• Convergence Security Journal
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• v.21 no.3
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• pp.13-23
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• 2021

The attacker's techniques and tools are becoming intelligent and sophisticated. Existing Anti-Virus cannot prevent security accident. So the security threats on the endpoint should also be considered. Recently, EDR security solutions to protect endpoints have emerged, but they focus on visibility. There is still a lack of detection and responsiveness. In this paper, we use real-world EDR event logs to aggregate knowledge-based MITRE ATT&CK and autoencoder-based anomaly detection techniques to detect anomalies in order to screen effective analysis and analysis targets from a security manager perspective. After that, detected anomaly attack signs show the security manager an alarm along with log information and can be connected to legacy systems. The experiment detected EDR event logs for 5 days, and verified them with hybrid analysis search. Therefore, it is expected to produce results on when, which IPs and processes is suspected based on the EDR event log and create a secure endpoint environment through measures on the suspicious IP/Process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.527-545
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• 2022

In order to effectively detect APT attacks performed by well-organized adversaries, we implemented a system to detect attacks by reconstructing attack chains of APT attacks. Our attack chain-based APT attack detection system consists of 'events collection and indexing' part which collects various events generated from hosts and network monitoring tools, 'unit attack detection' part which detects unit-level attacks defined in MITRE ATT&CK^® techniques, and 'attack chain reconstruction' part which reconstructs attack chains by performing causality analysis based on provenance graphs. To evaluate our system, we implemented a test-bed and conducted several simulated attack scenarios provided by MITRE ATT&CK Evaluation program. As a result of the experiment, we were able to confirm that our system effectively reconstructed the attack chains for the simulated attack scenarios. Using the system implemented in this study, rather than to understand attacks as fragmentary parts, it will be possible to understand and respond to attacks from the perspective of progress of attacks.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.15-26
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• 2022

Various subjects are carrying out cyberattacks using a variety of tactics and techniques. Additionally, cyberattacks for political and economic purposes are also being carried out by groups which is sponsored by its nation. To deal with cyberattacks, researchers used to classify the malware family and the subjects of the attack based on malware signature. Unfortunately, attackers can easily masquerade as other group. Also, as the attack varies with subject, techniques, and purpose, it is more effective for defenders to identify the attacker's purpose and goal to respond appropriately. The essential goal of cyberattacks is to threaten the information security of the target assets. Information security is achieved by preserving the confidentiality, integrity, and availability of the assets. In this paper, we relabel the attacker's goal based on MITRE ATT&CK® in the point of CIA triad as well as classifying cyber security reports to verify the labeling method. Experimental results show that the model classified the proposed CIA label with at most 80% probability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.673-689
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• 2022

We propose an APT attack scoring method as a part of the process for detecting and responding to APT attacks. First, unlike previous work that considered inconsistent and subjective factors determined by cyber security experts in the process of scoring cyber attacks, we identify quantifiable factors from components of MITRE ATT&CK^Ⓡ techniques and propose a method of quantifying each identified factor. Then, we propose a method of calculating the score of the unit attack technique from the quantified factors, and the score of the entire APT attack composed of one or more multiple attack techniques. We present the possibility of quantification to determine the threat level and urgency of cyber attacks by applying the proposed scoring method to the APT attack reports, which contains the hundreds of APT attack cases occurred worldwide. Using our work, it will be possible to determine whether actual cyber attacks have occurred in the process of detecting APT attacks, and respond to more urgent and important cyber attacks by estimating the priority of APT attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.53-60
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• 2024

Security assessment is an indispensable process for a secure network, and appropriate performance indicators must be present to manage risks. The most widely used quantitative indicator is CVSS. CVSS has a problem that it cannot consider context in terms of subjectivity, complexity of interpretation, and security risks. To compensate for these problems, we propose indicators that itemize and quantify four things: attackers, threats, responses, and assets, taking into account the security context of ISO/IEC 15408 documents. Vulnerabilities discovered through network scanning can be mapped to MITREATT&CK's technology by the connection between weaknesses and attack patterns (CAPEC). We use MITREATT&CK's Groups, Tactic, and Mitigations to produce consistent and intuitive scores. Accordingly, it is expected that security evaluation managers will have a positive impact on strengthening security such as corporate networks by expanding the range of choices among security indicators from various perspectives.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.797-805
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• 2019

Threats targeting cyberspace are becoming more intelligent and increasing day by day. To cope with such cyber threats, it is essential to improve the coping ability of system security officers. In this paper, we propose a simulated threat generator that automatically generates cyber threats for cyber defense training. The proposed Simulated Threat Generator is designed with MITRE ATT & CK(Adversarial Tactics, Techniques and Common Knowledge) framework to easily add an evolving cyber threat and select the next threat based on the threat execution result.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.15-24
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• 2023

Digital therapeutics (DTx) are utilized to replace or supplement drug therapy to treat patients. DTx are developed as a mobile application for portability and convenience. The government requires security verification to be performed on digital medical devices that manage sensitive information during the transmission and storage of patient data. Although safety verification is included in the approval process for DTx, the cybersecurity checklist used as a reference does not reflect the characteristics of mobile applications. This poses the risk of potentially overlooking vulnerabilities during security verification. This study aims to address this issue by comparing and analyzing existing items based on the mobile tactics, techniques, and procedures of MITRE ATT&CK, which manages globally known and occurring vulnerabilities through regular updates. We identify 16 items that require improvement and expand the checklist to 29 items to propose improvement measures. The findings of this study may contribute to the safe development and advancement of DTx for managing sensitive patient information.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.31-37
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• 2020

In this paper, we propose a system architecture and methodology based on cyber kill chain and MITRE ATT&CK for experiment analysis on cyber warfare simulation. Threat analysis is possible by applying various attacks that have actually occurred with continuous updates to reflect newly emerging attacks. In terms of cyber attack and defense, the current system(AS-IS) and the new system(TO-BE) are analyzed for effectiveness and quantitative results are presented. It can be used to establish proactive cyber COA(Course of Action) strategy, and also for strategic decision making. Through a case study, we presented the usability of the system architecture and methodology proposed in this paper. The proposed method will contribute to strengthening cyber warfare capabilities by increasing the level of technology for cyber warfare experiments.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.1-13
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• 2022

As the information and communication environment develops, the environment of military facilities is also development remarkably. In proportion to this, cyber threats are also increasing, and in particular, APT attacks, which are difficult to prevent with existing signature-based cyber defense systems, are frequently targeting military and national infrastructure. It is important to identify attack groups for appropriate response, but it is very difficult to identify them due to the nature of cyber attacks conducted in secret using methods such as anti-forensics. In the past, after an attack was detected, a security expert had to perform high-level analysis for a long time based on the large amount of evidence collected to get a clue about the attack group. To solve this problem, in this paper, we proposed an automation technique that can classify an attack group within a short time after detection. In case of APT attacks, compared to general cyber attacks, the number of attacks is small, there is not much known data, and it is designed to bypass signature-based cyber defense techniques. As an attack model, we used MITRE ATT&CK® which modeled many parts of cyber attacks. We design an impact score considering the versatility of the attack techniques and proposed a group similarity score based on this. Experimental results show that the proposed method classified the attack group with a 72.62% probability based on Top-5 accuracy.