• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.4
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• pp.3-19
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• 2009

This paper proposes efficient scalar multiplication algorithms based on Montgomery ladder method. The proposed algorithm represents the scalar as ternary or quaternary and applies new composite formulas utilizing only x coordinate on affine coordinate system in order to improve performance. Furthermore, side-channel atomicity mechanism is applied on the proposed composite formulas to prevent simple power analysis. The proposed methods saves at least 26% of running time with the reduced number of storage compared with existing algorithms such as window-based methods and comb-based methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5143-5158
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• 2018

Improving the intrusion detection system (IDS) is a pressing need for cyber security world. With the growth of computer networks, there are constantly daily new attacks. Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues. One of these issues relates to the imbalance of the diverse classes of network traffic. Accuracy paradox is a result of training ML algorithm with imbalanced classes. Most of the previous efforts concern improving the overall accuracy of these models which is truly important. However, even they improved the total accuracy of the system; it fell in the accuracy paradox. The seriousness of the threat caused by the minor classes and the pitfalls of the previous efforts to address this issue is the motive for this work. In this paper, we consolidated stratified sampling, cost function and weighted Support Vector Machine (WSVM) method to address the accuracy paradox of ID problem. This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99 which is called NSL-KDD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6145-6158
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• 2019

It is a challenge for the current security industry to respond to a large number of malicious codes distributed indiscriminately as well as intelligent APT attacks. As a result, studies using machine learning algorithms are being conducted as proactive prevention rather than post processing. The k-NN algorithm is widely used because it is intuitive and suitable for handling malicious code as unstructured data. In addition, in the malicious code analysis domain, the k-NN algorithm is easy to classify malicious codes based on previously analyzed malicious codes. For example, it is possible to classify malicious code families or analyze malicious code variants through similarity analysis with existing malicious codes. However, the main disadvantage of the k-NN algorithm is that the search time increases as the learning data increases. We propose a fast k-NN algorithm which improves the computation speed problem while taking the value of the k-NN algorithm. In the test environment, the k-NN algorithm was able to perform with only the comparison of the average of similarity of 19.71 times for 6.25 million malicious codes. Considering the way the algorithm works, Fast k-NN algorithm can also be used to search all data that can be vectorized as well as malware and SSDEEP. In the future, it is expected that if the k-NN approach is needed, and the central node can be effectively selected for clustering of large amount of data in various environments, it will be possible to design a sophisticated machine learning based system.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.437-445
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• 2023

A space system refers to a network of sensors, ground systems, and space-craft operating in space. The security of space systems relies on information systems and networks that support the design, launch, and operation of space missions. Characteristics of space operations, including command and control (C2) between space-craft (including satellites) and ground communication, also depend on wireless frequency and communication channels. Attackers can potentially engage in malicious activities such as destruction, disruption, and degradation of systems, networks, communication channels, and space operations. These malicious cyber activities include sensor spoofing, system damage, denial of service attacks, jamming of unauthorized commands, and injection of malicious code. Such activities ultimately lead to a decrease in the lifespan and functionality of space systems, and may result in damage to space-craft and, lead to loss of control. The Cybersecurity Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) matrix, proposed by Massachusetts Institute of Technology Research and Engineering (MITRE), consists of the following stages: Reconnaissance, Resource Development, Initial Access, Execution, Persistence, Privilege Escalation, Defense Evasion, Credential Access, Discovery, Lateral Movement, Collection, Command & Control, Exfiltration, and Impact. This paper identifies cybersecurity activities in space systems and satellite navigation systems through the National Institute of Standards and Technology (NIST)'s standard documents, former U.S. President Trump's executive orders, and presents risk management activities. This paper also explores cybersecurity's tactics attack techniques within the context of space systems (space-craft) by referencing the Sparta ATT&CK Matrix. In this paper, security threats in space systems analyzed, focusing on the cybersecurity attack tactics, techniques, and countermeasures of space-craft presented by Space Attack Research and Tactic Analysis (SPARTA). Through this study, cybersecurity attack tactics, techniques, and countermeasures existing in space-craft are identified, and an understanding of the direction of application in the design and implementation of safe small satellites is provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.933-939
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• 2007

There is a tremendous increase in the growth of Internet making people's life easy. The rapid growth in technology has caused misuse of the Internet like cyber Crime. There are several vulnerabilities in current firewall and Intrusion Detection Systems (IDS) of the Network Computing resources. Automatic real time station chase techniques can track the internet invader and reduce the probability of hacking Due to the recent trends the station chase technique has become inevitable. In this paper, we design and implement Active Security system using ICMP Traceback message. In this design no need to modify the router structure and we can deploy this technique in larger network. Our Implementation shows that ICMP Traceback system is safe to deploy and protect data in Internet from hackers and others.

• Journal of the Society of Disaster Information
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• v.12 no.1
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• pp.89-97
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• 2016

According to the statistics released by Ministry of Public Safety and Security, the number of restaurant fires in Korea reached around 2,400 and 169 damages of human life and damages to property was approximately over $8.8 billion for recent 3 years. It could be desirable having automatic commercial fire-extinguishing equipment at commercial facilities excluding housing facilities for the safety, applying at the place first where it has been more risky and expected fire can be occurred relatively because economical burden can be accelerated. In order to do this, adjust its level to meet the same level of the kitchen for 'Specific Target for Fire Fighting' that "gas leak alarm" has be equipped relevant regulations and it is considered and reasonable to expand the limit of application gradually.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.309-312
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• 2016

Considering that interkorean relations got worse and worse recently, cyber terror of North Korea has seriously become a possibility. Therefore, DoS(Denial of Service), a typical way of cyber terror, is becoming a big issue. Consequently, people are growing more and more interested in information security. Internal DoS attacks, out of a variety of ways of Dos attacks, include disks and memories and shortages of process resources. PAM(Pluggable Authentication Module) is one of the ways of preventing internal DoS attacks in Linux system. This paper provides with a method to internally respond to dos attacks and efficiently prevent shortages of resources by utilizing PAM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.358-360
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• 2022

Modern society is developing rapidly and technologies that provide convenience in living are developing day by day. On the other hand, the development of cyber attacks that threaten cybersecurity is developing faster, and it still adversely affects the industrial environment, and industrial damage is steadily occurring every year. Industrial security is an activity that safely protects major assets or technologies of companies and organizations from these attacks. Therefore, it is a situation that requires professional manpower for security. Currently, the manpower situation for security is staffed, but knowledge of the understanding and concept of industrial security jobs is insufficient. In other words, there is a lack of professional manpower for industrial security. It is the NCS that came out to solve this problem. NCS is the state standardized ability (knowledge, attitude, skills, etc.) necessary to perform duties in the industrial field. NCS can systematically design the curriculum using NCS as well as help in hiring personnel, and NCS can be applied to the national qualification system. However, in the field of industrial security, NCS has not yet been developed and is still having difficulties in hiring personnel and curriculum. Although the NCS system in the field of industrial security has not been developed, this paper proposes the industrial security NCS to solve the problem of hiring professionals later and to help the field of industrial security NCS to be established later.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.