Research on Hadoop Identity Authentication Based on Improved Kerberos Protocol

Hu, Daming; Chen, Deyun; Zhang, Yuanxu; Pei, Shujun

doi:10.14257/ijsia.2015.9.11.39

Cited by 5 publications

(2 citation statements)

References 2 publications

(1 reference statement)

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“…Hadoop currently supports security mechanisms to mitigate these risks, including the use of Kerberos, the adoption of firewalls, and the implementation of basic HDFS permissions [41]. However, Kerberos is not a mandatory requirement for a Hadoop cluster, which makes it possible to run entire groups of data without implementing any security.…”

Section: -Vulnerabilities Of Big Datamentioning

confidence: 99%

Big Data: Concept, Potentialities and Vulnerabilities

Almeida

2018

ESJ

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The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.

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Section: -Vulnerabilities Of Big Datamentioning

confidence: 99%

Big Data: Concept, Potentialities and Vulnerabilities

Almeida

2018

ESJ

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“…The rapid development of the network has brought great conveniences to people's study, life, work, and entertainment, 1 but at the same time, the security problem of the network has become increasingly prominent 2 ; therefore, technologies such as data encryption, 3 identity authentication, 4 intrusion detection, 5 and virus defense 6 have been extensively studied to realize network security better. Network virus refers to a group of codes that can destroy the function or data of computers and also has the ability of self-replication, that is, it can spread quickly among the networks to affect the normal use of computers.…”

Section: Introductionmentioning

confidence: 99%

Detection and defense of network virus using data mining technology

Zhi-jun

Jiang

2021

Security and Privacy

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The spread of network viruses has posed a serious threat to the security of the network; therefore, it is necessary to detect and defend them effectively. This paper used debugging application programming interface (API) technology to obtain the features of API calls as viruses, filtered API calls according to information entropy, and finally used the support vector machine (SVM) model for virus detection. The experimental results showed that when the number of API was 1200, the algorithm had the best virus detection performance, with an average true positive rate (TPR) of 95.2%, a false positive rate (FPR) of 3.31%, and an overall accuracy of 95.42%; compared with the C4.5 algorithm, the K-means algorithm, and the Naive Bayes algorithm, the SVM algorithm had the best performance. The results show that the proposed method is effective in virus detection and defense and can be further promoted and applied in practice.

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