• 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.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.88-93
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• 2011

Due to vulnerable authentication scheme of SIP, intruders can easily impersonate legitimate user. HTTP Digest authentication scheme or private key issued by trust third parties has been used to prevent impersonation attack. However, these methods have suffered security vulnerability or service delay due to computation overhead. In this paper, we propose new authentication method to generate automatically one-time password using the pre-shared password and time information of messages exchanged between SIP UA(User Agent) and SIP Registrar. This method protects against impersonation attack without significant modification of exiting SIP authentication procedure to build securer SIP environment.

• Computers and Concrete
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• v.19 no.1
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• pp.19-31
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• 2017

This paper uses modelling and experiment to perform a quantitative analysis for the gypsum and ettringite formations in cement pastes subjected to sulfate attack. Firstly, based on Fick's law and chemical reaction kinetics, a diffusion model of sulfate ions in cement pastes is proposed, and then the model of the gypsum and ettringite formations is established to analyze its contents in cement pastes with corrosion time. Secondly, the corrosion experiment of the specimens with cement pastes immersed into 2.5%, 5.0% and 10.0% $Na_2SO_4$ solutions are carried out, and by using XRD-Rietveld method, the phases of powder samples from the specimens are quantitatively analyzed to obtain the contents of gypsum and ettringite in different surface depth, solution concentration and corrosion time. Finally, the contents of gypsum and ettringite calculated by the models are compared with the results from the XRD experiments, and then the effects of surface depth, corrosion time and solution concentration on the gypsum and ettringite formations in cement pastes are discussed.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.69-78
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• 2023

With the global rise of digital data, the uncontrolled quantity of data is susceptible to cyber warfare or cyber attacks. Therefore, it is necessary to improve cyber security systems. This research studies the behavior of malicious acts and uses Higuchi Fractal Dimension (HFD), which is a non-linear mathematical method to examine the intricacy of the behavior of these malicious acts and anomalies within the cyber physical system. The HFD algorithm was tested successfully using synthetic time series network data and validated on real-time network data, producing accurate results. It was found that the highest fractal dimension value was computed from the DoS attack time series data. Furthermore, the difference in the HFD values between the DoS attack data and the normal traffic data was the highest. The malicious network data and the non-malicious network data were successfully classified using the Receiver Operating Characteristics (ROC) method in conjunction with a scaling stationary index that helps to boost the ROC technique in classifying normal and malicious traffic. Hence, the suggested methodology may be utilized to rapidly detect the existence of abnormalities in traffic with the aim of further using other methods of cyber-attack detection.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.59-66
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• 2009

A strong designated verifier signature scheme is a special type of signature scheme which provides signer anonymity by enabling the specified recipient, called a designated verifier, to simulate a signature which is indistinguishable from the signer's signature. It has many applications such as software distribution or electronic voting. In this paper, we consider two important security properties of strong designated verifier signature scheme - source hiding and security against key-compromise attack. We show that the two properties cannot be achieved at the same time. Finally, we present a new ID-based strong designated verifier signature scheme which is secure against key-compromise attack.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.911-914
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• 2008

It has been recognized for a long time that sulfate ions in seawater and soils can cause severe damage to concrete structures. There have been numerous field and lab. studies on the distress caused to concrete structures generated by sulfate attack. All these investigations emphasize that in order to understand the deterioration of concrete due to sulfate attack. Until now, however, it has been difficult to define the precise nature of the mechanism of sulfate attack because of its complex behavior. Thus, this work dealt with the deterioration mechanism caused by sulfate attack and the evaluation criteria.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4527-4547
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• 2018

Signcryption is a cryptographic primitive that provides authentication (signing) and confidentiality (encrypting) simultaneously at a lower computational cost and communication overhead. With the proposition of certificateless public key cryptography (CLPKC), certificateless signcryption (CLSC) scheme has gradually become a research hotspot and attracted extensive attentions. However, many of previous CLSC schemes are constructed based on time-consuming pairing operation, which is impractical for mobile devices with limited computation ability and battery capacity. Although researchers have proposed pairing-free CLSC schemes to solve the issue of efficiency, many of them are in fact still insecure. Therefore, the challenging problem is to keep the balance between efficiency and security in CLSC schemes. In this paper, several existing CLSC schemes are cryptanalyzed and a new CLSC scheme without pairing based on elliptic curve cryptosystem (ECC) is presented. The proposed CLSC scheme is provably secure against indistinguishability under adaptive chosen-ciphertext attack (IND-CCA2) and existential unforgeability under adaptive chosen-message attack (EUF-CMA) resting on Gap Diffie-Hellman (GDH) assumption and discrete logarithm problem in the random oracle model. Furthermore, the proposed scheme resists the ephemeral secret leakage (ESL) attack, public key replacement (PKR) attack, malicious but passive KGC (MPK) attack, and presents efficient computational overhead compared with the existing related CLSC schemes.

• Journal of Convergence Society for SMB
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• v.5 no.1
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• pp.25-30
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• 2015

The APT attacks are hackers created a variety of security threats will continue to attack applied to the network of a particular company or organization. It referred to as intelligent sustained attack. After securing your PC after a particular organization's internal staff access to internal server or database through the PC or remove and destroy the confidential information. The APT attack is so large, there are two zero-day attacks and rootkits. APT is a process of penetration attack, search, acquisition, and is divided into outlet Step 4. It was defined in two ways how you can respond to APT through the process. Technical descriptions were divided into ways to delay the attacker's malicious code attacks time and plan for attacks to be detected and removed through.

• Journal of Platform Technology
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• v.11 no.1
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• pp.72-84
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• 2023

With the rise of the Internet of Things, the security of such lightweight computing environments has become a hot topic. Lightweight block ciphers that can provide efficient performance and security by having a relatively simpler structure and smaller key and block sizes are drawing attention. Due to these characteristics, they can become a target for new attack techniques. One of the new cryptanalytic attacks that have been attracting interest is Neural cryptanalysis, which is a cryptanalytic technique based on neural networks. It showed interesting results with better results than the conventional cryptanalysis method without a great amount of time and cryptographic knowledge. The first work that showed good results was carried out by Aron Gohr in CRYPTO'19, the attack was conducted on the lightweight block cipher SPECK-/32/64 and showed better results than conventional differential cryptanalysis. In this paper, we first apply the Differential Neural Distinguisher proposed by Aron Gohr to the block ciphers HIGHT and GOST to test the applicability of the attack to ciphers with different structures. The performance of the Differential Neural Distinguisher is then analyzed by replacing the neural network attack model with five different models (Multi-Layer Perceptron, AlexNet, ResNext, SE-ResNet, SE-ResNext). We then propose a Related-key Neural Distinguisher and apply it to the SPECK-/32/64, HIGHT, and GOST block ciphers. The proposed Related-key Neural Distinguisher was constructed using the relationship between keys, and this made it possible to distinguish more rounds than the differential distinguisher.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1111-1123
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• 2023

In recent years, as generative models have developed, research that threatens them has also been actively conducted. We propose a new membership inference attack against text-to-image model. Existing membership inference attacks on Text-to-Image models produced a single image as captions of query images. On the other hand, this paper uses personalized embedding in query images through Textual Inversion. And we propose a membership inference attack that effectively generates multiple images as a method of generating Adversarial Prompt. In addition, the membership inference attack is tested for the first time on the Stable Diffusion model, which is attracting attention among the Text-to-Image models, and achieve an accuracy of up to 1.00.