• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.391-404
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• 2022

Aron Gohr proposed a cryptanalysis method based on deep learning technology for the lightweight block cipher Speck. This is a method that enables a chosen plaintext attack with higher accuracy than the classical differential cryptanalysis. In this paper, by using the probability distribution, we analyze the mechanism of such deep learning based cryptanalysis and propose the results applied to the lightweight block cipher Simon. In addition, we examine that the probability distributions of the predicted values of the neural networks within the cryptanalysis working processes are different depending upon the characteristics of round functions of Speck and Simon, and suggest a direction to improve the efficiency of the neural distinguisher which is the core technology of Aron Gohr's cryptanalysis.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.21-39
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• 2021

The acceptance of smartphone applications in the learning field is one of the most significant challenges for higher education institutions in Saudi Arabia. These institutions serve large and varied sectors of society and have a tremendous impact on the knowledge gained by student segments at various ages. M-learning is of great importance because it provides access to learning through a wide range of mobile networks and allows students to learn at any time and in any place. There is a lack of quality requirements for M-learning applications in Saudi societies partly because of mandates for high levels of privacy and gender segregation in education (Garg, 2013; Sarrab et al., 2014). According to the Saudi Arabian education ministry policy, gender segregation in education reflects the country's religious and traditional values (Ministry of Education, 2013, No. 155). The opportunity of many applications would help the Saudi target audience more easily accept M-learning applications and expand their knowledge while maintaining government policy related to religious values and gender segregation in the educational environment. In addition, students can share information through the online framework without breaking religious restrictions. This study uses a quantitative perspective to focus on defining the technical aspects and learning requirements for distributing knowledge among students within the digital environment. Additionally, the framework of the unified theory of acceptance and use of technology (UTAUT) is used to modify new constructs, called application quality requirements, that consist of quality requirements for systems, information, and interfaces.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1499-1508
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• 2018

Although fast-growing e-commerce markets gave a lot of companies opportunities to expand their customer bases, it is also the case that there are growing number of cases in which the so-called 'black consumers' cause much damage on many companies. In this study, we will implement and optimize a machine learning model that detects black consumers using customer data from e-commerce store. Using filter method for feature selection and 4 different algorithms for classification, we could get the best-performing machine learning model that detects black consumer with F-measure 0.667 and could also yield improvements in performance which are 11.44% in F-measure, 10.51% in AURC, and 22.87% in TPR.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.109-120
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• 2023

With the increasing interest in blockchain technology and its employment in diverse sectors and industries, including: finance, business, voting, industrial and many other medical and educational applications. Recently, the blockchain technology has played significant role in preventing fraud transactions in accounting systems, as the blockchain offers high security measurements, reduces the need for centralized processing, and blocks access to the organization information and system. Therefore, this paper studies, analyses, and investigates the adoption of blockchain technology with accounting systems, through analyzing the results of several research works which have employed the blockchain technology to secure their accounting systems. In addition, we investigate the performance of applying the deep learning and machine learning approaches for the purpose of fraud detection and classification. As a result of this study, the adoption of blockchain technology will enhance the safety and security of accounting systems, through identifying and classifying the possible frauds that may attack the accounting and business organizations.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.125-134
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• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.61-68
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• 2021

Recently, various technologies that use machine learning to classify malicious code have been studied. In order to enhance the effectiveness of machine learning, it is most important to extract properties to identify malicious codes and normal binaries. In this paper, we propose a feature extraction method for use in machine learning using recursive methods. The proposed method selects the final feature using recursive methods for individual features to maximize the performance of machine learning. In detail, we use the method of extracting the best performing features among individual feature at each stage, and then combining the extracted features. We extract features with the proposed method and apply them to machine learning algorithms such as Decision Tree, SVM, Random Forest, and KNN, to validate that machine learning performance improves as the steps continue.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.73-74
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• 2021

코로나로 인해 X-ray, CT, MRI와 같은 의료영상 분야에서 딥러닝을 많이 접목시키고 있다. 간단히 접할 수 있는 X-ray 영상으로 코로나 진단을 위해 CNN, R-CNN 등과 같은 영상 딥러닝 분야에서 많은 연구가 진행되고 있다. 의료영상 기반 딥러닝 학습은 바이오마커를 정확히 찾아내고, 최소한의 손실률과 높은 정확도를 필요로한다, 따라서 본 논문에서는 높은 정확도를 위한 학습 모델을 선정하고 실험을 진행하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.321-322
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• 2022

스마트폰 사용자가 늘어남에 따라 갖춰줘야 할 보안성이 취약하여, 다양한 바이러스 및 악성코드 위험에 노출되어 있다. 안드로이드는 운영체제 중 가장 많이 사용되는 운영체제로, 개방성이 높으며 수많은 악성 앱 및 바이러스가 마켓에 존재하여 위험에 쉽게 노출된다. 2년 넘게 이어진 코로나 바이러스(Covid-19)으로 인해 꾸준히 위험도가 높아진 피싱공격(Phshing attack)은 현재 최고의 스마트폰 보안 위협 Top10에 위치한다. 본 논문에서는 딥러닝 기반 자연어처리 기술을 통해 피싱 공격 대응 방법 제안 및 실험 결과를 도출하고, 또한 향후 제안 방법을 보완하여 피싱 공격 및 다양한 모바일 보안 위협에 대응할 수 있는 앱을 설계할 것이다.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.248-254
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• 2024

Artificial intelligence techniques have improved fire-detection methods; however, false alarms still occur. Conventional methods detect fires using current sensors, which can lead to detection errors due to temporary environmental changes or noise. Thus, fire-detection methods must include a trend analysis of past information. We propose a deep-learning-based fire detection method using multi-sensor data and Kendall's tau. The proposed system used a BiLSTM model to predict fires using pre-processed multi-sensor data and extracted trend information. Kendall's tau indicates the trend of a time-series data as a score; therefore, it is easy to obtain a target pattern. The experimental results showed that the proposed system with trend values recorded an accuracy of 99.93% for BiLSTM and GRU models in a 20-tap moving average filter and 40% fire threshold. Thus, the proposed trend approach is more accurate than that of conventional approaches.