Yong-Su Park

When one-time signatures are used for stream authentication, one of the most serious drawbacks is that their large signature size yields high communication overhead. In this paper, we present two efficient one-time signature schemes for stream authentication. Compared with the previous schemes, these schemes have the smallest signature sizes. Moreover, their verification overheads are low. The signature size of Scheme 1 is smaller than that of Scheme 2 whereas Scheme 2 has much smaller signing c

It is well known that conventional digital signature algorithms such as RSA and ECDSA are vulnerable to quantum computing attacks. Hash-based signature schemes are attractive as post-quantum signature schemes in that it is possible to calculate the quantitative security level and the security is proven. SPHINCS is a stateless hash-based signature scheme and introduces HORST few-time signature scheme which is an improvement of HORS. However, HORST as well as HORS suffers from pretty large signatu

Time-release cryptography is a special encryption technique that allows a message to be hidden for some time. The previous schemes have shortcomings in that the encryptor should predict the decryptor’s computing power precisely or the trusted agent should be always available. In this paper, we propose a new, practical time-release blockchain, and find the key to decrypt the content after a certain time. In order to verify the effectiveness of the blockchain system automatically, which uses the p

Software uses cryptography to provide confidentiality in communication and to provide authentication. Additionally, cryptographic algorithms can be used to protect software against cracking core algorithms in software implementation. Recently, malware and ransomware have begun to use encryption to protect their codes from analysis. As for the detection of cryptographic algorithms, previous works have had demerits in analyzing anti-reverse engineered binaries that can detect differences in analys

With the increasing use of sophisticated obfuscation techniques, malware detection remains a critical challenge in cybersecurity. This paper introduces a novel deep learning approach to classify malware obfuscated by virtual machine (VM) code. We specifically explore the application of depth-wise convolutional neural networks (CNNs) combined with a spatial attention mechanism to tackle VM-protected cybersecurity datasets. To address the scarcity of obfuscated malware samples, the dataset was gen

This study was conducted to obtain the basic research about Siberian roe deer (Capreolus pygargus), and to establish a conservation area into Seoraksan(Mt.) National Park, South Korea. We recorded Siberian roe deer field signs (bedding sites, feeding areas, feces and tracks etc), and habitat variables such as nearest distance to the watercourse, trails, slope, aspect, forest type and land cover etc. from Jan. 2006 to Dec. 2007 in Seoraksan(Mt.) National Park. According to the research of habitat

Malware and ransomware are often encrypted to protect their own code, making it challenging to apply reverse engineering to analyze them. Recently, various studies have been underway to identify cryptography algorithms in malware or ransomware that use anti-reversing technology via deep-learning technology. In particular, CNNs (convolution neural networks) are deep-learning algorithms with superior performance, as compared to existing machine-learning algorithms in image classification. In the c

A distributed trigger counting (DTC) problem is to detect w triggers in the distributed system consisting of n nodes. DTC algorithms can be used for monitoring systems using sensors to detect a significant global change. When designing an efficient DTC algorithm, the following goals should be considered; minimizing the whole number of exchanged messages used for counting triggers and even distribution of communication loads among nodes. In this paper, we present an efficient DTC algorithm, DDR-c