Hadoop eco system for big data security and privacy

Adluru, Pradeep; Datla, Srikari Sindhoori; Zhang, Xiaowen

doi:10.1109/lisat.2015.7160211

Cited by 22 publications

(10 citation statements)

References 10 publications

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“…(This part develops the previous study presented in [9])According to the results obtained previously, vulnerabilities number remains high, and their exploitation, as described by the first 10 OWASP [13] aims to undermine the integrity or the availability of the information system with the possibility of viral contamination. The number of vulnerabilities progresses in an exponential way until the years 2006 and 2017.…”

Section: Fig 3: Big Data Processing Systems Vulnerabilities Over Thementioning

confidence: 76%

“…To carry out an assessment of a vulnerability and the capabilities of a group of threats, it will be necessary first to define the number of vulnerabilities detected over the years by Big Data management systems [9]. According to studies interested in data security, such as the annual report of cisco and cyber Security [12] , OWASP [13], and other studies work on the situation of Big Data, as well as to the security situation of systems allowing Big Data treatment, the annual Big Data guides as example, we have been able to make a graph showing the evolution of Big Data management systems vulnerabilities over the years, from 1999, until 2017 [9].…”

Section: Big Data Processing Systems Vulnerabilities Over Thementioning

confidence: 99%

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A Novel Security Architecture Based on Haystack System for HDFS Storage System: Extended Work

Kandrouch*,

Hmina,

Hmina

2020

IJITEE

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Over the past few years, the digitization of our different daily transactions and our activities have helped to create the new Big Data era. This phenomenon has become more widely used in different fields for many reasons, such as information retrieval, decision making, etc. But keeping this data safe from any attack attempts constitutes a real challenge. Indeed, Big Data security has become very important recently, since all technologies and large organizations have become dependent on these Data. For this reason, we deal with the Big Data security problems, especially those encountered during their management and treatment, and more precisely, those appeared during the processing with Hadoop system. The paper aims to highlight the Big Data system processing situation in terms of security, also it shed light on some existing security solutions used for overcoming these security issues, we will reveal its strengths and its weaknesses according to the security pillars. Subsequently, we will present our new security architecture, based on Haystack intrusion detection system, which serves to improve the inactive data security contained in HDFS, while analyzing its feasibility and its parameters

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Section: Fig 3: Big Data Processing Systems Vulnerabilities Over Thementioning

confidence: 76%

Section: Big Data Processing Systems Vulnerabilities Over Thementioning

confidence: 99%

A Novel Security Architecture Based on Haystack System for HDFS Storage System: Extended Work

Kandrouch*,

Hmina,

Hmina

2020

IJITEE

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“…To overcome these reasons, some advanced techniques and technologies are developed to monitor, protect and audit big data processes. Considering the related survey, this paper has classified privacy and security issues for big data under 5 titles as Lack of High secure Design, Hadoop security, cloud security, monitoring and auditing, key management and Anonymization [4]. …”

Section: Challenging Issues For Security In Big Datamentioning

confidence: 99%

Privacy and Security in Big Data Management

Rannels¹,

Falconieri²,

Phillips³

et al. 2018

IJRTER

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Abstract-Finding new ways to take advantage of big data, small or big organizations need high security techniques end efficient policies to guarantee their systems. In big data, it is very difficult to store data and analyse these data with conventional techniques. It is though that the traditional mechanisms are not efficient on big data security and privacy challenges. Different challenges include Hadoop security, cloud security and monitoring and auditing, etc. Big data deals with different data formats, data collection done from different sources, streaming of data may cause security problems the existing systems led to the violation of the individual privacy control. Data should be shared in a secure environment without violating people's privacy to overcome such complex issues, it is necessary to implement some set of rules and regulations in performing data sharing, authentication process, in generating personal information, and misusing of the system. With respect to these problems, proposed system introduced some mechanism challenging security and privacy issues for both user and application oriented.

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“…Each data node has only a portion of data and manages their portion of data, as per the instruction from the name node. Backup node updates its status, at prescribed checkpoints, to recover name node, in case of any failures . The advantage of using the framework is the increasing performance of data analysis with support for parallel processing, without the apprehension about managing the distributed environment process (which is internally managed by the framework) …”

Section: Introductionmentioning

confidence: 99%

Hadoop‐based analytic framework for cyber forensics

Chhabra

Singh

2018

Int J Communication

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With an exponential increase in the data size and complexity of various documents to be investigated, existing methods of network forensics are found not much efficient with respect to accuracy and detection ratio. The existing techniques for network forensic analysis exhibit inherent limitations while processing a huge volume, variety, and velocity of data. It makes network forensic a time-consuming and resource-consuming task. To balance time taken and output delivered, these existing techniques put a limit on the amount of data under analysis, which results in a polynomial time complexity of these solutions. So to mitigate these issues, in this paper, we propose an effective framework to overcome the limitation to handle large volume, variety, and velocity of data. An architectural setup that consists of MapReduce framework on top of Hadoop Distributed File System environment is proposed in this paper. The proposed framework demonstrates its capability to handle issues of storage and processing of big data using cloud computing. Also, in the proposed framework, supervised machine learning (random forest-based decision tree) algorithm has been implemented to demonstrate better sensitivity. To train and validate the model, online available data set from CAIDA is taken and university network traffic samples, with increasing size, has been taken for experiment. Results thus obtained confirm the superiority of the proposed framework in network forensics, with an average accuracy of 99.34% (malicious and nonmalicious traffic).

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Hadoop eco system for big data security and privacy

Cited by 22 publications

References 10 publications

A Novel Security Architecture Based on Haystack System for HDFS Storage System: Extended Work

A Novel Security Architecture Based on Haystack System for HDFS Storage System: Extended Work

Privacy and Security in Big Data Management

Hadoop‐based analytic framework for cyber forensics

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