• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.155-162
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• 2022

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.108-116
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• 2019

In this paper, we propose a method to apply artificial intelligence technology efficiently to integrated security control technology. In other words, by applying machine learning learning to artificial intelligence based on big data collected in integrated security control system, cyber attacks are detected and appropriately responded. As technology develops, many large capacity Is limited to analyzing individual logs. The analysis method should also be applied to the integrated security control more quickly because it needs to correlate the logs of various heterogeneous security devices rather than one log. We have newly proposed an integrated security service model based on artificial intelligence, which analyzes and responds to these behaviors gradually evolves and matures through effective learning methods. We sought a solution to the key problems expected in the proposed model. And we developed a learning method based on normal behavior based learning model to strengthen the response ability against unidentified abnormal behavior threat. In addition, future research directions for security management that can efficiently support analysis and correspondence of security personnel through proposed security service model are suggested.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.470-472
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• 2019

Cyber Threat Intelligence (CTI)는 성장하는 사이버 공격에 대응하는 새로운 보안체계 개념으로써 최근 많은 조직에서 보안성을 향상시키기 위해 도입하고 있다. 조직에서 CTI의 도입은 보안 조직간의 위협 정보와 그에 대응할 수 있는 방어 전략을 공유하여 다양한 선제공격을 방어하기 위해 필수적이다. 이에 따라 CTI 위협 정보를 공유하는 조직이 점차 늘어나고 있으며 정보의 양은 점차 많아지고 있다. 하지만 정보를 공유받는 대부분의 조직이 공격자가 악의적으로 잘못된 위협 정보를 수집한 경우 또 다른 사이버 공격으로 이어질 수 있다. 본 논문에서는 CTI 정보 공유에서 사이버 위협 대응 조치 전략을 조직에 적용하기 전에 가상의 네트워크 아키텍처에서 적용시킨 후 평가 및 검증을 통해 공격을 목적으로 한 악의적인 정보가 적용되지 않도록 사전에 방어한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.286-289
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• 2020

정보통신 기술의 발달로 무분별한 사이버 공격에 노출되어 있기 때문에 정보보안의 기술이 중요해지고 있다. 이중 침입 탐지 시스템은 방화벽과 더불어 시스템 및 네트워크 보안을 위한 대표적인 수단으로, 현재까지 네트워크 기반인 NIDS와 호스트 기반인 HIDS에 대한 많은 연구가 이루어졌다. 이러한 침입 탐지에 대한 CTI(Cyber Threat Intelligence)를 공유하기 위해 다양한 CTI 프레임워크를 사용하여 CTI 정보를 공유하는 연구가 진행되고 있다. 이에 본 논문에서는 CVE기반의 OpenIOC와 Snort 및 OSSEC에서 생성된 Raw Data를 결합하여 새로운 CTI 프레임 워크를 제안한다. 제안된 시스템을 테스트하기 위해서는 CVE 분석을 기반으로한 Kali Linux로 공격을 진행한다, 이를 통해 생성된 데이터는 시간이 지남에 따라 축적된 데이터를 저장 및 검색을 위해 대규모 분산 처리 시스템과도 결합이 필요할 것으로 예상되며 추후 딥러닝 기술을 활용하면 지능형 지속 위협을 분석하는데 용이할 것으로 예상된다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.201-208
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• 2019

In the phono-sapiens era of the intelligence information society, most business activities are increasingly dependent on networks and information systems. SMEs, which occupy the majority of Korean companies, are increasingly possessing the value and technology of their information assets, and their ability to protect core technologies that are the driving force of corporate growth will be the most important competitiveness of enterprises. Accordingly, the Ministry of Science and ICT and the Korea Internet & Security Agency(KISA) provides a foundation for minimizing the damage from cyber threats such as hacking and information leakage by evaluating the current information protection level of SMEs and enhancing information protection capability by supporting a high level of customized information protection consulting. In this study, we analyze the effectiveness of information protection based on the results of KISA SMEs consulting. In addition, by identifying problems and limitations derived from SMEs information protection consulting results, SMEs should propose measures to improve information security of SMEs that can manage information protection management system more efficiently and effectively.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.87-96
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• 2023

As the number of security vulnerabilities increases yearly, security threats continue to occur, and the vulnerability risk is also important. We devise a security threat score calculation reflecting trends to determine the risk of security vulnerabilities. The three stages considered key elements such as attack type, supplier, vulnerability trend, and current attack methods and techniques. First, it reflects the results of checking the relevance of the attack type, supplier, and CVE. Secondly, it considers the characteristics of the topic group and CVE identified through the LDA algorithm by the Jaccard similarity technique. Third, the latest version of the MITER ATT&CK framework attack method, technology trend, and relevance between CVE are considered. We used the data within overseas sites provide reliable security information to review the usability of the proposed final formula CTRS. The scoring formula makes it possible to fast patch and respond to related information by identifying vulnerabilities with high relevance and risk only with some particular phrase.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.507-522
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• 2014

Domestic CERTs are carrying out monitoring and response against cyber attacks using security devices(e.g., IDS, TMS, etc) based on signatures. Particularly, in case of public and research institutes, about 30 security monitoring and response centers are being operated under National Cyber Security Center(NCSC) of National Intelligence Service(NIS). They are mainly using Threat Management System(TMS) for providing security monitoring and response service. Since TMS raises a large amount of security events and most of them are not related to real cyber attacks, security analyst who carries out the security monitoring and response suffers from analyzing all the TMS events and finding out real cyber attacks from them. Also, since the security monitoring and response tasks depend on security analyst's know-how, there is a fatal problem in that they tend to focus on analyzing specific security events, so that it is unable to analyze and respond unknown cyber attacks. Therefore, we propose automated verification method of security events based on their empirical analysis to improve performance of security monitoring and response.

• Convergence Security Journal
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• v.22 no.2
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• pp.9-19
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• 2022

As the expansion of digital transformation, we are more exposed to the threat of cyber attacks, and many institution or company is operating a signature-based intrusion prevention system at the forefront of the network to prevent the inflow of attacks. However, in order to provide appropriate services to the related ICT system, strict blocking rules cannot be applied, causing many false events and lowering operational efficiency. Therefore, many research projects using artificial intelligence are being performed to improve attack detection accuracy. Most researches were performed using a specific research data set which cannot be seen in real network, so it was impossible to use in the actual system. In this paper, we propose a technique for classifying major attack keywords in the security event log collected from the actual system, assigning a weight to each key keyword, and then performing a similarity check using TF-IDF to determine whether an actual attack has occurred.