• Journal of KIISE:Information Networking
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• v.36 no.2
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• pp.90-97
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• 2009

In 2008, Kim-Jun proposed a RFID mutual authentication protocol using one-time random number that can withstand malicious attacks by the leakage of important information and resolve the criminal abuse problems. Through the security analysis, they claimed that the proposed protocol can withstand various security attacks including the replay attack. However, this paper demonstrates that Kim-Jun' s RFID authentication protocol still insecure to the replay attack. In addition, this paper also proposes a simply improved RFID mutual authentication protocol using one-time random number which not only provides same computational efficiency, but also withstands the replay attack.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.165-172
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• 2019

Machine-learning techniques have been actively employed to information security in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks due to unpredictable behaviors and unknown vulnerabilities. By employing ML techniques, we are able to develop intrusion detection systems (IDS) based on anomaly detection instead of misuse detection. Moreover, threshold issues in anomaly detection can also be resolved through machine-learning. There are very few datasets for network intrusion detection compared to datasets for malicious code. KDD CUP 99 (KDD) is the most widely used dataset for the evaluation of IDS. Numerous studies on ML-based IDS have been using KDD or the upgraded versions of KDD. In this work, we develop an IDS model using CSE-CIC-IDS 2018, a dataset containing the most up-to-date common network attacks. We employ deep-learning techniques and develop a convolutional neural network (CNN) model for CSE-CIC-IDS 2018. We then evaluate its performance comparing with a recurrent neural network (RNN) model. Our experimental results show that the performance of our CNN model is higher than that of the RNN model when applied to CSE-CIC-IDS 2018 dataset. Furthermore, we suggest a way of improving the performance of our model.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.83-91
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• 2012

Authentication schemes preserving user anonymity have first been proposed by Das et al, and most of user anonymity schemes provide user anonymity against outside attacks in the communication channel. In this paper, according to the increasing of personal information exposure incidents by server attack, we propose a new authentication scheme that provides user anonymity against server as well as one against outside attacks in the communication channel. Furthermore, the proposed authentication scheme provides traceability that remote server should be able to trace the malicious user and it also solves the problem of increasing computational load of remote server by solving weakness of wrong password input by mistake.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.435-442
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• 2000

In the world of mobile agents, security aspects are extensively being discussed, with strong emphasis on how agents can be protected against malicious hosts and vice versa. This paper discusses methods for protecting an agent's route information from being misused by sites on route interested in gaining insight into the profile of the agent's owner or in obstructing the owner's original goal. Our methods provide visited sites with just a minimum of route information, but on the other hand allow sites to detect modifying attacks of preceding sites. Though, under noncolluding attacks, all methods presented provide a similar level of protection, the performance and the points of time differ when an attack can be detected.

• Convergence Security Journal
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• v.11 no.6
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• pp.3-8
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• 2011

Advanced Persistent Threat (APT), aims a specific business or political targets, is rapidly growing due to fast technological advancement in hacking, malicious code, and social engineering techniques. One of the most important characteristics of APT is persistence. Attackers constantly collect information by remaining inside of the targets. Enterprise Security Management (EMS) system can misidentify APT as normal pattern of an access or an entry of a normal user as an attack. In order to analyze this misidentification, a new system development and a research are required. This study suggests the way of forecasting APT and the effective countermeasures against APT attacks by categorizing misidentified data in data-mining through threshold ratings. This proposed technique can improve the detection of future APT attacks by categorizing the data of long-term attack attempts.

• Convergence Security Journal
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• v.11 no.6
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• pp.53-58
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• 2011

There is increasing use of common commercial operation system and standard PCs to control industrial production systems, and cyber security threat for industrial facilities have emerged as a serious problem. Now these network connected ICS(Industrial Control Systems) stand vulnerable to the same threats that the enterprise information systems have faced and they are exposed to malicious attacks. In particular Stuxnet is a computer worm targeting a specific industrial control system, such as a gas pipeline or power plant and in theory, being able to cause physical damage. In this paper we present an overview of the general configuration and cyber security threats of a SCADA and investigate the attack tree analysis to identify and assess security vulnerabilities in SCADA for the purpose of response to cyber attacks in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2236-2257
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• 2020

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1077-1082
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• 2016

IDS can be used as a countermeasure for malicious attacks which cause degrade of network transmission performance by disturbing of MANET routing function. In this paper, effects of IDS for transmission performance on MANET under grayhole attacks which has intrusion objects for a part of transmissions packets, some suggestion for effective IDS will be considered. Computer simulation based on NS-2 is used for performance analysis, performance is measured with VoIP(: Voice over Internet Protocol) as an application service. MOS(: Mean Opinion Score), CCR(: Call Connection Rate) and end-to-end delay is used for performance parameter as standard transmission quality factor for voice transmission.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1042-1062
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• 2010

Cooperative spectrum sensing (CSS) is an effective technology for alleviating the unreliability of local spectrum sensing due to fading/shadowing effects. Unlike most existing solutions, this paper considers the use of CSS technology in decentralized networks where a fusion center is not available. In such a decentralized network, some attackers may sneak into the ranks of cooperative users. On the basis of recent advances in bio-inspired consensus algorithms, an attack-proof, decentralized CSS scheme is proposed in which all secondary users can maintain cooperative sensing by exchanging information locally instead of requiring centralized control or data fusion. Users no longer need any prior knowledge of the network. To counter three potential categories of spectrum sensing data falsification (SSDF) attacks, some anti-attack strategies are applied to the iterative process of information exchange. This enables most authentic users to exclude potentially malicious users from their neighborhood. As represented by simulation results, the proposed scheme can generally ensure that most authentic users reach a consensus within the given number of iterations, and it also demonstrates much better robustness against different SSDF attacks than several existing schemes.