• 한국정보통신학회논문지
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• 제21권12호
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• pp.2325-2332
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• 2017

주요정보통신기반시설의 취약점진단 등 컨설팅 프로젝트가 증가하고 있으며, 공공기관의 개인정보영향평가(PIA) 의무화와 정보보호 관리체계(ISMS) 인증 의무화 등 정보통신 분야의 법률 준수가 의무화 되면서 정보보호 컨설팅 수요는 지속적으로 증가하고 있으나 정보보호 컨설턴트 부족은 개선되고 있지 않다. 한 이유는 증가하는 정보 보호 컨설팅 수요에 맞게 정보보호 컨설턴트가 양성되고 있지 않기 때문이다. 본 논문에서는 정보보호 컨설턴트 직무를 해외 사례와 국내 사례를 살펴보고 이를 기반으로 표준화/규격화하여 해당 직무를 실무관점에서 학습하고 교육할 수 있는 방안으로 정보보호 컨설팅 업무를 시나리오로 제시하여 스스로 문제를 풀어나가는 PBL (Problem-based learning) 교육방법을 제안한다. 또한 전문가 분석을 실시하고 그 효과성을 알아본다.

• ETRI Journal
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• 제43권3호
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.91-100
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• 2023

As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

• 정보보호학회논문지
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• 제31권5호
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• pp.1055-1062
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• 2021

코로나 19의 확산으로 대면교육이 어려워지면서 이러닝 및 가상훈련의 활용이 증가하고 있다. 정보보호 교육의 경우 대응 기술을 익히는 실습이 중요하기 때문에 오래 전부터 모의 해킹과 취약점 분석 등을 가상훈련으로 지원해 오고 있다. 교육훈련 효과를 높이기 위해서는 실제 상황과 유사하게 콘텐츠를 개발하고 학습 목표를 달성하기 위한 학습 활동이 설계되어야 한다. 또한 다양한 학습 활동을 지원하는 교육훈련 시스템 품질의 우수성이 요구된다. 연구자는 비대면 교육의 핵심 요소를 고려하여 비대면 교육을 지원하는 LMS 평가지표를 개발하였으며, 이를 정보보호 교육 플랫폼에 적용하여 실무 활용성을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.327-329
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• 2022

Education is affected by technical and scientific developments. Progress in one of these areas leads give way to new educational methods and strategies. One of these advanced learning modes is what has been conventionally termed as Micro-learning (ML). It has emerged in educational technology as a result of advances in information technology as well as advances in research in memory, brain, and social-cognitive processes.In this paper, the researcher discusses micro-learning in terms of its concepts, tools, and associated concepts, advantages and disadvantages.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.224-230
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• 2022

The intent of this paper is to unveil the effectiveness of different learning environments (traditional, electronic, blended) in educational institutions through a set of dimensions: an introduction to traditional education and e-learning, the importance and objectives of e-learning, the difference between e-learning and traditional education and teachers' roles in e-learning, the challenges facing the use of e-learning. It also introduces blended learning, providing an account about its emergence, concept, importance, the difference between blended learning and e-learning, the advantages of blended learning, and the challenges confront using blended learning.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.11-17
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• 2023

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.