Visible to the public "Honeypot based unauthorized data access detection in MapReduce systems"Conflict Detection Enabled

Title"Honeypot based unauthorized data access detection in MapReduce systems"
Publication TypeConference Paper
Year of Publication2015
AuthorsH. Ulusoy, M. Kantarcioglu, B. Thuraisingham, L. Khan
Conference Name2015 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date PublishedMay
ISBN Number978-1-4799-9889-0
Accession Number15311573
KeywordsBig Data, Big Data revolution, cloud computing, Computational modeling, cryptography, Data analysis, data analytics tasks, data encryption, Data models, data privacy, data processing capabilities, Distributed databases, general programming languages, homomorphic encryption methods, honeypot, MapReduce systems, on-demand scalability, parallel processing, privacy, pubcrawl, pubcrawl170105, unauthorized data access detection

The data processing capabilities of MapReduce systems pioneered with the on-demand scalability of cloud computing have enabled the Big Data revolution. However, the data controllers/owners worried about the privacy and accountability impact of storing their data in the cloud infrastructures as the existing cloud computing solutions provide very limited control on the underlying systems. The intuitive approach - encrypting data before uploading to the cloud - is not applicable to MapReduce computation as the data analytics tasks are ad-hoc defined in the MapReduce environment using general programming languages (e.g, Java) and homomorphic encryption methods that can scale to big data do not exist. In this paper, we address the challenges of determining and detecting unauthorized access to data stored in MapReduce based cloud environments. To this end, we introduce alarm raising honeypots distributed over the data that are not accessed by the authorized MapReduce jobs, but only by the attackers and/or unauthorized users. Our analysis shows that unauthorized data accesses can be detected with reasonable performance in MapReduce based cloud environments.

Citation Key7165951