• 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 the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1021-1031
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• 2023

State-sponsored cyber attacks are highly impactful because they are carried out to achieve pre-planned goals. As a defender, it is difficult to respond to them because of the large scale of the attack and the possibility that unknown vulnerabilities may be exploited. In addition, overreacting can reduce the availability of users and cause business disruption. Therefore, there is a need for a response policy that can effectively defend against attacks while ensuring user availability. To solve this problem, this paper proposes a method to collect the number of processes and sessions of defense assets in real time and use them for learning. Using this method to learn reinforcement learning-based policies on a cyber attack simulator, the attack duration based on 100 time-steps was reduced by 27.9 time-steps and 3.1 time-steps for two attacker models, respectively, and the number of "restore" actions that impede user availability during the defense process was also reduced, resulting in an overall better policy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.333-336
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• 2022

With the development of IT technology, cybercrime is becoming sophisticated and intelligent. In particular, in the case of BackDoor, which is used in the APT attack (intelligent continuous attack), it is very important to detect malicious behavior and respond to infringement because it is often unaware that it has been damaged by an attacker. This paper aims to build an EDR platform that can monitor, analyze, and respond to malicious behavior in real time by collecting, storing, analyzing, and visualizing logs in an endpoint environment in real time using open source-based analysis solutions ELK Stack and Sysmon.

• Journal of Platform Technology
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• v.9 no.3
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• pp.3-17
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• 2021

Advanced persistent threat (APT) attacks are attacks aimed at a particular entity as a set of latent and persistent computer hacking processes. These APT attacks are usually carried out through various methods, including spam mail and disguised banner advertising. The same name is also used for files, since most of them are distributed via spam mail disguised as invoices, shipment documents, and purchase orders. In addition, such Infostealer attacks were the most frequently discovered malicious code in the first week of February 2021. CDR is a 'Content Disarm & Reconstruction' technology that can prevent the risk of malware infection by removing potential security threats from files and recombining them into safe files. Gartner, a global IT advisory organization, recommends CDR as a solution to attacks in the form of attachments. There is a program using CDR techniques released as open source is called 'Dangerzone'. The program supports the extension of most document files, but does not support the extension of HWP files that are widely used in Korea. In addition, Gmail blocks malicious URLs first, but it does not block malicious URLs in mail systems such as Naver and Daum, so malicious URLs can be easily distributed. Based on this problem, we developed a 'Dangerzone' program that supports the HWP extension to prevent APT attacks, and a Chrome extension that performs URL checking in Naver and Daum mail and blocking banner ads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• Journal of the Korea Convergence Society
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• v.5 no.3
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• pp.1-5
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• 2014

The development of communication method for a variety of intelligent automobiles are delivering comfortable and safe information. However the development of such communication method must also think about security. Even the update of navigation to be used for intelligent automobiles uses the wireless updating methods but the updating methods currently being used has no reliable security measures. The navigation communications used in the intelligent automobiles are being provided with TTI(Traffic and Travel Information) service using a variety of methods by the countries. In the case of Korea, most are based on T-DMB using the TPEG method for transmitting the information. By identifying the characteristics on the navigation wireless update, a security solution is proposed for delivering the reliable update information after creating the attack scenario.

• 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.22 no.5
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• pp.1103-1113
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• 2012

DLP technology has been effectively enforcing privacy protection policy in on-line computing environment. However, with wide spread use of smart devices and promotion of smart-works, it has been pointed out that DLP technology cannot effectively prevent privacy leakage at smart devices and is comparatively weak at APT attack. In this paper, we suggests a hybrid approach, PPS, which integrates E-DRM system with DLP technology, taking advantages of both technologies. The technology basically uses encryption function and access control of E-DRM system, and thus it can effectively prevent leakage of privacy information of customers, even if the documents are in the hands of malicious third parties.

• Convergence Security Journal
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• v.22 no.3
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• pp.87-99
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• 2022

Cyber attacks have become more difficult to detect and track as sophisticated and advanced APT attacks increase. System providence graphs provide analysts of cyber security with techniques to determine the origin of attacks. Various system provenance graph techniques have been studied to reveal the origin of penetration against cyber attacks. In this study, we investigated various system provenance graph techniques and described about data collection and analysis techniques. In addition, based on the results of our survey, we presented some future research directions.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.1-8
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• 2020

Recently, the number of APT(Advanced Persistent Threats) attack using malware has been increasing, and research is underway to prevent and detect them. While it is important to detect and block attacks before they occur, it is also important to make an effective response through an accurate analysis for attack case and attack type, these respond which can be determined by analyzing the attack group of such attacks. Therefore, this paper propose a framework based on genetic algorithm for analyzing malware and understanding attacker group's features. The framework uses decompiler and disassembler to extract related code in collected malware, and analyzes information related to author through code analysis. Malware has unique characteristics that only it has, which can be said to be features that can identify the author or attacker groups of that malware. So, we select specific features only having attack group among the various features extracted from binary and source code through the authorship clustering method, and apply genetic algorithm to accurate clustering to infer specific features. Also, we find features which based on characteristics each group of malware authors has that can express each group, and create profiles to verify that the group of authors is correctly clustered. In this paper, we do experiment about author classification using genetic algorithm and finding specific features to express author characteristic. In experiment result, we identified an author classification accuracy of 86% and selected features to be used for authorship analysis among the information extracted through genetic algorithm.