• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.303-310
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• 2011

Recently, many user authentication schemes using smart cards have been proposed to improve the security weaknesses in user authentication process. In 2009, Wang et al. proposed a more effective and secure dynamic ID-based remote user authentication scheme to improve the security weakness of Das et al.'s scheme, and asserted that the improved scheme is secure against independent of password in authentication phase and provides mutual authentication between the user and the remote server. However, in this paper, we analyze the security of Wang et al. scheme and demonstrate that Wang et al.'s scheme is vulnerable to the man-in-the-middle attack and the off-line password guessing attack. In addition, we show that Wang et al. scheme also fails to provide mutual authentication. Accordingly, we propose an improved scheme to overcome these security weakness even if the secrete information stored in the smart card is revealed. Our proposed scheme can withstand the user impersonation attack, the server masquerading attack and off-line password guessing attack. Furthermore, this improved scheme provides the mutual authentication and is more effective than Wang et al.'s scheme in term of the computational complexities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.208-218
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• 2006

In this paper, we introduce the creation methods of attack detection model using data mining technologies that can classify the latest attack types, and can detect the modification of existing attacks as well as the novel attacks. Also, we evaluate comparatively these attack detection models in the view of detection accuracy and detection time. As the important factors for creating detection models, there are data, attribute, and detection algorithm. Thus, we used NetFlow data gathered at the real network, and KDD Cup 1999 data for the experiment in large quantities. And for attribute selection, we used a heuristic method and a theoretical method using decision tree algorithm. We evaluate comparatively detection models using a single supervised/unsupervised data mining approach and a combined supervised data mining approach. As a result, although a combined supervised data mining approach required more modeling time, it had better detection rate. All models using data mining techniques could detect the attacks within 1 second, thus these approaches could prove the real-time detection. Also, our experimental results for anomaly detection showed that our approaches provided the detection possibility for novel attack, and especially SOM model provided the additional information about existing attack that is similar to novel attack.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.223-230
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• 2009

Among the remote user authentication schemes, password-based authentication methods are the most widely used. In 2004, Das et al. proposed a "Dynamic ID Based Remote User Authentication Scheme" that is the password based scheme with smart-cards, and is the light-weight technique using only one-way hash algorithm and XOR calculation. This scheme adopts a dynamic ID that protects against ID-theft attack, and can resist replay attack with timestamp features. Later, many flaws of this scheme were founded that it allows any passwords to be authenticated, and can be vulnerable to impersonation attack, and guessing attack. By this reason many modifications were announced. These scheme including all modifications are similarly maintained security against replay the authentication message attack by the timestamp. But, if advisory can replay the login immediately, this attempt can be succeeded. In this paper, we analyze the security vulnerabilities of Das scheme, and propose improved scheme which can resist on real-time replay attack using the counter of authentication. Besides our scheme still secure against impersonation attack, guessing attack, and also provides mutual authentication feature.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1103-1116
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• 2014

Collision attacks using side channel analysis confirm same intermediate value and restore sensitive data of algorithm using this point. In CHES 2011 Clavier and other authors implemented the improved attack using Blacklist so they carried out the attack successfully using less plaintext than before. However they did not refer the details of Blacklist method and just performed algorithms with the number of used plaintext. Therefore in this paper, we propose the specific method to carry out efficient collision attack. At first we define basic concepts, terms, and notations. And using these, we propose various methods. Also we describe facts that greatly influence on attack performance in priority, and then we try to improve the performance of this attack by analyzing the algorithm and structuring more efficient one.

• 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.

• Convergence Security Journal
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• v.19 no.1
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• pp.103-110
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• 2019

Recently security related attacks occur in very diverse ways, aiming at people who operate the system rather than the system itself by exploiting vulnerabilities of the system. However, to the our best knowledge, there has been very few works to analyze and strategically to deal with the risks of social engineering attacks targeting people. In this paper, in order to access risks of social engineering attacks we analyze those attacks in terms of attack routes, attack means, attack steps, attack tools, attack goals. Then, with the purpose of accessing the organizational risks we consider the characteristics and environments of the organizations because the impacts of attacks on the organizations obviously depend on the characteristics and environments of the organizations. In addition, we analyze general attack risk assessment methods such as CVSS, CWSS, and OWASP Risk Rating Methodolog. Finally, we propose the risk access scheme of social engineering attacks for the organizations. The proposed scheme allows each organization to take its own proper actions to address social engineering attacks according to the changes of its environments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.81-88
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• 2021

In this paper, we analyze the possibility of deregistration attack in 5G SNPN (Standalone Non-Public Network) based on 3GPP standard document. In the deregistraion attack, the attacker pretends to be a UE that is normally registered with AMF (Access and Mobility Management Function) and attempts to establish a spoofed RRC (Radio Resource Control) connection, causing AMF to deregister the existing UE. The existing deregistration attack attempts a spoofed RRC connection to the AMF in which the UE is registered. In addition, this paper analyzes whether deregistration attack is possible even when an attacker attempts to establish a spoofed RRC connection to a new AMF that is different from the registered AMF. When the 5G mobile communication network system is implemented by faithfully complying with the 3GPP standard, it is determined that a deregistration attack of a UE is impossible.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.101-110
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• 2011

Recently, an attack to an application incapacitates the intrusion detection rule, the defense policy for a network and database and induces intrusion incidents. Thus, it is necessary to study integration security to ensure the security of an internal network and database from that attack. This article is about building an integration security system to prevent an attack to an application set with intrusion detection rules. It responds to network-based attack through detection, disperses attack with the internal integration security system through virtual clustering and load balancing, and sets up defense policy for attacking destination packets, analyzes and records attack packets, and updates rules through monitoring and analysis. Moreover, this study establishes defense policy according to attacking types to settle access traffic through virtual machine partition policy and suggests an integration security system applied to prevent attack and tests its defense. The result of this study is expected to provide practical data for integration security defense for hacking attack from outside.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.27-35
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• 2022

In this paper we propose white noise adding method to prevent missclassification of deep learning system by adversarial attacks. The proposed method is that adding white noise to input image that is benign or adversarial example. The experimental results are showing that the proposed method is robustness to 3 adversarial attacks such as FGSM attack, BIN attack and CW attack. The recognition accuracies of Resnet model with 18, 34, 50 and 101 layers are enhanced when white noise is added to test data set while it does not affect to classification of benign test dataset. The proposed model is applicable to defense to adversarial attacks and replace to time- consuming and high expensive defense method against adversarial attacks such as adversarial training method and deep learning replacing method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1227-1236
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• 2021

Adversarial attack, one of the attacks on deep learning classification model, is attack that add indistinguishable perturbations to input data and cause deep learning classification model to misclassify the input data. There are various adversarial attack algorithms. Accordingly, many studies have been conducted to detect adversarial attack but few studies have been conducted to classify what adversarial attack algorithms to generate adversarial input. if adversarial attacks can be classified, more robust deep learning classification model can be established by analyzing differences between attacks. In this paper, we proposed a model that detects and classifies adversarial attacks by constructing a random forest classification model with input features extracted from a target deep learning model. In feature extraction, feature is extracted from a output value of hidden layer based on class predicted by the target deep learning model. Through Experiments the model proposed has shown 3.02% accuracy on clean data, 0.80% accuracy on adversarial data higher than the result of pre-existing studies and classify new adversarial attack that was not classified in pre-existing studies.