• Convergence Security Journal
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• v.17 no.1
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• pp.31-38
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• 2017

As the cyber-attacks through the networks advance, it is difficult for the intrusion detection system based on the simple rules to detect the novel type of attacks such as Advanced Persistent Threat(APT) attack. At present, many types of research have been focused on the application of machine learning techniques to the intrusion detection system in order to detect previously unknown attacks. In the case of using the machine learning techniques, the performance of the intrusion detection system largely depends on the feature set which is used as an input to the system. Generally, more features increase the accuracy of the intrusion detection system whereas they cause a problem when fast responses are required owing to their large elapsed time. In this paper, we present a network intrusion detection system based on artificial neural network, which adopts a multi-objective genetic algorithm to satisfy the both requirements: accuracy, and fast response. The comparison between the proposing approach and previously proposed other approaches is conducted against NSL_KDD data set for the evaluation of the performance of the proposing approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.759-761
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• 2016

Recent changes in the information security environment have led to persistent attacks on intelligent assets such as cyber security breaches, leakage of confidential information, and global security threats. Since existing information security systems are not adequate for Advanced Persistent Threat; APT attacks, bypassing attacks, and attacks on encryption packets, therefore, continuous monitoring is required to detect and protect against such attacks. Accordingly, this paper suggests an operation plan for managing an information security system to block the attack routes of advanced persistent threats. This is achieved with identifying the valuable assets for prevention control by establishing information control policies through analyzing the vulnerability and risks to remove potential hazard, as well as constructing detection control through controlling access to servers and conducting surveillance on encrypted communication, and enabling intelligent violation of response by having corrective control through packet tagging, platform security, system backups, and recovery.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.347-363
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• 2019

Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.1-9
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• 2022

As various services are linked to IoT(Internet of Things) and portable communication terminals, cyber attacks that exploit security vulnerabilities of the devices are rapidly increasing. In particular, cyber attacks targeting heterogeneous devices in large-scale network environments through advanced persistent threat (APT) attacks are on the rise. Therefore, in order to improve the effectiveness of the response system in the event of a breach, it is necessary to apply a data enrichment mechanism for the collected artifact data to improve threat analysis and detection performance. Therefore, in this study, by analyzing the data supplementation common elements performed in the existing incident management framework for the artifacts collected for the analysis of intrusion accidents, characteristic elements applicable to the actual system were derived, and based on this, an improved accident analysis framework The prototype structure was presented and the suitability of the derived data supplementary extension elements was verified. Through this, it is expected to improve the detection performance when analyzing cyber incidents targeting artifacts collected from heterogeneous devices.

• ETRI Journal
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• v.41 no.5
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• pp.585-598
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• 2019

A cyber-physical system (CPS) is a new mechanism controlled or monitored by computer algorithms that intertwine physical and software components. Advanced persistent threats (APTs) represent stealthy, powerful, and well-funded attacks against CPSs; they integrate physical processes and have recently become an active research area. Existing offensive and defensive processes for APTs in CPSs are usually modeled by incomplete information game theory. However, honeypots, which are effective security vulnerability defense mechanisms, have not been widely adopted or modeled for defense against APT attacks in CPSs. In this study, a honeypot game-theoretical model considering both low- and high-interaction modes is used to investigate the offensive and defensive interactions, so that defensive strategies against APTs can be optimized. In this model, human analysis and honeypot allocation costs are introduced as limited resources. We prove the existence of Bayesian Nash equilibrium strategies and obtain the optimal defensive strategy under limited resources. Finally, numerical simulations demonstrate that the proposed method is effective in obtaining the optimal defensive effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2067-2072
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• 2016

Recently, due to the development of attack techniques that can circumvent existing information protection systems, continuous threats in a form unrecognized by the user have threatened information assets. Therefore, it is necessary to support the prompt responses to anticipated attempts of APT attacks, bypass access attacks, and encryption packet attacks, which the existing systems have difficulty defending against through a single response, and to continuously monitor information protection systems with a defense strategy based on Indicators of Attack (IOA). In this paper, I suggest a centralized intelligent information protection system to support the intelligent response to a violation by discerning important assets through prevention control in a performance impact assessment about information properties in order to block the attack routes of APT; establishing information control policies through weakness/risk analyses in order to remove the risks in advance; establishing detection control by restricting interior/exterior bypass networks to server access and monitoring encrypted communications; and lastly, performing related corrective control through backup/restoration.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.59-66
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• 2021

In this paper, an improved integrated security control procedure is newly proposed by applying artificial intelligence technology to integrated security control and unifying the existing security control and AI security control response procedures. Current cyber security control is highly dependent on the level of human ability. In other words, it is practically unreasonable to analyze various logs generated by people from different types of equipment and analyze and process all of the security events that are rapidly increasing. And, the signature-based security equipment that detects by matching a string and a pattern has insufficient functions to accurately detect advanced and advanced cyberattacks such as APT (Advanced Persistent Threat). As one way to solve these pending problems, the artificial intelligence technology of supervised and unsupervised learning is applied to the detection and analysis of cyber attacks, and through this, the analysis of logs and events that occur innumerable times is automated and intelligent through this. The level of response has been raised in the overall aspect by making it possible to predict and block the continuous occurrence of cyberattacks. And after applying AI security control technology, an improved integrated security control service model was newly proposed by integrating and solving the problem of overlapping detection of AI and SIEM into a unified breach response process(procedure).

• Electronics and Telecommunications Trends
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• v.28 no.3
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• pp.19-29
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• 2013

최근 외부 해킹으로 대량의 개인정보 유출, 대규모 시스템 장애 등 사고가 빈번히 발생하고 있다. 특히, 보안체계를 잘 갖추고 있던 조직들도 APT(Advanced Persistent Threat) 공격과 같이 지속적으로 특정 표적을 목표로 하는 공격 앞에 무력하게 당하는 사건들을 접하면서 많은 기업 및 조직들이 대응 방안 마련에 고심하고 있다. 본고에서는 사이버테러, 사이버전(戰), 핵티비즘 등의 공격방법으로 활용되고 있는 사이버 표적공격 위협에 대한 방어 기술로서 최근 관심을 받고 있는 빅데이터 처리 기술을 기반으로 다중소스 데이터 수집 분석을 통한 지능형 보안 기술에 대한 개념과 관련 기술 및 제품의 동향에 대하여 살펴본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.770-773
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• 2012

특정 기업 및 국가를 대상으로 하는 APT(Advanced Persistent Threat)공격의 경우 특정 시스템을 겨냥하여 제작되기 때문에 기존의 시그니처 기반의 악성코드 탐지 방식으로는 해당 악성코드를 탐지할 수 없다. 따라서 알려지지 않은 악성코드를 탐지할 수 있는 행위 기반의 악성코드 탐지 방식이 최근 이슈화되었다. 본 논문에서는 연구되고 있는 행위 분석 기반의 악성코드 탐지 방식들을 분석함으로써 향후 행위 기반 악성코드 탐지 기술 개발 및 연구에 기여하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.277-279
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• 2014

In case of APT attacks, the update server is being used as a means of dissemination, the update program is running malicious code or data in applications such as anti-virus signature is vulnerable to manipulation, SW Update threat identification and prevention measures are urgently required. This paper presents a natiional and international SW update structure, update process exploits and response measures to examine, Through the extraction/analysis of a domestic famous SW update log, we are willing to select the necessary component of the normal program update to identify a white list.