國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Information similarity metrics in in...

Quach, Tu-Thach.

Information similarity metrics in information security and forensics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Information similarity metrics in information security and forensics.
作者:	Quach, Tu-Thach.
面頁冊數:	116 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-02, Section: B, page: 1200.
附註:	Adviser: Gregory L. Heileman.
Contained By:	Dissertation Abstracts International71-02B.
標題:	Engineering, Computer.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3390826
ISBN:	9781109611717

Information similarity metrics in information security and forensics.
Quach, Tu-Thach.

Information similarity metrics in information security and forensics. - 116 p.

Source: Dissertation Abstracts International, Volume: 71-02, Section: B, page: 1200.

Thesis (Ph.D.)--The University of New Mexico, 2009.

We study two information similarity measures, relative entropy and the similarity metric, and methods for estimating them. Relative entropy can be readily estimated with existing algorithms based on compression. The similarity metric, based on algorithmic complexity, proves to be more difficult to estimate due to the fact that algorithmic complexity itself is not computable. We again turn to compression for estimating the similarity metric. Previous studies rely on the compression ratio as an indicator for choosing compressors to estimate the similarity metric. This assumption, however, is fundamentally flawed. We propose a new method to benchmark compressors for estimating the similarity metric. To demonstrate its use, we propose to quantify the security of a stegosystem using the similarity metric. Unlike other measures of steganographic security, the similarity metric is not only a true distance metric, but it is also universal in the sense that it is asymptotically minimal among all computable metrics between two objects. Therefore, it accounts for all similarities between two objects. In contrast, relative entropy, a widely accepted steganographic security definition, only takes into consideration the statistical similarity between two random variables. As an application, we present a general method for benchmarking stegosystems. The method is general in the sense that it is not restricted to any covertext medium and therefore, can be applied to a wide range of stegosystems. For demonstration, we analyze several image stegosystems using the newly proposed similarity metric as the security metric. The results show the true security limits of stegosystems regardless of the chosen security metric or the existence of steganalysis detectors. In other words, this makes it possible to show that a stegosystem with a large similarity metric is inherently insecure, even if it has not yet been broken.

ISBN: 9781109611717Subjects--Topical Terms:

384375
Engineering, Computer.

Information similarity metrics in information security and forensics.
LDR:02992nmm 2200313 4500 001 280775
005 20110119094951.5
008 110301s2009 ||||||||||||||||| ||eng d
020 $a 9781109611717
035 $a (UMI)AAI3390826
035 $a AAI3390826
040 $a UMI $c UMI
100 1 $a Quach, Tu-Thach. $3 492876
245 1 0 $a Information similarity metrics in information security and forensics.
300 $a 116 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-02, Section: B, page: 1200.
500 $a Adviser: Gregory L. Heileman.
502 $a Thesis (Ph.D.)--The University of New Mexico, 2009.
520 $a We study two information similarity measures, relative entropy and the similarity metric, and methods for estimating them. Relative entropy can be readily estimated with existing algorithms based on compression. The similarity metric, based on algorithmic complexity, proves to be more difficult to estimate due to the fact that algorithmic complexity itself is not computable. We again turn to compression for estimating the similarity metric. Previous studies rely on the compression ratio as an indicator for choosing compressors to estimate the similarity metric. This assumption, however, is fundamentally flawed. We propose a new method to benchmark compressors for estimating the similarity metric. To demonstrate its use, we propose to quantify the security of a stegosystem using the similarity metric. Unlike other measures of steganographic security, the similarity metric is not only a true distance metric, but it is also universal in the sense that it is asymptotically minimal among all computable metrics between two objects. Therefore, it accounts for all similarities between two objects. In contrast, relative entropy, a widely accepted steganographic security definition, only takes into consideration the statistical similarity between two random variables. As an application, we present a general method for benchmarking stegosystems. The method is general in the sense that it is not restricted to any covertext medium and therefore, can be applied to a wide range of stegosystems. For demonstration, we analyze several image stegosystems using the newly proposed similarity metric as the security metric. The results show the true security limits of stegosystems regardless of the chosen security metric or the existence of steganalysis detectors. In other words, this makes it possible to show that a stegosystem with a large similarity metric is inherently insecure, even if it has not yet been broken.
590 $a School code: 0142.
650 4 $a Engineering, Computer. $3 384375
650 4 $a Engineering, Electronics and Electrical. $3 226981
690 $a 0464
690 $a 0544
710 2 $a The University of New Mexico. $b Computer Engineering. $3 492877
773 0 $t Dissertation Abstracts International $g 71-02B.
790 1 0 $a Heileman, Gregory L., $e advisor
790 1 0 $a Perez-Gonzalez, Fernando $e committee member
790 1 0 $a Abdallah, Chaouki T. $e committee member
790 1 0 $a Pattichis, Marios S. $e committee member
790 $a 0142
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3390826