Cite this DOI

Chandrika, D., & Sreedhar Reddy, D. M. (2017). Protecting Virtualized Infrastructures in Cloud Computing Based On Big Data Security Analytics. *Journal of Science & Technology*, *02*(05), 42–46. https://doi.org/10.46243/jstj.2017.v2.i5.63

@article{chandrika2017protecting,
  author    = {Chandrika, Deshpande and Sreedhar Reddy, Dr. M.},
  title     = {{Protecting Virtualized Infrastructures in Cloud Computing Based On Big Data Security Analytics}},
  journal   = {Journal of Science \& Technology},
  year      = {2017},
  month     = {sep},
  volume    = {02},
  number    = {05},
  pages     = {42--46},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jstj.2017.v2.i5.63},
  url       = {https://doi.org/10.46243/jstj.2017.v2.i5.63},
  language  = {en},
  abstract  = {Virtualized infrastructure in cloud computing has become an attractive target for cyber attackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and MapReduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namley logistic regression is applied to calculate attack’s conditional probabilities with respect to the attributes, andbelief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead.}
}

TY  - JOUR
TI  - Protecting Virtualized Infrastructures in Cloud Computing Based On Big Data Security Analytics
AU  - Chandrika, Deshpande
AU  - Sreedhar Reddy, Dr. M.
JO  - Journal of Science & Technology
PY  - 2017
DA  - 2017/09/13/
VL  - 02
IS  - 05
SP  - 42
EP  - 46
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Virtualized infrastructure in cloud computing has become an attractive target for cyber attackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and MapReduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namley logistic regression is applied to calculate attack’s conditional probabilities with respect to the attributes, andbelief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead.
DO  - 10.46243/jstj.2017.v2.i5.63
UR  - https://doi.org/10.46243/jstj.2017.v2.i5.63
ER  -

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    "DOI": "10.46243/jstj.2017.v2.i5.63",
    "URL": "https://doi.org/10.46243/jstj.2017.v2.i5.63",
    "title": "Protecting Virtualized Infrastructures in Cloud Computing Based On Big Data Security Analytics",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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            "family": "Chandrika",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Virtualized infrastructure in cloud computing has become an attractive target for cyber attackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and MapReduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namley logistic regression is applied to calculate attack’s conditional probabilities with respect to the attributes, andbelief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjstj.2017.v2.i5.63 gives all four in one JSON answer.