Browsing by Author "Chang, Jen-Chun"

Now showing 1 - 4 of 4

An Efficient Fragile Watermarking Scheme for Pixel-wise Tamper Detection
(2012)
Lee, Yang-Kuo
;
Chang, Jen-Chun
;
Wu, Hsin-Lung
;
Chen, Rong-Jaye
;
資訊工程學系
In this paper we propose a novel pixel-wise fragile watermarking scheme which is able to detect tampered pixels with very high probability. Based on a new technique called Image-Dependent Seed (IDS), the proposed method is not only efficient but also secure against many known attacks. Furthermore, the proposed scheme can be simply implemented with only bit-wise AND and Exclusive-OR operations.
Distance-preserving and distance-increasing mappings from ternary vectors to permutations
(2008-03-01)
Lin, Jyh-Shyan
;
Chang, Jen-Chun
;
Chen, Rong-Jaye
;
Klove, Torleiv
;
資訊工程學系
Permutation arrays have found applications in powerline communication. One construction method for permutation arrays is to map good codes to permutations using a distance-preserving mappings (DPM). DPMs are mappings from the set of all q-ary vectors of a fixed length to the set of permutations of some fixed length (the same or longer) such that every two distinct vectors are mapped to permutations with the same or larger Hamming distance than that of the vectors. A DIM is called distance increasing (DIM) if the distances are strictly increased (except when the two vectors are equal). In this correspondence, we propose constructions of DPMs and DIMs from ternary vectors. The constructed DPMs and DIMs improve many lower bounds on the maximal size of permutation arrays.
Feature-Selection-Based Ransomware Detection with Machine Learning of Data Analysis
(2018-01-01)
Wan, Yu-Lun
;
Chang, Jen-Chun
;
Chen, Rong-Jaye
;
Wang, Shiuh-Jeng
;
資訊工程學系
Ransomwares are continuously produced in underground markets such that increasingly high-level and sophisticated ransomwares are spreading all over the world, significantly affecting individuals, businesses, governments, and countries. To prevent large-scale attacks, most companies buy intrusion detection systems to alert regarding any abnormal network behavior. However, they cannot be detected using conventional signature-based detection even though ransomwares belong to the same family. In this study, a method is provided to develop a network intrusion detection model that is based on big data technology. The system uses Argus for packet preprocessing, merging, and labeling the known malicious data. A concept of Biflow was proposed to replace the packet data. Further, we observe that the data size is reduced to 1000:1. Additionally, the characteristics of a complete traffic are obtained. Six feature selection algorithms were combined to achieve a better accuracy in terms of classification. Finally, the decision tree model of the supervised machine learning was used to enhance the performance of intrusion detection system.
New simple constructions of distance-increasing mappings from binary vectors to permutations
(2006-10-31)
Lin, Jyh-Shyan
;
Chang, Jen-Chun
;
Chen, Rong-Jaye
;
資訊工程學系
;
Department of Computer Science
Distance-increasing mappings (DIMs) are mappings from the set of binary vectors of a fixed length to the set of permutations of the same length that increase Hamming distances except when that is obviously not possible. In this paper, we propose new non-recursive constructions of DIMs which are based on simple compositions of permutations. In comparison with Chang's constructions, our new constructions do not need any table-lookup operations, and usually have better distance expansion distributions when the length is odd. (c) 2006 Elsevier B.V. All rights reserved.