생체의 모델링과 Simulation

Covert channels are used for the secret transfer of information. Encryption only protects communication from being decoded by unauthorised parties, whereas covert channels aim to hide the very existence of the communication. Initially, covert channels were identified as a security threat on monolithic systems i.e. mainframes. More recently focus has shifted towards covert channels in computer network protocols. The huge amount of data and vast number of different protocols in the Internet seems ideal as a high-bandwidth vehicle for covert communication. This article is a survey of the existing techniques for creating covert channels in widely deployed network and application protocols. We also give an overview of common methods for their detection, elimination, and capacity limitation, required to improve security in future computer networks.

https://researchbank.swinburne.edu.au/file/5de97ea6-fcdf-43e5-98ae-0567cdd495f6/1/PDF (Published version).pdf

A survey of covert channels and countermeasures in computer network protocols

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

Cat swarm optimization

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

https://journals.sagepub.com/doi/pdf/10.1155/2015/431047

Data mining for the Internet of Things: literature review and challenges

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

The rapid growth of digital multimedia and Internet technologies has made copyright protection, copy protection, and integrity verification three important issues in the digital world. To solve these problems, the digital watermarking technique has been presented and widely researched. Traditional watermarking algorithms are mostly based on discrete transform domains, such as the discrete cosine transform, discrete Fourier transform (DFT), and discrete wavelet transform (DWT). Most of these algorithms are good for only one purpose. Recently, some multipurpose digital watermarking methods have been presented, which can achieve the goal of content authentication and copyright protection simultaneously. However, they are based on DWT or DFT. Lately, several robust watermarking schemes based on vector quantization (VQ) have been presented, but they can only be used for copyright protection. In this paper, we present a novel multipurpose digital image watermarking method based on the multistage vector quantizer structure, which can be applied to image authentication and copyright protection. In the proposed method, the semi-fragile watermark and the robust watermark are embedded in different VQ stages using different techniques, and both of them can be extracted without the original image. Simulation results demonstrate the effectiveness of our algorithm in terms of robustness and fragility.

Multipurpose image watermarking algorithm based on multistage vector quantization

A digital image watermarking technique based on vector quantisation (VQ) is presented. This technique uses codeword indices to carry the watermark information. The technique is secret and efficient, and the watermarked image is robust to VQ compression with the same codebook. The simulation results prove the effectiveness of this technique.

Digital image watermarking technique based on vector quantisation

In this paper, a new and fast encoding algorithm for vector quantization is presented. This algorithm makes full use of two characteristics of a vector: the sum and the variance. A vector is separated into two subvectors: one is composed of the first half of vector components and the other consists of the remaining vector components. Three inequalities based on the sums and variances of a vector and its two subvectors components are introduced to reject those codewords that are impossible to be the nearest codeword, thereby saving a great deal of computational time, while introducing no extra distortion compared to the conventional full search algorithm. The simulation results show that the proposed algorithm is faster than the equal-average nearest neighbor search (ENNS), the improved ENNS, the equal-average equal-variance nearest neighbor search (EENNS) and the improved EENNS algorithms. Comparing with the improved EENNS algorithm, the proposed algorithm reduces the computational time and the number of distortion calculations by 2.4% to 6% and 20.5% to 26.8%, respectively. The average improvements of the computational time and the number of distortion calculations are 4% and 24.6% for the codebook sizes of 128 to 1024, respectively.

An efficient encoding algorithm for vector quantization based on subvector technique

A vector-quantisation (VQ)-based watermarking method is presented which utilises the codebook expansion technique. This method is efficient, provides enhanced security and the watermarked image is robust against the effects of VQ compression. Moreover, the watermark extraction can be performed without the original image. Experimental results are presented which demonstrate the effectiveness of this algorithm.

VQ-based digital image watermarking method

https://ir.nctu.edu.tw:443/bitstream/11536/29511/1/000169493900013.pdf

Sheng-He Sun

Papers

Multipurpose image watermarking algorithm based on multistage vector quantization

Digital image watermarking technique based on vector quantisation

An efficient encoding algorithm for vector quantization based on subvector technique

VQ-based digital image watermarking method

Vector quantization based on genetic simulated annealing