https://scholar.dgist.ac.kr/handle/20.500.11750/46476 Sat, 04 Apr 2026 15:16:55 GMT 2026-04-04T15:16:55Z https://scholar.dgist.ac.kr/handle/20.500.11750/59056 Title: Fast polynomial inversion algorithms for the post-quantum cryptography Author(s): Seo, Eun-Young; Kim, Young-Sik; No, Jong-Seon Abstract: Several cryptosystems suggested for the post-quantum cryptography candidates, including Falcon, BIKE, and NTRU, are defined in a polynomial ring. They must derive the inverse polynomial of any given polynomial for generating a public key. This process consumes considerable processing time; therefore, reducing the time to derive the inverse polynomial significantly improves many cryptosystems’ performance. In this paper, we primarily suggest two polynomial inversion algorithms, combined-variable-time and combined-constant-time algorithms, based on the modification of the extended Euclidean algorithm. The combined-variable-time algorithm shows how to calculate the inverse polynomial by introducing the combined matrix fast, which is generated by merging several steps of the polynomial operations. In cryptosystems, to defend against side-channel attacks, the implementation with constant running time is essential in preventing information leakage. Thus, we propose the combined-constant-time polynomial inversion algorithm, which expends less running time than the conventional NTRU inversion algorithm. For binary polynomial inversion, the proposed combined-variable-time algorithm is 1.95 times faster than the variable-time algorithm used in the previous NTRU (Silverman Almost inverses and fast NTRU key creation, NTRU Tech Report, no. 014v1, Mar. 15, 1999), and the combined-constant-time algorithms are 1.43 times faster than the reference constant-time algorithms submitted to round 3 of the NIST PQC standardization, respectively. For ternary polynomial inversion, the proposed combined-variable-time and combined-constant-time algorithms are 1.59 and 1.29 times faster than the corresponding reference algorithms. © 2025 Elsevier B.V., All rights reserved. Thu, 31 Jul 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59056 2025-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57806 Title: Channel-Hopping Sequence and Searching Algorithm for Rendezvous of Spectrum Sensing Author(s): Choi, Young-June; Kim, Young-Sik; Jang, Ji-Woong Abstract: In this paper, we propose a method for applying the p-ary m-sequence as a channel-searching pattern for rendezvous in the asymmetric channel model of cognitive radio. We mathematically analyzed and calculated the ETTR when the m-sequence is applied to the conventional scheme, and our simulation results demonstrated that the ETTR performance is significantly better than that of the JS algorithm. Furthermore, we introduced a new channel-searching scheme that maximizes the benefits of the m-sequence and proposed a method to adapt the generation of the m-sequence for use in the newly proposed scheme. We also derived the ETTR mathematically for the new scheme with the m-sequence and showed through simulations that the performance of the new scheme with the m-sequence is superior to that of the conventional scheme with the m-sequence. Notably, when there is only one common channel, the new scheme with the m-sequence achieved approximately four times the improvement in the ETTR compared to the conventional scheme. © 2024 by the authors. Tue, 31 Dec 2024 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/57806 2024-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57678 Title: Lazy Modular Reduction for NTT Author(s): Kim, Geumtae; Seo, Eun-Young; Lee, Yongwoo; Kim, Young-Sik; No, Jong-Seon Abstract: The number theoretic transform (NTT) is a fundamental operation in cryptography, especially for lattice-based cryptographic schemes. This paper introduces LazyNTT, a novel method that reduces the number of Montgomery multiplications required in the NTT computation by replacing some of them with standard multiplication without modular reduction. This approach enhances the performance of the NTT computation and modular polynomial multiplication in lattice-based cryptographic schemes. The proposed LazyNTT can be generalized by increasing the number of standard multiplications. The experimental results show that the proposed LazyNTT improves the cycle counts of the NTT by up to (Formula presented.) and (Formula presented.), respectively, by allowing two and one standard multiplications. © 2024 by the authors. Sat, 30 Nov 2024 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/57678 2024-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/47699 Title: A Deep Learning-Based IDS for Automotive Theft Detection for In-Vehicle CAN Bus Author(s): Khan, Junaid Ahmad; Lim, Dae-Woon; Kim, Young-Sik Abstract: Driver behavior features extracted from the controller area network (CAN) have potential applications in improving vehicle safety. However, the development of a classifier-based intrusion detection system (IDS) for in-vehicle networks remains an open research problem. To address this challenge, we incorporate novel n -fold cross-validation windowing techniques on two publicly available driving behavior datasets. A driver classification-based IDS is proposed using the LSTM-FCN model that utilizes the strengths of both fully convolutional network (FCN) and long short-term memory (LSTM) networks. These modules allow the model to learn spatial and temporal features and utilize contextual information. In addition, we combine three squeeze and excite (SnE) layers following FCN layers to incorporate adjacent spatial locations and augment a scaled dot product attention mechanism into the LSTM to improve its feature selection and extraction capabilities. Our proposed IDS uses hacking and countermeasure research lab (HCRL) and test datasets, which achieve an improvement in accuracy of 4.18% and 13.99% respectively, from the baseline LSTM-FCN model. The experimental results of our method exhibited an overall accuracy of 99.36% and 96.36% for both datasets and outperformed various state-of-the-art methods. © 2023 The Authors. Sat, 30 Sep 2023 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/47699 2023-09-30T15:00:00Z