• The Journal of Society for e-Business Studies
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• v.20 no.3
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• pp.113-125
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• 2015

Information Security Incidents that have recently happen rapidly spread and the scale of that incidents' damage is large. In addition, as it proceeds to the era of converged industry in the future environment and the virtual cyber world expands to the physical world, new types of security threats have occurred. Now, it is time to supply security professionals who have a multi-dimensional security capabilities that can manage the strategies of technological security and physical security from the management point of view, rather than the ones who primarily focus on the traditional technologic-centered strategies to solve new types of security threats. In conclusion, in this paper we try to produce the curriculum of information security featured in the occupational classification system and analyze the subjects that are additionally required for those who move to other occupations to cultivate security professionals who suited to the converged-industrial environment. It is expected that multi-dimensional security professionals who suited to the converged-industrial environment will be cultivated by harmoniously integrating information security subjects from technological and business/managerial perspectives, and education training courses will be developed that effectively provide core knowledges per occupational classification when people moves to other occupations in the areas of information security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.629-643
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• 2019

In the era of the 4th industrial revolution, countries around the world are conducting projects to rapidly expand smart factory to secure competitiveness in manufacturing industries. However, unlike existing factories where the network environment was closed, smart factories can be vulnerable because internal and external objects are interconnected and various ICT technologies are used. And smart factories are likely to be the subject of cyber-attacks that are designed to cause monetary damage to certain targets because economic damage is so serious when an accident occurs. Therefore, it is necessary to study and apply security for smart factories, but there is no specific smart factory system architecture, so there is no establish for smart factory security requirements. In order to solve these problems, this paper derives the smart factory architecture that can extract and reflect the main characteristics of a smart factory based on the domestic and foreign reference model of smart factories. And this paper identifies the security threats based on the derived smart factory architecture and present the security requirements to cope with them for contributing to the improvement of the security of the smart factory.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.871-883
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• 2014

New types of attacks that mainly compromise the public, portal and financial websites for the purpose of economic profit or national confusion are being emerged and evolved. In addition, in case of 'drive by download' attack, if a host just visits the compromised websites, then the host is infected by a malware. Website falsification detection system is one of the most powerful solutions to cope with such cyber threats that try to attack the websites. Many domestic CERTs including NCSC (National Cyber Security Center) that carry out security monitoring and response service deploy it into the target organizations. However, the existing techniques for the website falsification detection system have practical problems in that their time complexity is high and the detection accuracy is not high. In this paper, we propose website falsification detection system based on image and code analysis for improving the performance of the security monitoring and response service in CERTs. The proposed system focuses on improvement of the accuracy as well as the rapidity in detecting falsification of the target websites.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.847-857
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• 2020

As IoT equipment is commercialized, Bluetooth or wireless networks will be built into general living devices such as IP cameras, door locks, cars and TVs. Security for IoT equipment is becoming more important because IoT equipment shares a lot of information through the network and collects personal information and operates the system. In addition, web-based attacks and application attacks currently account for a significant portion of cyber threats, and security experts are analyzing the vulnerabilities of cyber attacks through manual analysis to secure them. However, since it is virtually impossible to analyze vulnerabilities with only manual analysis, researchers studying system security are currently working on automated vulnerability detection systems, and Firm-AFL, published recently in USENIX, proposed a system by conducting a study on fuzzing processing speed and efficiency using a coverage-based fuzzer. However, the existing tools were focused on the fuzzing processing speed of the firmware, and as a result, they did not find any vulnerability in various paths. In this paper, we propose IoTFirmFuzz, which finds more paths, resolves constraints, and discovers more crashes by strengthening the mutation process to find vulnerabilities in various paths not found in existing tools.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.763-780
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• 2024

As the domestic and international landscape undergoes rapid changes, the importance of implementing security measures in response to the growing threats that businesses face is increasing. In this context, the need for Security by Design (SbD), integrating security from the early design stages, is becoming more pronounced, with threat modeling recognized as a fundamental tool of SbD. Particularly, to save costs and time by detecting and resolving security issues early, the application of the Shift Left strategy requires the involvement of personnel with limited security expertise, such as software developers, in threat modeling. Although various automated threat modeling tools have been released, their lack of user-friendliness for personnel lacking security expertise poses challenges in conducting threat modeling effectively. To address this, we conducted an analysis of research related to threat modeling tools and derived usability evaluation criteria based on the GQM(Goal-Question-Metric) approach. An expert survey was conducted to validate both the validity and objectivity of the derived criteria. We performed usability evaluations of three threat modeling tools (MS TMT, SPARTA, PyTM), and the evaluation results led to the conclusion that MS TMT exhibited superior usability compared to other tools. This study aims to contribute to the creation of an environment where personnel with limited security expertise can effectively conduct threat modeling by proposing usability evaluation criteria.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.695-706
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• 2018

In recent years, industrial convergence centered on ICBM (internet of things (IoT), cloud, big data, mobile) has been experiencing rapid development in various fields such as agriculture and the financial industry. In order to prepare for cyber threats, one of the biggest problems facing the convergence industry in the future, the development of the industry must proceed in tandem with a framework of information security. In this study, we analyze the details of the current industrial development policy and related information protection policies using cross impact analysis and present policy priorities through the expert questionnaire. The aim of the study was to clarify the priorities and interrelationships within information security policy as a first step in suggesting effective policy direction. As a result, all six information security policy tasks derived from this study belong to key drivers. Considering the importance of policies, policies such as improving the constitution of the security industry and strengthening of support, training of information protection talent, and investing in the information security industry need to be implemented relatively first.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.147-153
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• 2015

Since the DoS(Denial of Service) attack is still an important vulnerable element in many web service sites, sites including public institution should try their best in constructing defensive systems. Recently, DDoS(Distributed Denial of Service) has been raised by prompting mass network traffic that uses NTP's monlist function or DoS attack has been made related to the DNS infrastructure which is impossible for direct defense. For instance, in June 2013, there has been an outbreak of an infringement accident where Computing and Information Agency was the target. There was a DNS application DoS attack which made the public institution's Information System impossible to run its normal services. Like this, since there is a high possibility in having an extensive damage due to the characteristics of DDoS in attacking unspecific information service and not being limited to a particular information system, efforts have to be made in order to minimize cyber threats. This thesis proposes a method for using TTL (Time To Live) value in IP header to detect DDoS attack with IP spoofing, which occurs when data is transmitted under the agreed regulation between the international and domestic information system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.693-704
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• 2020

As a company's network structure is getting bigger and the number of security system is increasing, it is not easy to quickly detect abnormal traffic from huge amounts of security system events. In this paper, We propose traffic visualization analysis system(FDANT-PCSV) that can detect and analyze security events of information security systems such as firewalls in real time. FDANT-PCSV consists of Parallel Coordinates visualization using five factors(source IP, destination IP, destination port, packet length, processing status) and Sankey visualization using four factors(source IP, destination IP, number of events, data size) among security events. In addition, the use of big data-based SIEM enables real-time detection of network attacks and network failure traffic from the internet and intranet. FDANT-PCSV enables cyber security officers and network administrators to quickly and easily detect network abnormal traffic and respond quickly to network threats.