• Annual Conference of KIPS
• /
• 2014.04a
• /
• pp.475-478
• /
• 2014

최근 인터넷이 발전함에 따라 월드와이드웹(World Wide Web) 기반의 웹 서비스가 급격한 발전을 이루었다. 또한 이 웹 서비스를 바탕으로 다양한 컨텐츠들과 이를 이용하는 사용자의 수도 함께 증가하였다. 그러나 이와 같은 웹 서비스의 보편화가 증대될수록 이를 악용하려는 사이버 범죄 또한 비례하여 증가하고 있다. 최근에는 공격자들이 스마트폰을 대상으로 악성코드를 전파하기 위한 방법으로 웹 서비스를 활용하기 시작하면서 웹 서비스의 보안에 대한 중요성이 더욱 강조되고 있다. 이러한 웹 서비스 보안의 필요성을 인지하고, 많은 사람들이 무료로 쉽게 웹 서비스 보안취약점을 진단 할 수 있도록 여러 오픈소스 기반의 보안 취약점 진단도구가 연구, 개발되고 있다. 하지만 웹 서비스의 보안약점을 진단하는 도구의 적합성 평가 및 기능 분류가 명확하지 않아서 진단도구를 선택하고 활용함에 있어 어려움이 따른다. 본 논문에서는 OWASP에서 위험도에 따라 선정한 웹 서비스의 보안 취약점 Top 10 항목과 소프트웨어 보안약점 진단가이드 등을 통해 웹 서비스 보안 취약점을 진단하는 도구에 대한 분석 기준을 제시한다. 이후 오픈소스로 공개된 테스트 기반 취약점 탐지도구와 소스 기반 취약점 진단도구들에 대해 제시한 기준을 이용하여 분석한다. 본 논문의 분석결과로 웹 서비스의 안전성을 평가하기 위해 활용할 수 있는 진단 도구에 대한 분석정보를 제공함으로써 보다 안전한 웹 서비스의 개발과 운영에 기여할 것으로 기대한다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.4
• /
• pp.795-805
• /
• 2019

Recently, there is a growing interest in network anomaly detection technology to tackle unknown attacks. For this purpose, diverse studies using data mining, machine learning, and deep learning have been applied to detect network anomalies. In this paper, we evaluate the decision tree to see its feasibility for network anomaly detection on NSL-KDD data set, which is one of the most popular data mining techniques for classification. In order to handle the over-fitting problem of decision tree, we select 13 features from the original 41 features of the data set using chi-square test, and then model the decision tree using TensorFlow and Scik-Learn, yielding 84% and 70% of binary classification accuracies on the KDDTest+ and KDDTest-21 of NSL-KDD test data set. This result shows 3% and 6% improvements compared to the previous 81% and 64% of binary classification accuracies by decision tree technologies, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.4
• /
• pp.775-784
• /
• 2019

Malware infringement attacks are continuously increasing in various environments such as mobile, IOT, windows and mac due to the emergence of new and variant malware, and signature-based countermeasures have limitations in detection of malware. In addition, analytical performance is deteriorating due to obfuscation, packing, and anti-VM technique. In this paper, we propose a system that can detect malware based on machine learning by using similarity hashing-based pattern detection technique and static analysis after file classification according to packing. This enables more efficient detection because it utilizes both pattern-based detection, which is well-known malware detection, and machine learning-based detection technology, which is advantageous for detecting new and variant malware. The results of this study were obtained by detecting accuracy of 95.79% or more for benign sample files and malware sample files provided by the AI-based malware detection track of the Information Security R&D Data Challenge 2018 competition. In the future, it is expected that it will be possible to build a system that improves detection performance by applying a feature vector and a detection method to the characteristics of a packed file.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.2
• /
• pp.65-72
• /
• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.607-614
• /
• 2020

The purpose of this study is to analyze the penalty types and characteristics of world judo players. Using a total of 19,889 penalties in 9,737 matches at the World Judo Grand Prix, the team draws a difference in the types and time of fouls according to gender, weight class and time. Results Both foul play and warning showed gender differences, and both men and women affected warning rates in weight class and time classification. In addition, fouls caused by non-combat behavior and camouflage attacks have a forward direction of time, and a low time of fouling by heavyweight athletes. Judo athletes and leaders expect to be used as basic data based on scientific data, such as training, skills, tactics and motivation, taking into account the characteristics of the foul type. Requires follow-up studies of scoring relationships and winning factors.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.6
• /
• pp.1141-1149
• /
• 2020

Most of the industrial control network is an independent closed network, which is operated for a long time after installation, and thus the OS is not updated, so security threats increase and security vulnerabilities exist. The zero-day attack defense must be applied with the latest patch, but in a large-scale industrial network, it requires a higher level of real-time and non-disruptive operation due to the direct handling of physical devices, so a step-by-step approach is required to apply it to a live system. In order to solve this problem, utility-specific patch impact assessment is required for reliable patch application. In this paper, we propose a method to test and safely install the patch using the regression analysis technique and show the proven results. As a patch impact evaluation methodology, the maximum allowance for determining the safety of a patch was derived by classifying test types based on system-specific functions, performance, and behavior before and after applying the patch. Finally, we report the results of case studies applied directly to industrial control networks, the OS patch has been updated while ensuring 99.99% availability.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.3
• /
• pp.501-511
• /
• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1880-1889
• /
• 2022

The online portal platform provides online news with online comments, but the anonymity of comments causes incivility, and online comments are considered social problems. While there are many foreign language-based incivility detection studies, in-depth research is not being conducted in Korea since there has not been implemented Korean language dataset which is labeled detailed criteria of incivility. In this study, the incivility notation of comments was conducted in a total of 13 items, uncivil words were summarized. Furthermore, Attention algorithm was applied to each comment and summary to extract embedding vectors. 2-d CNN followed at the end to detect incivility in given data. As a result, we showed that the proposed algorithm is useful for anti-citizen detection such as name-calling and offensive tones. This study is expected to contribute to the formation of a healthy online comment culture by detecting uncivil comments which hinder democratic discourse.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.411-425
• /
• 2023

Today, AI (Artificial Intelligence) technology is widely used in various fields, performing classification and regression tasks according to the purpose of use, and research is also actively progressing. Especially in the field of security, unexpected threats need to be detected, and unsupervised learning-based anomaly detection techniques that can detect threats without adding known threat information to the model training process are promising methods. However, most of the preceding studies that provide interpretability for AI judgments are designed for supervised learning, so it is difficult to apply them to unsupervised learning models with fundamentally different learning methods. In addition, previously researched vision-centered AI mechanism interpretation studies are not suitable for application to the security field that is not expressed in images. Therefore, In this paper, we use a technique that provides interpretability for detected anomalies by searching for and comparing optimization references, which are the source of intrusion attacks. In this paper, based on reference, we propose additional logic to search for data closest to real data. Based on real data, it aims to provide a more intuitive interpretation of anomalies and to promote effective use of an anomaly detection model in the security field.

• Annual Conference of KIPS
• /
• 2021.11a
• /
• pp.659-662
• /
• 2021

국내에서는 매년 많은 수의 자격시험이 치러지고 있다. 현재 대부분의 시험장에서 응시자 본인 확인 절차는 감독관이 응시자의 얼굴과 신분증 사진을 비교하는 방식으로 이루어진다. 하지만 이 방식은 사람에 따라 오차가 클 수 있으며, 사진과 눈에 띄는 차이가 없으면 동일인물로 판단하기 쉽다. 최근까지도 대리응시 이슈가 발생하고 있어 근절을 위한 보다 강력한 조치가 필요하다. 본 논문에서는 지문과 오토인코더, SGAN을 이용하여 대리응시방지를 강화할 수 있는 본인 확인 시스템을 제안한다. 이때 응시자의 지문정보가 그대로 인증 서버에 저장되면 응시자의 생체정보가 노출될 수 있다는 문제점이 존재한다. 따라서 오토인코더를 통해 지문의 특징점만 추출하여 인증용 이미지를 생성하고 이 이미지를 서버에 저장하여 학습시키도록 한다. 적은 학습데이터 환경에서 분류기로써 좋은 성능을 갖는 SGAN을 통해 인증 이미지와 응시자가 동일인물인지 확인할 수 있다. 서버가 공격을 받더라도 응시자의 지문데이터가 그대로 노출되지 않게 되어 보안 취약점을 극복할 수 있다.