotive Ethernet, especially SOME/IP, which supports wide bandwidth and two-way communication, is widely adopted to overcome the bandwidth limitation of traditional CAN communication. SOME/IP is a standard protocol compatible with various automobile operating systems, and improves connectivity between components in the vehicle. However, no encryption or authentication process is defined in the SOME/IP protocol itself. Therefore, there is a need for a security study on the SOME/IP protocol. This paper proposes a deep learning-based intrusion detection system in SOME/IP and performs six attacks to confirm the performance of the intrusion detection system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1081-1090
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• 2022

The face recognition model is used for identity recognition of smartphones, providing convenience to many users. As a result, the security review of the DNN model is becoming important, with adversarial attacks present as a well-known vulnerability of the DNN model. Adversarial attacks have evolved to decision-based attack techniques that use only the recognition results of deep learning models to perform attacks. However, existing decision-based attack technique[14] have a problem that requires a large number of queries when generating adversarial examples. In particular, it takes a large number of queries to approximate the gradient. Therefore, in this paper, we propose a method of generating adversarial examples using orthogonal space sampling and dimensionality reduction sampling to avoid wasting queries that are consumed to approximate the gradient of existing decision-based attack technique[14]. Experiments show that our method can reduce the perturbation size of adversarial examples by about 2.4 compared to existing attack technique[14] and increase the attack success rate by 14% compared to existing attack technique[14]. Experimental results demonstrate that the adversarial example generation method proposed in this paper has superior attack performance.