• ETRI Journal
• /
• v.41 no.5
• /
• pp.585-598
• /
• 2019

A cyber-physical system (CPS) is a new mechanism controlled or monitored by computer algorithms that intertwine physical and software components. Advanced persistent threats (APTs) represent stealthy, powerful, and well-funded attacks against CPSs; they integrate physical processes and have recently become an active research area. Existing offensive and defensive processes for APTs in CPSs are usually modeled by incomplete information game theory. However, honeypots, which are effective security vulnerability defense mechanisms, have not been widely adopted or modeled for defense against APT attacks in CPSs. In this study, a honeypot game-theoretical model considering both low- and high-interaction modes is used to investigate the offensive and defensive interactions, so that defensive strategies against APTs can be optimized. In this model, human analysis and honeypot allocation costs are introduced as limited resources. We prove the existence of Bayesian Nash equilibrium strategies and obtain the optimal defensive strategy under limited resources. Finally, numerical simulations demonstrate that the proposed method is effective in obtaining the optimal defensive effect.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.237-245
• /
• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• International Journal of Computer Science & Network Security
• /
• v.24 no.5
• /
• pp.40-52
• /
• 2024

Cyber-Physical Systems (CPS) are introduced as complex, interconnected systems that combine physical components with computational elements and networking capabilities. They bridge the gap between the physical world and the digital world, enabling the monitoring and control of physical processes through embedded computing systems and networked communication. These systems introduce several security challenges. These challenges, if not addressed, can lead to vulnerabilities that may result in substantial losses. Therefore, it is crucial to thoroughly examine and address the security concerns associated with CPS to guarantee the safe and reliable operation of these systems. To handle these security concerns, different existing security requirements methods are considered but they were unable to produce required results because they were originally developed for software systems not for CPS and they are obsolete methods for CPS. In this paper, a Security Requirements Engineering Methodology for CPS (CPS-SREM) is proposed. A comparison of state-of-the-art methods (UMLSec, CLASP, SQUARE, SREP) and the proposed method is done and it has demonstrated that the proposed method performs better than existing SRE methods and enabling experts to uncover a broader spectrum of security requirements specific to CPS. Conclusion: The proposed method is also validated using a case study of the healthcare system and the results are promising. The proposed model will provide substantial advantages to both practitioners and researcher, assisting them in identifying the security requirements for CPS in Industry 4.0.

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

• Convergence Security Journal
• /
• v.15 no.6_2
• /
• pp.29-36
• /
• 2015

The hacking threats made against the Korea Hydro & Nuclear Power(KNDP) and the leakage of critical information on nuclear power safety raised the public awareness on the importance of protecting and managing national infrastructure necessary for sustaining the state and society. Cyber security activities and relevant institutions in the ROK, however, are still insufficient, because of which there is a possibility that similar incidents would reoccur and cause serious damages. Hence, a grave and direct threat is posed to the national security of the ROK. In this thesis, I would like to give my analysis and assessment on the recent cyber intrusions against infrastructure at home and abroad, measures established in response and their implementation, and the deficiency of the existing infrastructure protection system ; and lastly propose measures to reinforce infrastructure protection of the ROK.

• Convergence Security Journal
• /
• v.13 no.4
• /
• pp.77-83
• /
• 2013

As the network composed of numerous sensor nodes, sensor network conducts the function of sensing the surrounding information by sensor and of the sensed information. The concept of the battlefield is also changing to one that includes not only physical spaces but all areas including the networks of the nation's key industries and military facilities, energy facilities, transportation, and communication networks. In light of the changing warfare in terms of how it is conducted and what form it takes, the Korea military has to seek ways to effectively respond to threats of cyber warfare. In the past, although partial strategies on cyber warfare were studied, no research was done through the overall system flow. In this paper, key variables related to cyber warfare security are classified into personnel, management, and technology. A simple model and an extended model are suggested for each area, and based on the technology area of the extended model, formal methods are used to verify the validity and a detailed response strategy is suggested according to the identified leverage.

• Journal of Internet Computing and Services
• /
• v.16 no.5
• /
• pp.1-9
• /
• 2015

As attacks in cyber space become advanced and complex, monotonous defense approach of one-one matching manner between attack and defense may be limited to defend them. More efficient defense method is required. This paper proposes multi layers security scheme that can support to defend assets against diverse cyber attacks in systematical and adaptive. We model multi layers security scheme based on Defense Zone including several defense layers and also discuss essential technical elements necessary to realize multi layers security scheme such as cyber threats analysis and automated assignment of defense techniques. Also effects of multi layers security scheme and its applicability are explained. In future, for embodiment of multi layers security scheme, researches about detailed architecture design for Defense Zone, automated method to select the best defense technique against attack and modeling normal state of asset for attack detection are needed.

• Convergence Security Journal
• /
• v.21 no.1
• /
• pp.45-53
• /
• 2021

When the security monitoring system is performed in a separation network, there is little normal anomaly detection in internal networks or high-risk sections. Therefore, after the establishment of the security network, a model is needed to evaluate state-of-the-art cyber threat anomalies for internal network in separation network to complete the optimized security structure. In this study, We evaluate it by generating datasets of cyber vulnerabilities and malicious code arising from general and separation networks, It prepare for the latest cyber vulnerabilities in internal network cyber attacks to analyze threats, and established a cyber security test evaluation system that fits the characteristics. The study designed an evaluation model that can be applied to actual separation network institutions, and constructed a test data set for each situation and applied a real-time security assessment model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.3
• /
• pp.645-656
• /
• 2019

As the number of cyber threats to control systems increases, the need for control system cyber security is also increasing. Currently, various cyber security related education and control system education are being conducted. However, it does not fully reflect the characteristics of the control system and the characteristics of the participants. In this paper, we propose a training system and technique to enhance the control system cyber security capability. To this end, we analyze the limitations of existing security education. Based on the results, we develop a control system training environment model based on the IEC62443 Standard and develop an ARCH based training method.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.12
• /
• pp.125-134
• /
• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.