Security Test by using F T M and Data Allocation Strategies on Leakage Detection

2005 ◽  
Vol 4 (2) ◽  
pp. 393-400
Author(s):  
Pallavali Radha ◽  
G. Sireesha
Keyword(s):  
Third Party ◽  
Distributed Data ◽  
Data Allocation ◽  
Security Testing ◽  
Sensitive Data ◽  
Sample Data ◽  
The One ◽  
Security Test ◽  
Data Request ◽  
Allocation Strategies

The data distributors work is to give sensitive data to a set of presumably trusted third party agents.The data i.e., sent to these third parties are available on the unauthorized places like web and or some ones systems, due to data leakage. The distributor must know the way the data was leaked from one or more agents instead of as opposed to having been independently gathered by other means. Our new proposal on data allocation strategies will improve the probability of identifying leakages along with Security attacks typically result from unintended behaviors or invalid inputs.  Due to too many invalid inputs in the real world programs is labor intensive about security testing.The most desirable thing is to automate or partially automate security-testing process. In this paper we represented Predicate/ Transition nets approach for security tests automated generationby using formal threat models to detect the agents using allocation strategies without modifying the original data.The guilty agent is the one who leaks the distributed data. To detect guilty agents more effectively the idea is to distribute the data intelligently to agents based on sample data request and explicit data request. The fake object implementation algorithms will improve the distributor chance of detecting guilty agents.

scholarly journals Symbolic Gray Code As A Data Allocation Scheme For Two-disc Systems

1992 ◽  
Vol 35 (3) ◽  
pp. 299-305 ◽  
Author(s):  
C. C. Chang ◽  
H. Y. Chen ◽  
C. Y. Chen
Keyword(s):  
Gray Code ◽  
Data Allocation ◽  
Allocation Scheme

scholarly journals Data Allocation Service ADAS for the Data Rebalancing of ATLAS

2019 ◽  
Vol 214 ◽  
pp. 06012
Author(s):  
Ralf Vamosi ◽  
Mario Lassnig ◽  
Erich Schikuta
Keyword(s):  
Scientific Workflow ◽  
Data Management System ◽  
Atlas Collaboration ◽  
Distributed Data ◽  
Data Allocation ◽  
Data Services ◽  
Learning Agent ◽  
And Storage ◽  
Proper Operation ◽  
Global Datasets

The distributed data management system Rucio manages all data of the ATLAS collaboration across the grid. Automation, such as data replication and data rebalancing are important to ensure proper operation and execution of the scientific workflow. In this proceedings, a new data allocation grid service based on machine learning is proposed. This learning agent takes subsets of the global datasets and proposes a better allocation based on the imposed cost metric, such as waiting time in the workflow. As a service, it can be modularized and can run independently of the existing rebalancing and replication mechanisms. Furthermore, it collects data from other services and learns better allocation while running in the background. Apart from the user selecting datasets, other data services may consult this meta-heuristic service for improved data placement. Network and storage utilization is also taken into account.

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