• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.703-709
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• 2011

State-based infrastructure on IT-based network are prone to numerous cyber attack including subsequent hacking and internet infringement. These acts of terrorism are increasing because of the expanding IT convergence technology. Recently, the trend on cyber security monitoring and control researches focus on combining the general idea of security monitoring and control along with IT field and other control systems. This convergence trend has been increasing in both the use and importance. This research analyzes the state-based infrastructure monitoring and control system, its vulnerability as well as its improvement by incorporating the cyber convergence systems to existing systems. The subject of this research is for extensive use of CCTV systems which is expanded for 'CCTV Monitoring and Control Field' as well as 'Traffic Monitoring and Control Field' operated by 'Intelligent Traffic Information System' and Disaster Management Area which is studied in various fields. Eventually, the objective of the paper is to solve these issues, to apply related systems and to suggest improvement on the convergence system.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.79-89
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• 2010

The government and companies build a DDoS correspondence system hastily to protect assets from cyber threats. It has become more and more intelligent and advanced such as DDoS attack. However, when outbreaks of the social incidents such as 7.7 DDoS attack(2009.7.7) or cases of the direct damage occurred, information security systems(ISS) only become the issue in the short term. As usual, sustained investment about ISS is a negative recognition. Since the characteristic of ISS is hard to recognize the effectiveness of them before incidents occurs. Also, results of incidents occurred classify attack and detection. Detailed and objective measurement criterion to measure effectiveness and efficiency of ISS is not existed. Recently, it is progress that evaluation and certification about for the information security management system(ISMS). Since these works propose only a general guideline, it is difficult to utilize as a result of ISMS improvement for organization. Therefore, this paper proposes a framework to develop main criteria by a correspondence strategy and process. It is able to detailed and objective measurements.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.49-60
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• 2015

Smart Grid devices are the major components of the Smart Grid. They collect and process a variety informations relating power services and support intelligent power services by exchanging informations with other SG devices or systems. However, If a SG device is attacked, the device can provide attack route to attacker and attacker can attack other SG devices or systems using the route. It may cause problem in power services. So, when cyber incident is happened, we need to acquire and examine digital evidence of SG device quickly to secure availability of SG. In this paper, we designed remote evidence acquisition system to acquire digital evidences from SG devices to response quickly to incidents of SG devices. To achieve this, we analyzed operating environment of SG devices and thought remote digital evidence acquisition system of SG devices will be more effective than remote digital evidence acquisition system targeted general IT devices. So, we introduce design method for SG devices remote evidence acquisition system considered operating environment of SG devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1067-1076
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• 2023

Recently, as variant malware has increased, the scale of cyber hacking incidents is expanding. To respond to intelligent cyberhacking attack, machine learning-based research is actively underway to effectively classify malware families. However, existing classification models have problems where performance deteriorates when the dataset is obfuscated or sparse. In this paper, we propose a hybrid dataset that combines features extracted from ASM files and BYTES files, and evaluate classification performance using FNN. As a result of the experiment, the proposed method showed performance improvement of about 4% compared to a single dataset, and in particular, performance improvement of about 30% for rare families.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1399-1410
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• 2015

Gartner is requiring companies to considerably change their survival paradigms insisting that companies need to understand and provide again the upcoming era of data competition. With the revealing of successful business cases through statistic algorithm-based predictive analytics, also, the conversion into preemptive countermeasure through predictive analysis from follow-up action through data analysis in the past is becoming a necessity of leading enterprises. This trend is influencing security analysis and log analysis and in reality, the cases regarding the application of the big data analysis framework to large-scale log analysis and intelligent and long-term security analysis are being reported file by file. But all the functions and techniques required for a big data log analysis system cannot be accommodated in a Hadoop-based big data platform, so independent platform-based big data log analysis products are still being provided to the market. This paper aims to suggest a framework, which is equipped with a real-time and non-real-time predictive analysis engine for these independent big data log analysis systems and can cope with cyber attack preemptively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.807-808
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• 2016

With the recent rise in nuclear families and single-member families, there is a need for the kind of home management unaffected by neither space nor time. Moreover, electronic devices in and around the home need to be managed efficiently and prevented from overheating, and there is an increasing risk of fire, theft, and leak of personal data with these devices, which is leading to an increase in the economic costs. Accordingly, there is a growing need for an efficient and secure smart home management system. This paper proposes a home management system that uses smart devices. This system has addressed the shortcomings of a conventional Internet-based home network. Furthermore, it communicates with IoT-enabled devices and features intelligent information home appliances that are isolated from personally identifiable information and which are secure against advanced persistent threats, a type of cyber-attack.