• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.633-642
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• 2005

Ubiquitous computing system is about networked processors, which is constructed with one or more computers interconnected by the networks. However, traditional security solution lacks a Proactive maintenance technique because of its focusing on developing the qualitative detection and countermeasure after attack. Thus, in this paper, we propose a quantitative assessment modeling technique, by which the general infrastructure can be improved and the attacks on a specific infrastructure be detected and protected. First of all, we develop the definition of survivality and modeling technique for quantitative assessment modeling with the static information on the system random information, and attack-type modeling. in addition, the survivality analysis on TCP-SYN attack and code-Red worm attack is performed for validating the proposed technique.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.158-163
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• 2012

This paper proposes an FHIDS(Fuzzy logic based Hybrid Intrusion Detection System) design that detects anomaly and misuse attacks by using a Naive Bayesian algorithm, Data Mining, and Fuzzy Logic. The NB-AAD(Naive Bayesian based Anomaly Attack Detection) technique using a Naive Bayesian algorithm within the FHIDS detects anomaly attacks. The DM-MAD(Data Mining based Misuse Attack Detection) technique using Data Mining within it analyzes the correlation rules among packets and detects new attacks or transformed attacks by generating the new rule-based patterns or by extracting the transformed rule-based patterns. The FLD(Fuzzy Logic based Decision) technique within it judges the attacks by using the result of the NB-AAD and DM-MAD. Therefore, the FHIDS is the hybrid attack detection system that improves a transformed attack detection ratio, and reduces False Positive ratio by making it possible to detect anomaly and misuse attacks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.470-476
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• 2019

The pattern locking technique applied to smart phones is a locking technique that many people use conveniently. However, the safety of pattern locking techniques is very low compared with other techniques. The pattern locking technique is vulnerable to a shoulder surfing attack, which is based on the user's input and can be interpreted by looking at the movement of the shoulder, and the smudge attack is also vulnerable due to fingerprint drag marks remaining on the mobile phone pad. Therefore, in this paper, we want to add a new security method to check the pressed time by using a thread in the pattern locking scheme to secure the vulnerability. It is divided into short, middle, and long click according to the pressing time at each point. When dragging using the technique, security performance enhances $3^n$ tiems. Therefore, even if dragging in the same 'ㄱ' manner, it becomes a completely different pattern depending on the pressing time at each point.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.25-36
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• 2023

Today, as AI (Artificial Intelligence) technology is introduced in various fields, including security, the development of technology is accelerating. However, with the development of AI technology, attack techniques that cleverly bypass malicious behavior detection are also developing. In the classification process of AI models, an Adversarial attack has emerged that induces misclassification and a decrease in reliability through fine adjustment of input values. The attacks that will appear in the future are not new attacks created by an attacker but rather a method of avoiding the detection system by slightly modifying existing attacks, such as Adversarial attacks. Developing a robust model that can respond to these malware variants is necessary. In this paper, we propose two methods of generating Adversarial attacks as efficient Adversarial attack generation techniques for improving Robustness in AI models. The proposed technique is the XAI-based attack technique using the XAI technique and the Reference based attack through the model's decision boundary search. After that, a classification model was constructed through a malicious code dataset to compare performance with the PGD attack, one of the existing Adversarial attacks. In terms of generation speed, XAI-based attack, and reference-based attack take 0.35 seconds and 0.47 seconds, respectively, compared to the existing PGD attack, which takes 20 minutes, showing a very high speed, especially in the case of reference-based attack, 97.7%, which is higher than the existing PGD attack's generation rate of 75.5%. Therefore, the proposed technique enables more efficient Adversarial attacks and is expected to contribute to research to build a robust AI model in the future.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.443-450
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• 2005

Digital fingerprinting is a contents-protection technique, where customer information is inserted into digital contents. Fingerprinted contents undergo various attacks. Especially, attackers can remove easily the inserted fingerprint by collusion attacks, because digital fingerprinting inserts slightly different codes according to the customers. Among collusion attacks, averaging attack is a simple, fast, and efficient attack. In this paper, we propose a video fingerprinting system that is robust to the averaging attack. In order to achieve code efficiency and robustness against the averaging attack, we adopt anti-collusion code (fingerprint code) from GD-PBIBD theory. When the number of users is increased, the size of fingerprint code also grows. Thus, this paper addresses a zero-based code modulation technique to embed and detect this fingerprint code efficiently. We implemented a blind video fingerprinting system based on our proposed technique and performed experiments on various colluding cases. Based on the results, we could detect most of colluders. In the worst case, our scheme could trace at least one colluder successfully.

• Convergence Security Journal
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• v.11 no.6
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• pp.25-30
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• 2011

MANET has a weak construction in security more because it is consisted of only moving nodes and doesn't have central management system. The DDoS attack is a serious attack among these attacks which threaten wireless network. The DDoS attack has various object and trick and become intelligent. In this paper, we propose the technique to raise DDoS detection rate by classifying abnormal traffic pattern. Cluster head performs sentinel agent after nodes which compose MANET are made into cluster. The decision tree is applied to detect abnormal traffic pattern after the sentinel agent collects all traffics and it judges traffic pattern and detects attack also. We confirm high attack detection rate of proposed detection technique in this study through experimentation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1568-1570
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• 2005

The internet is already a part of life. It is very convenient and people can do almost everything with internet that should be done in real life. Along with the increase of the number of internet user, various network attacks through the internet have been increased as well. Also, Large-scale network attacks are a cause great concern for the computer security communication. These network attack becomes biggest threat could be down utility of network availability. Most of the techniques to detect and analyze abnormal traffic are statistic technique using mathematical modeling. It is difficult accurately to analyze abnormal traffic attack using mathematical modeling, but network simulation technique is possible to analyze and simulate under various network simulation environment with attack scenarios. This paper performs modeling and simulation under virtual network environment including $NGSS^{1}$ system to analyze abnormal traffic-flooding attack.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.863-871
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• 2013

Recently application of open protocols and external network linkage to the national critical infrastructure has been growing with the development of information and communication technologies. This trend could mean that the national critical infrastructure is exposed to cyber attacks and can be seriously jeopardized when it gets remotely operated or controlled by viruses, crackers, or cyber terrorists. In this paper virtual Honeynet model which can reduce installation and operation resource problems of Honeynet system is proposed. It maintains the merits of Honeynet system and adapts the virtualization technology. Also, virtual Honeynet model that can minimize operating cost is proposed with data analysis and collecting technique based on the verification of attack intention and focus-oriented analysis technique. With the proposed model, new type of attack detection system based on virtual Honeynet, that is Cybertrap, is designed and implemented with the host and data collecting technique based on the verification of attack intention and the network attack pattern visualization technique. To test proposed system we establish test-bed and evaluate the functionality and performance through series of experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1027-1037
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• 2019

Conventional cyber-attack detection solutions are generally based on signature-based or malicious behavior analysis so that have had difficulty in detecting unknown method-based attacks. Since the various information occurring all the time reflects the state of the system, by modeling it in a steady state and detecting an abnormal state, an unknown attack can be detected. Since a variety of system information occurs in a string form, word embedding, ie, techniques for converting strings into vectors preserving their order and semantics, can be used for modeling and detection. Novelty Detection, which is a technique for detecting a small number of abnormal data in a plurality of normal data, can be performed in order to detect an abnormal condition. This paper proposes a method to detect system anomaly by cyber attack using embedding and novelty detection.