• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.153-169
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• 2018

Previous e-financial anomalies analysis and detection technology collects large amounts of electronic financial transaction logs generated from electronic financial business systems into big-data-based storage space. And it detects abnormal transactions in real time using detection rules that analyze transaction pattern profiling of existing customers and various accident transactions. However, deep analysis such as attempts to access e-finance by insiders of financial institutions with large scale of damages and social ripple effects and stealing important information from e-financial users through bypass of internal control environments is not conducted. This paper analyzes the management status of e-financial security programs of financial companies and draws the possibility that they are allies in security control of insiders who exploit vulnerability in management. In order to efficiently respond to this problem, it will present a comprehensive e-financial security management environment linked to insider threat monitoring as well as the existing e-financial transaction detection system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 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 Security & Cryptology
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• v.34 no.4
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• pp.615-627
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• 2024

With the evolution of power systems and advancements in IT technology, there is an increasing demand to shift from serial-based communication to TCP/IP-based communication. However, TCP/IP communication entails various security threats, necessitating extensive consideration from an information security perspective. Security measures such as authentication and encryption cannot be rapidly implemented due to issues like the replacement of Remote Terminal Units (RTUs) and the performance requirements of encryption algorithms. This paper proposes a state estimation-based intrusion detection model to identify and effectively detect threats to power control systems in such a context. The proposed model, in addition to signature detection methods, verifies the validity of acquired data, enabling it to detect attacks that are difficult to identify using traditional methods, such as data tampering.

• The Journal of Society for e-Business Studies
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• v.17 no.3
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• pp.117-128
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• 2012

There was a large scale of DDoS(Distributed Denial of Service) attacks mostly targeted at Korean government web sites and cooperations's on March 4, 2010(3.4 DDoS attack) after 7.7 DDoS on July 7, 2009. In these days, anyone can create zombie PCs to attack someone's website with malware development toolkits and farther more improve their knowledge of hacking skills as well as toolkits because it has become easier to obtain these toolkits on line, For that trend, it has been difficult for computer security specialists to counteract DDoS attacks. In this paper, we will introduce an essential control list to prevent malware infection with live forensics techniques after analysis of monitoring network systems and PCs. Hopefully our suggestion of how to coordinate a security monitoring system in this paper will give a good guideline for cooperations who try to build their new systems or to secure their existing systems.

• Convergence Security Journal
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• v.14 no.4
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• pp.27-32
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• 2014

Utilization of web application has been widely spread and complication in recent years by the rapid development of network technologies and changes in the computing environment. The attack being target of this is increasing and the means is diverse and intelligent while these web applications are using to a lot of important services. In this paper, we proposed security model using virtualization technology to prevent attacks using vulnerabilities of web application. The request information for query in a database server also can be recognized by conveying to the virtual web server after ID is given to created session by the client request and the type of the query is analyzed in this request. VM-Master module is constructed in order to monitor traffic between the virtual web servers and prevent the waste of resources of Host OS. The performance of attack detection and resource utilization of the proposed method is experimentally confirmed.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.7-10
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• 2007

최근 정보화 수준이 고도화 되고 대외 기술 교류가 활발해짐에 따라 기업 정보 유출에 의한 피해 사례가 급증하고 있고, 자료 유출 사례 중 전 현직 종사원인 내부자에 의해 발생되는 건이 80%이상을 차지하고 있어 내부정보 유출 방지체계에 대한 구축이 절실히 요구되고 있다. 내부 정보 유출 방지체계는 침입탐지시스템이나 방화벽 같은 외부 공격자에 대한 방어 대책으로는 한계가 있어 새로운 정보보호 체계가 필요하다. 본 논문은 내부정보 유통 구조에 내재되어 있는 내부정보 유출 취약점을 분석하고 이에 대한 대책으로서 정보보호 모델을 제안하며, 제안된 정보보호 모델을 구현하는 한 방법으로서 DRM 기술을 적용한 정보보호 기술구조를 제안하고 구현 시 고려사항을 기술한다. 제안된 기술구조는 조직에서 운용하고 있는 정보체계와 정보기기들을 관리영역으로 식별하는 방법을 제공하며 관리영역에서 비 관리영역으로의 자료 유통을 근본적으로 통제하는 장점을 갖고 있다.

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

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.837-840
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• 2017

각종 사이버 범죄가 증가함에 따라 실시간 모니터링을 통한 사전 탐지 기술뿐만 아니라, 사후 원인 분석을 통한 사고 재발 방지 기술의 중요성이 증가하고 있다. 사후 분석은 시스템에서 생산된 다양한 유형의 대용량 로그를 기반으로 분석가가 보안 위협 과정을 규명하는 것으로 이를 지원하는 다양한 상용 및 오픈 소스 SW 존재하나, 대부분 단일 분석가 PC에서 운용되는 파일 기반 SW로 대용량 데이터에 대한 분석 성능 저하, 다수 분석가 간의 데이터 공유 불가, 통계 연관 분석 한계 및 대화형 점진적 내용 분석 불가 등의 문제점을 해결하지 못하고 있다. 이러한 문제점을 해결하기 위하여 고성능 인메모리 관계형 데이터베이스 시스템을 로그 스토리지로 활용하는 대용량 로그 분석 SW 개발하였다. 특히, 기 확보된 공격자 프로파일을 활용하여 공격의 유무를 확인하는 텍스트 패턴 매칭 연산은 전통적인 관계형 데이터베이스 시스템의 FTS(Full-Text Search) 기능 활용이 가능하나, 대용량 전용 색인 생성에 따른 비현실적인 DB 구축 소요 시간과 최소 3배 이상의 DB 용량 증가로 인한 시스템 리소스 추가 요구 등의 단점이 있다. 본 논문에서는 인메모리 관계형 데이터베이스 시스템 기반 효율적인 텍스트 패턴 매칭 연산을 위하여, 고성능의 대용량 로그 DB 적재 방법과 새로운 유형의 패턴 매칭 방법을 제안하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.883-885
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• 2002

오늘날 인터넷 기술이 급격한 성장과, 인터넷을 통한 조직과 개인의 사회적 활동의 증가에 따라 인터넷에 대한 우리의 생활 의존도가 점차로 커지고 있으며 이에 따른 역작용으로 침해사고 및 정보의 유출, 파괴, 서비스방해, 위조, 변조 등의 컴퓨터범죄가 날로 증가하여 심가한 사회문제로 대두되고 있다. 사이버 공간에서 전산망을 보호하기 위해서 사용자 인증, 무결성 점검, 침입탐지, 파이어 월 등 다양한 기술이 사용되고 있다, 하지만 가장 우선시 되어야 하는 것은 공격의 목표가 되는 시스템의 보안 취약점을 찾아내고 이를 제거하는 작업이라고 할 수 있다, 이 논문에서는 대학 전산망의 확대와 해킹기술은 급속히 발달하고 있지만 이에 비해 대학 전산망 보호를 위한 보안장비 및 관리자/사용자들의 보안 지식 및 기술은 절대적으로 부족한 실정을 감안해서, 내부 사용자들이 자신의 취약점을 쉽게 점검 및 해결할 수 있고, 관리자들이 전산망의 취약점을 파악하는데 효과적인 시스템을 제안한다.