• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.53-58
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• 2021

There is little research on actual business activities in the field of security control. Therefore, in this paper, we intend to present a practical research methodology that can contribute to the calculation of the size of the appropriate input personnel through the modeling of the threat information detection response time of the security control and to analyze the effectiveness of the latest security solutions. The total threat information detection response time performed by the security control center is defined as TIDRT (Total Intelligence Detection & Response Time). The total threat information detection response time (TIDRT) is composed of the sum of the internal intelligence detection & response time (IIDRT) and the external intelligence detection & response time (EIDRT). The internal threat information detection response time (IIDRT) can be calculated as the sum of the five steps required. The ultimate goal of this study is to model the major business activities of the security control center with an equation to calculate the cyber threat information detection response time calculation formula of the security control center. In Chapter 2, previous studies are examined, and in Chapter 3, the calculation formula of the total threat information detection response time is modeled. Chapter 4 concludes with a conclusion.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.793-799
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• 2021

Recently, there has been a growing interest in threat hunting presented to overcome the limitations of existing security solutions. Threat hunting is generally recognized as a technique for identifying and eliminating threats that exit inside the system. But, the definition is not clear, so there is confusion in terms with penetration testing, intrusion detection, and incident analysis. Therefore, in this paper, compare and analyze the definitions of threat hunting extracted from reports and papers to clarify their implications and compare with defense techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.868-871
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• 2005

We proposed the detection method and security threat under ubiquitous surroundings. We described the problems that must be faced in the design of such a wireless protocol model. The internet is a natural and universal means of providing this interconnection. The networking of these ubiquitous computing devices is driven by the synergy between three trends. In this paper, we analysed the security model under ubiquitous surroundings.

• Convergence Security Journal
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• v.3 no.2
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• pp.67-70
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• 2003

Since IT industries grew, The information security of both individual and company has come to the front. But, nowadays, It is very hard to satisfy the diversity of security Protection Profile with simple Intrusion Detection System, because of highly developed Intrusion Skills. The Intrusion Detection System is the system that detects, reports and copes with of every kind of Intrusion actions immediately. In this paper, we compare the concept of IDS PPs and analyze the threat of PP.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.55-62
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• 2018

Recently, as the threat of cyber crime increases, the importance of security control to cope with cyber attacks on the information systems in the first place such as real-time detection is increasing. In the name of security control center, cyber terror response center and infringement response center, institutional control personnel are making efforts to prevent cyber attacks. Especially, we are detecting infringement accident by using network security equipment or utilizing control system, but it's not enough to prevent infringement accident by just controlling based on device-driven simple patterns. Therefore, the security control system is continuously being upgraded, and the development and research on the detection method are being actively carried out by the prevention activity against the threat of infringement. In this paper, we have defined the method of detecting infringement of major component module in order to improve the problem of existing infringement detection method. Through the performance tests for each module, we propose measures for effective security control and study effective infringement threat detection method by upgrading the control system using Security Information Event Management (SIEM).

• Journal of Platform Technology
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• v.11 no.2
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• pp.15-25
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• 2023

This study analyzes the limitations of the insider threat datasets used for insider threat detection research and compares and analyzes the solution-based insider threat data with public insider threat data using a security solution to overcome this. Through this, we design a data format suitable for insider threat detection and implement a system that can safely share insider threat information between different institutions and companies using blockchain technology. Currently, there is no dataset collected based on actual events in the insider threat dataset that is revealed to researchers. Public datasets are virtual synthetic data randomly created for research, and when used as a learning model, there are many limitations in the real environment. In this study, to improve these limitations, a private blockchain was designed to secure information sharing between institutions of different affiliations, and a method was derived to increase reliability and maintain information integrity and consistency through agreement and verification among participants. The proposed method is expected to collect data through an outflow threat collector and collect quality data sets that posed a threat, not synthetic data, through a blockchain-based sharing system, to solve the current outflow threat dataset problem and contribute to the insider threat detection model in the future.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.1-10
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• 2018

Network threats such as Internet worms and computer viruses have been significantly increasing. In particular, APTs(Advanced Persistent Threats) and ransomwares become clever and complex. IDSes(Intrusion Detection Systems) have performed a key role as information security solutions during last few decades. To use an IDS effectively, IDS rules must be written properly. An IDS rule includes a key signature and is incorporated into an IDS. If so, the network threat containing the signature can be detected by the IDS while it is passing through the IDS. However, it is challenging to find a key signature for a specific network threat. We first need to analyze a network threat rigorously, and write a proper IDS rule based on the analysis result. If we use a signature that is common to benign and/or normal network traffic, we will observe a lot of false alarms. In this paper, we propose a scheme that analyzes a network threat and extracts key signatures corresponding to the threat. Specifically, our proposed scheme quantifies the degree of correspondence between a network threat and a signature using the LDA(Latent Dirichlet Allocation) algorithm. Obviously, a signature that has significant correspondence to the network threat can be utilized as an IDS rule for detection of the threat.

• Journal of IKEEE
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• v.23 no.4
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• pp.1134-1139
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• 2019

One of the most important applications of computer vision algorithms is the detection of threat objects in x-ray security images. However, in the practical setting, this task is complicated by two properties inherent to the dataset, namely, the problem of class imbalance and visual complexity. In our previous work, we resolved the class imbalance problem by using a GAN-based anomaly detection to balance out the bias induced by training a classification model on a non-practical dataset. In this paper, we propose a new method to alleviate the visual complexity problem by using a KNN-based automatic cropping algorithm to remove distracting and irrelevant information from the x-ray images. We use the cropped images as inputs to our current model. Empirical results show substantial improvement to our model, e.g. about 3% in the practical dataset, thus further outperforming previous approaches, which is very critical for security-based applications.