@inproceedings{699d0721631546a0a8fc5f424349b72a,
title = "Using MapReduce and hierarchical entropy analysis to speed-up the detection of covert timing channels",
abstract = "Covert timing channels provide a mechanism to transmit unauthorized information across different processes. Applications that generate large datasets allow this information to be easily hidden within the big data, making it difficult to detect. In this paper, we introduce the application of big data analysis techniques, specifically MapReduce, in the process of speeding up the performance of covert time channels detection. The hierarchal entropy algorithm (HEA) is utilized to reveal a 'needle' of covert timing channels from a huge 'haystack' of inter-arrival times. A real indexed inter-arrival dataset of approximately 1.4 gigabyte is generated between two different machines and injected by 615 bytes of covert timing message. The HEA with MapReduce was able to uncover around 7∗10-6 of hidden covert message from this huge amount of data in a significantly shorter time as compared to the classical sequential HEA.",
keywords = "Big data, Covert timing channels, Hierarchical entropy, MapReduce, Security",
author = "Omar Darwish and Ala Al-Fuqaha and {Ben Brahim}, Ghassen and Javed, {Muhamad Awais}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017 ; Conference date: 26-06-2017 Through 30-06-2017",
year = "2017",
month = jul,
day = "19",
doi = "10.1109/IWCMC.2017.7986439",
language = "English",
series = "2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1102--1107",
booktitle = "2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017",
address = "United States",
}