Optimal Spot-Checking for Collusion Tolerance in Computer Grids

Many grid-computing systems adopt voting-based techniques to resist sabotage. However, these techniques become ineffective in grid systems subject to collusion behavior, where some malicious resources can collectively sabotage a job execution by returning identical wrong results. Spot-checking has b...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on dependable and secure computing Ročník 16; číslo 2; s. 301 - 312
Hlavní autoři: Levitin, Gregory, Xing, Liudong, Dai, Yuanshun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Washington IEEE 01.03.2019
IEEE Computer Society
Témata:
Collusion
Collusion tolerance
Computational grids
Computational modeling
computer grid
Computer networks
Computers
Cybersecurity
Iterative methods
Measurement
Optimization
Parameter uncertainty
Performance measurement
Resists
Resource management
Sabotage
spot-checking
System performance
ISSN:1545-5971, 1941-0018
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Many grid-computing systems adopt voting-based techniques to resist sabotage. However, these techniques become ineffective in grid systems subject to collusion behavior, where some malicious resources can collectively sabotage a job execution by returning identical wrong results. Spot-checking has been used to detect and tackle the collusive issue by sending randomly chosen resources a certain number of spotter jobs with known correct results to estimate resource credibility based on the returned result. This paper makes original contributions by formulating and solving a new spot-checking optimization problem for grid systems subject to collusion attacks, with the objective to minimize probability of the genuine task failure (PGTF, i.e., the wrong output probability) while meeting an expected overhead constraint. The problem solution contains an optimal combination of task distribution policy parameters, including the number of deployed spotter tasks, the number of resources tested by each spotter task, and the number of resources assigned to perform the genuine task. The optimization procedure encompasses a new iterative method for evaluating system performance metrics of PGTF and expected overhead in terms of the total number of task assignments. Both fixed and uncertain attack parameters are considered. Illustrative examples are provided to demonstrate the proposed optimization problem and solution methodology.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2017.2690293