• ETRI Journal
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• v.43 no.2
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• pp.332-343
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• 2021

With cyberattack techniques on the rise, there have been increasing developments in the detection techniques that defend against such attacks. However, cyber attackers are now developing fileless malware to bypass existing detection techniques. To combat this trend, security vendors are publishing analysis reports to help manage and better understand fileless malware. However, only fragmentary analysis reports for specific fileless cyberattacks exist, and there have been no comprehensive analyses on the variety of fileless cyberattacks that can be encountered. In this study, we analyze 10 selected cyberattacks that have occurred over the past five years in which fileless techniques were utilized. We also propose a methodology for classification based on the attack techniques and characteristics used in fileless cyberattacks. Finally, we describe how the response time can be improved during a fileless attack using our quick and effective classification technique.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.33-38
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• 2022

The main purpose of the study is to determine the key aspects of the mechanisms of state management of the exchange of information about cyberattacks, cyber incidents, and information security incidents. The methodology includes a set of theoretical methods. Modern government, on the one hand, must take into account the emergence of such a new weapon as cyber, which can break various information systems, can be used in hybrid wars, influence political events, pose a threat to the national security of any state. As a result of the study, key elements of the mechanisms of state management of the exchange of information about cyberattacks, cyber incidents, and information security incidents were identified.

• Annual Conference of KIPS
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• 2024.10a
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• pp.324-327
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• 2024

Recently, automotive security is gerning significant attention due to the gradual surge in cyberattacks on automotive systems experienced by the industry over the past few years. These cyberattacks stem from the widened attack surface especially caused by increased connectivity and architectural complexity in modern automotive systems. Hardware Trust Anchors (HTAs), a known security technology in the cyber space, have been suggested as a candidate means to prevent automotive cyberattacks. In this paper, we analyze the effectiveness of HTAs in preventing automotive cyberattacks, and the current challenges adopting existing HTAs for automotive security. Simultaneously, we shed a light on complementary cyber defenses that may accompany HTAs to further enhance automotive security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1291-1307
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• 2021

State-sponsored cyberattacks have increased significantly and threaten national security in recent years. State-sponsored cyberattacks are often more sophisticated and destructive that attacks by individuals and private groups because of the concentration of manpower and resources. So major countries including the United States and the United Kingdom, as well as international organizations such as the EU and OECD, are recommending proportional response measures against cyberattacks. The Republic of Korea(ROK) is also trying to change its will to secure cyberattack deterrence and prepare active response through the 「National Cybersecurity Strategy 2019」. However, the ROK is not equipped with an adequate methodology to assess the severity of cyberattacks nor measures for proportional response to such attacks. In this paper, we propose a Cyber Attack Severity Assessment(CASA) methodology that can assess the scale and impact of damage to prepare external response threshold for cyberattacks at the government-level and to enable proportional responses when responding.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.857-880
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• 2016

Due to an increasing number of cyberattacks globally, cybersecurity has become a crucial part of national security in many countries. In particular, the Digital Pearl Harbor has become a real and aggressive security threat, and is considered to be a global issue that can introduce instability to the dynamics of international security. Against this context, the cyberattacks that targeted nuclear power plants (NPPs) in the Republic of Korea triggered concerns regarding the potential effects of cyber terror on critical infrastructure protection (CIP), making it a new security threat to society. Thus, in an attempt to establish measures that strengthen CIP from a cybersecurity perspective, we perform a case study on the cyber-terror attacks that targeted the Korea Hydro & Nuclear Power Co., Ltd. In order to fully appreciate the actual effects of cyber threats on critical infrastructure (CI), and to determine the challenges faced when responding to these threats, we examine factual relationships between the cyberattacks and their responses, and we perform analyses of the characteristics of the cyberattack under consideration. Moreover, we examine the significance of the event considering international norms, while applying the Tallinn Manual. Based on our analyses, we discuss implications for the cybersecurity of CI in South Korea, after which we propose a framework for strengthening cybersecurity in order to protect CI. Then, we discuss the direction of national policies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Annual Conference of KIPS
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• 2023.05a
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• pp.262-264
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• 2023

Internet of Things (IoT) networks currently employ an increased number of users and applications, raising their susceptibility to cyberattacks and data breaches, and endangering our security and privacy. Intrusion detection, which includes monitoring and analyzing incoming and outgoing traffic to detect and prohibit the hostile activity, is critical to ensure cybersecurity. Conventional intrusion detection systems (IDS) are centralized, making them susceptible to cyberattacks and other relevant privacy issues because all the data is gathered and processed inside a single entity. This research aims to create a blockchain-based architecture to support federated learning and improve cybersecurity and intrusion detection in IoT networks. In order to assess the effectiveness of the suggested approach, we have utilized well-known cybersecurity datasets along with centralized and federated machine learning models.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.1-12
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• 2021

An operation of an organization is currently using a digital environment which opens to potential cyber-attacks. These phenomena become worst as the cyberattack landscape is changing rapidly. The impact of cyber-attacks varies depending on the scope of the organization and the value of assets that need to be protected. It is difficult to assess the damage to an organization from cyberattacks due to a lack of understanding of tools, metrics, and knowledge on the type of attacks and their impacts. Hence, this paper aims to identify domains and sub-domains of cyber-attack taxonomy to facilitate the understanding of cyber-attacks. Four phases are carried in this research: identify existing cyber-attack taxonomy, determine and classify domains and sub-domains of cyber-attack, and construct the enhanced cyber-attack taxonomy. The existing cyber-attack taxonomies are analyzed, domains and sub-domains are selected based on the focus and objectives of the research, and the proposed taxonomy named AVOIDITALS Cyber-attack Taxonomy is constructed. AVOIDITALS consists of 8 domains, 105 sub-domains, 142 sub-sub-domains, and 90 other sub-sub-domains that act as a guideline to assist administrators in determining cyber-attacks through cyber-attacks pattern identification that commonly occurred on digital infrastructure and provide the best prevention method to minimize impact. This research can be further developed in line with the emergence of new types and categories of current cyberattacks and the future.