Attack tolerant architecture for big data file systems

Abstract

Data driven decisions derived from big data have become critical in many application domains, fueling the demand for collection, transportation, storage and processing of massive volumes of data. Such applications have made data a valuable resource that needs to be provided appropriate security. High value associated with big data sets has rendered big data storage systems attractive targets for cyber attackers, whose goal is to compromise the Confidentiality, Integrity and Availability of data and information. Common defense strategy for protecting cyber assets has been to first take preventive measures, and if these fail, detecting intrusions and finally recovery. Unfortunately, attackers have developed tremendous technical sophistication to defeat most defensive mechanisms. Alternative strategy is to design architectures which are intrinsically attack tolerant. This paper describes a technique that involves eliminating single point of security failures through fragmentation, coding, dispersion and reassembly. It is shown that this technique can be successfully applied to routing, networked storage systems, and big data file systems to make them attack tolerant.

Big Data SAVE: Secure Anonymous Vault Environment

“…Reference [20] introduces the use of survivable routing and Redundant Array of Inexpensive Disks (RAID) for providing integrity and confidential Big Data storage in network file systems (when combined with broader schemes such as Hadoop). The authors argue that the common security tactics of preventative measures, attack detection, and then recovery are inadequate to secure such large cyber assets.…”

Securing unmanned autonomous systems from cyber threats

“…The simple redundancy used in SITAR, 75 MAFTIA 76 and SCIT 77 in fact increases the probability of confidentiality and integrity compromises. 80…”

“…The architecture proposed by Madan and Banik 80 is based on data Fragmentation, Coding, Dispersal and Reassembly (FCDR) of coded fragments across multiple, diverse and non-overlapping systems, and has been shown to be capable of surviving all such intrusions. In this architecture, each word (of word-length w = 8, 16, 32 or 64 bits) of a data block or a packet is fragmented into n fragments, such that each fragment is v -bits long, where v = w / n .…”

“…It therefore follows that a FCDR-based cyber system has the ability to survive up to n − 1 intrusions designed to compromise confidentiality and at least k successful intrusions designed to compromise integrity and availability. 80 The ability of the FCDR-based intrusion tolerant architecture to survive multiple intrusions that can compromise the confidentiality, integrity and availability of data stored in the file systems or communicated over networks makes it suitable for building intrusion tolerance capabilities in UAS infrastructures.…”

Enhancement Data Integrity Checking Using Combination MD5 and SHA1 Algorithm in Hadoop Architecture

“…Due to large sets of data transferred from one node to the other, data detection is needed to check if there have been modifications to the data being imperative to ensure data integrity. There are two ways in which data might be altered: firstly accidentally, through hardware and transmission errors, and secondly because of a cyber-attack [4]. The aim of the data integrity service is to detect whether data has been altered or not, however it does not consider data restoration to its original state.…”