• Nuclear Engineering and Technology
• /
• v.51 no.1
• /
• pp.138-145
• /
• 2019

With the application of digital technology to safety-critical infrastructures, cyber-attacks have emerged as one of the new dangerous threats. In safety-critical infrastructures such as a nuclear power plant (NPP), a cyber-attack could have serious consequences by initiating dangerous events or rendering important safety systems unavailable. Since a cyber-attack is conducted intentionally, numerous possible cases should be considered for developing a cyber security system, such as the attack paths, methods, and potential target systems. Therefore, prior to developing a risk-informed cyber security strategy, the importance of cyber-attacks and significant critical digital assets (CDAs) should be analyzed. In this work, an importance analysis method for cyber-attacks on an NPP was proposed using the probabilistic safety assessment (PSA) method. To develop an importance analysis framework for cyber-attacks, possible cyber-attacks were identified with failure modes, and a PSA model for cyber-attacks was developed. For case studies, the quantitative evaluations of cyber-attack scenarios were performed using the proposed method. By using quantitative importance of cyber-attacks and identifying significant CDAs that must be defended against cyber-attacks, it is possible to develop an efficient and reliable defense strategy against cyber-attacks on NPPs.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.272-276
• /
• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

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

• Journal of Information Technology Services
• /
• v.15 no.1
• /
• pp.67-79
• /
• 2016

This study aims to present the necessary elements that should be part of South Korea's National Defense Strategy against the recent North Korean cyber-attacks. The elements proposed in this study also reflect the recent trend of cyber-attack incidents that are happening in the Unites States and other countries and have been classified into the three levels of cyber incidents: cyberwarfare, cyberterrorism and cybercrime. As such, the elements proposed are presented in accordance with this classification system. In order to properly take into account the recent trend of cyber-attacks perpetrated by North Korea, this paper analyzed the characteristics of recent North Korean cyber-attacks as well as the countermeasures and responses of South Korea. Moreover, by making use of case studies of cyber-attack incidents by foreign nations that threaten national security, the response measures at a national level can be deduced and applied as in this study. Thus, the authors of this study hope that the newly proposed elements here within will help to strengthen the level of Korea's cyber security against foreign attacks, specifically that of North Korea such as the KHNP hacking incidents and so on. It is hoped that further damage such as leakage of confidential information, invasion of privacy and physical intimidation can be mitigated.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.159-168
• /
• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.389-395
• /
• 2022

With the evolution of technology, cyber physical systems (CPSs) are being upgraded, and new types of cyber attacks are being discovered accordingly. There are many forms of cyber attack, and all cyber attacks are made to manipulate the target systems. A representative system among cyber physical systems is a cyber physical power system (CPPS), that is, a smart grid. Smart grid is a new type of power system that provides reliable, safe, and efficient energy transmission and distribution. In this paper, specific types of cyber attacks well known as false data injection attacks targeting state estimation and energy distribution of smart grid, and protection strategies for defense of these attacks and dynamic monitoring for detection are described.

• Convergence Security Journal
• /
• v.23 no.3
• /
• pp.21-28
• /
• 2023

The development of information and communication technology in the 21st century has increased operational efficiency by providing hyper-connectivity and hyper-intelligence in the control systems of major infrastructure, but is also increasing security vulnerabilities, exposing it to hacking threats. Among them, the electric power system that supplies electric power essential for daily life has become a major target of cyber-attacks as a national critical infrastructure system. Recently, in order to protect these power systems, various security systems have been developed and the stability of the power systems has been maintained through practical cyber battle training. However, as cyber-attacks are combined with advanced ICT technologies such as artificial intelligence and big data, it is not easy to defend cyber-attacks that are becoming more intelligent with existing security systems. In order to defend against such intelligent cyber-attacks, it is necessary to know the types and aspects of intelligent cyber-attacks in advance. In this study, we analyzed the evolution of cyber attacks combined with advanced ICT technology.

• Journal of Information Processing Systems
• /
• v.15 no.4
• /
• pp.865-889
• /
• 2019

The need for cyber resilience is increasingly important in our technology-dependent society where computing devices and data have been, and will continue to be, the target of cyber-attackers, particularly advanced persistent threat (APT) and nation-state/sponsored actors. APT and nation-state/sponsored actors tend to be more sophisticated, having access to significantly more resources and time to facilitate their attacks, which in most cases are not financially driven (unlike typical cyber-criminals). For example, such threat actors often utilize a broad range of attack vectors, cyber and/or physical, and constantly evolve their attack tactics. Thus, having up-to-date and detailed information of APT's tactics, techniques, and procedures (TTPs) facilitates the design of effective defense strategies as the focus of this paper. Specifically, we posit the importance of taxonomies in categorizing cyber-attacks. Note, however, that existing information about APT attack campaigns is fragmented across practitioner, government (including intelligence/classified), and academic publications, and existing taxonomies generally have a narrow scope (e.g., to a limited number of APT campaigns). Therefore, in this paper, we leverage the Cyber Kill Chain (CKC) model to "decompose" any complex attack and identify the relevant characteristics of such attacks. We then comprehensively analyze more than 40 APT campaigns disclosed before 2018 to build our taxonomy. Such taxonomy can facilitate incident response and cyber threat hunting by aiding in understanding of the potential attacks to organizations as well as which attacks may surface. In addition, the taxonomy can allow national security and intelligence agencies and businesses to share their analysis of ongoing, sensitive APT campaigns without the need to disclose detailed information about the campaigns. It can also notify future security policies and mitigation strategy formulation.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.6
• /
• pp.311-317
• /
• 2023

This paper presents a sliding mode observer (SMO) for reconstructing cyber attacks affecting a system. The system is first re-expressed such that its design freedom is easier to manipulate. The SMO is then used to reconstruct the cyber attack affecting the system. A simulation example is used to verify the performance of the SMO under two types of cyber attacks, and its results demonstrate the effectiveness of our proposed scheme.

• International Journal of Computer Science & Network Security
• /
• v.23 no.5
• /
• pp.179-192
• /
• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.