• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1247-1258
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• 2018

The Cyber Kill Chain originally proposed by Lockheed Martin defines the standard procedure of general cyber attacks and suggests tailored defensive actions per each step, eventually neutralizing the intent of the attackers. Defenders can effectively deal with Advanced Persistent Threat(APT)s which are difficult to be handled by other defensive mechanisms under the Cyber Kill Chain. Recently, however, social engineering techniques that exploits the vulnerabilities of humans who manage the target systems are prevail rather than the technical attacks directly attacking the target systems themselves. Under the circumstance, the Cyber Kill Chain model should evolve to encompass social engineering attacks for the improved effectiveness. Therefore, this paper aims to establish a definite concept of Cyber Kill Chain for social engineering based cyber attacks, called Social Engineering Cyber Kill Chain, helping future researchers in this literature.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.939-946
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• 2013

This paper proposes normal behavior profiling methods for anomaly detection in IEC 61850 based substation network. Signature based security solutions, currently used primarily, are inadequate for APT attack using zero-day vulnerabilities. Recently, some researches about anomaly detection in control network are ongoing. However, there are no published result for IEC 61850 substation network. Our proposed methods includes 3-phase preprocessing for MMS/GOOSE packets and normal behavior profiling using one-class SVM algorithm. These approaches are beneficial to detect APT attacks on IEC 61850 substation network.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.249-252
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• 2013

The recent cyber attacks paralyzed several major banking services, broadcasters, and affected the services of a telecommunications provider. Media outlets classified the attack as cyber terror and named it an Advanced Persistant Threat. Although the attack significantly disrupted these services for at least one day, various components used in the attack were not new. Previous major cyber attacks towards targets in South Korea employed more advanced techniques thus causing greater damage. This paper studies the anatomy of the recent 2013.3.20 attack, studies the technical sophistication of the malware and attack vectors used compared with previous attacks.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.313-317
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• 2014

Smart-phone, PC or tablet platforms, such as smart terminals spread to the masses trying to capitalize. Smart TV also is increasing. In Korea, market size of TV is growing fast with growth of risk of hacking. In this paper, several kinds of Smart TV hacking cases are presented with the possibility of attacks against the vulnerability analysis and countermeasures. Most of the Linux operating system is open. Thus, it is vulnerable for latest hacking techniques. Most are based on the Linux OS to enhance security mount Sand-Box. However, bypass procedure using the technique, or APT attacks can avoid San-Box technique. New hacking techniques and a variety of ways will occur in the future. Therefore, this paper will develop Smart TV, and it analysis of a security threat and establishes better prepared in the future because new hacking attacks are expected to prepare more.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8908-8914
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• 2015

The Internet continues to grow and develop, but there are going to generate a variety of Internet attacks that exploit it. In the initial Internet environment, the attackers maliciously exploited Internet environments for ostentations and hobbies. but these days many malicious attempts purpose the financial gain so systematic and sophisticated attacks that are associated with various crimes are occurred. The structures, such as viruses and worms were present in the form of one source multi-target before. but recently, APT(Advanced Persistent Threat, intelligent continuous attacks) in the form of multi-source single target is dealing massive damage. The performance evaluation analyzed whether to generate audit data and detect integrity infringement, and false positives for normal traffic, process detecting and blocking functions, and Agent policy capabilities with respect to the application availability.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.60-69
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• 2013

Highlighted by recent security breaches including Google, Western Energy Company, and the Stuxnet infiltration of Iranian nuclear sites, Cyber warfare attacks pose a threat to national and global security. In particular, targeted attacks such as APT exploiting a high degree of stealthiness over a long period, has extended their victims from PCs and enterprise servers to government organizations and critical national infrastructure whereas the existing security measures exhibited limited capabilities in detecting and countermeasuring them. As a solution to fight against such attacks, we designed and implemented a security monitoring system, which shares security information and helps cooperative countermeasure. The proposed security monitoring system collects security event logs from heterogeneous security devices, analyses them, and visualizes the security status using 3D technology. The capability of the proposed system was evaluated and demonstrated throughly by deploying it under real network in a ISP for a week.

• 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.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1313-1322
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• 2016

Recently, many virus and hacking attacks on public organizations and financial institutions by internet are becoming increasingly intelligent and sophisticated. The Advanced Persistent Threat has been considered as an important cyber risk. This attack is basically accomplished by spreading malicious codes through complex networks. To detect and extract PE files in smart manufacturing industry networks, an efficient processing method which is performed before analysis procedure on malicious codes is proposed. We implement a preprocessor of open intrusion detection system Snort for fast extraction of PE files and install on a hardware sensor equipment. As a result of practical experiment, we verify that the network sensor can extract the PE files which are often suspected as a malware.