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 到查詢結果 [ subject:"Psychology, Behavioral." ]

切換: 標籤 | MARC模式 | ISBD

Using power-law properties of social...

Louisiana Tech University.

Using power-law properties of social groups for cloud defense and community detection.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using power-law properties of social groups for cloud defense and community detection.
作者:	Rice, Justin L.
面頁冊數:	99 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
附註:	Adviser: Vir V. Phoha.
Contained By:	Dissertation Abstracts International74-09B(E).
標題:	Computer Science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3570079
ISBN:	9781303095504

Using power-law properties of social groups for cloud defense and community detection.
Rice, Justin L.

Using power-law properties of social groups for cloud defense and community detection. - 99 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--Louisiana Tech University, 2013.

The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Levy walk best describes their self-organizing movement strategy. A mussel's step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection.

ISBN: 9781303095504Subjects--Topical Terms:

212513
Computer Science.

Using power-law properties of social groups for cloud defense and community detection.
LDR:03591nmm a2200313 4500 001 419290
005 20140520124010.5
008 140717s2013 ||||||||||||||||| ||eng d
020 $a 9781303095504
035 $a (MiAaPQ)AAI3570079
035 $a AAI3570079
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rice, Justin L. $3 660329
245 1 0 $a Using power-law properties of social groups for cloud defense and community detection.
300 $a 99 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Vir V. Phoha.
502 $a Thesis (Ph.D.)--Louisiana Tech University, 2013.
520 $a The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Levy walk best describes their self-organizing movement strategy. A mussel's step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection.
520 $a Privacy and security are two areas of concern for cloud systems. Recent research has provided evidence indicating how a malicious user could perform co-residence profiling and public to private IP mapping to target and exploit customers which share physical resources. This work proposes a defense strategy, in part inspired by mussel self-organization, that relies on user account and workload clustering to mitigate co-residence profiling. To obfuscate the public to private IP map, clusters are managed and accessed by account proxies. This work also describes a set of capabilities and attack paths an attacker needs to execute for targeted co-residence, and presents arguments to show how the defense strategy disrupts the critical steps in the attack path for most cases. Further, it performs a risk assessment to determine the likelihood an individual user will be victimized, given that a successful non-directed exploit has occurred. Results suggest that while possible, this event is highly unlikely.
520 $a As for community detection, several algorithms have been proposed. Most of these, however, share similar disadvantages. Some algorithms require apriori information, such as threshold values or the desired number of communities, while others are computationally expensive. A third category of algorithms suffer from a combination of the two. This work proposes a greedy community detection heuristic which exploits the scale-free properties of social networks. It hypothesizes that highly connected nodes, or hubs, form the basic building blocks of communities. A detection technique that explores these characteristics remains largely unexplored throughout recent literature. To show its effectiveness, the algorithm is tested on commonly used real network data sets. In most cases, it classifies nodes into communities which coincide with their respective known structures. Unlike other implementations, the proposed heuristic is computationally inexpensive, deterministic, and does not require apriori information.
590 $a School code: 0109.
650 4 $a Computer Science. $3 212513
650 4 $a Engineering, Computer. $3 384375
650 4 $a Psychology, Behavioral. $3 212473
690 $a 0984
690 $a 0464
690 $a 0384
710 2 $a Louisiana Tech University. $b Computer Science and Engineering. $3 660330
773 0 $t Dissertation Abstracts International $g 74-09B(E).
790 $a 0109
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3570079

筆 0 讀者評論

多媒體

多媒體檔案
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3570079

評論

新增評論分享你的心得

Export

取書館別