Real-time Detection of Anomalies on Performance Data of Container Virtualization Platforms
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Published
Sep 8, 2021
Venkata daya sagar Ketaraju
Abstract
Application virtualization platforms are virtualization Technologies that allow applications to run
independently. It is observed that applications running on application virtualization platforms may have abnormal
working conditions from time to time. However, such situations can be caught by system administrators examining the
application log files in detail. This causes abnormal operating conditions to be captured long after they occur. Within the
scope of this research, a method that allows to detect abnormal running conditions of applications running on
application virtualization platforms in real time is proposed. The proposed method uses both unsupervised learning and
supervised learning algorithms together. A prototype application was developed to demonstrate the usability of the
proposed method. In order to demonstrate the success of the method, the tests we performed on the prototype yielded
high accuracy in a real-time detection of abnormal operating conditions
How to Cite
Ketaraju, V. daya sagar. (2021). Real-time Detection of Anomalies on Performance Data of Container Virtualization Platforms. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/204
Article Details
Keywords
Detection of Abnormal Working Conditions, Principal Component Analysis, Logistic Regression, Application Virtualization Platforms, Container-based Virtualization, Real- time Abnormal Working Condition Detection, Preventive Maintenance
References
Chandola, V. et al. (2009) "Anomaly detection: A survey". ACM computing surveys (CSUR), 2009, 41.3: 15.
[2] Thottan, M. et al. (2003) "Anomaly detection in IP networks". IEEE Transactions on signal processing, 2003,
51.8: 2191-2204.
[3] Kruegel, C. Et al. "Anomaly detection of web-based attacks." In: Proceedings of the 10th ACM conference on
Computer and communications security. ACM, 2003. p. 251-261.
[4] Leung, K., et al., (2005) "Unsupervised anomaly detection in network intrusion detection using clusters". In:
Proceedings of the Twenty-eighth Australasian conference on Computer Science-Volume 38. Australian
Computer Society, Inc., 2005. p. 333-342.
[5] Mahadevan, V. et al. (2010) Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition. IEEE, 2010. p. 1975-1981.
[6] Hauskrecht, M. et al. (2007) Evidence-based anomaly detection in clinical domains. In: AMIA Annual
Symposium Proceedings. American Medical Informatics Association, 2007. p. 319.
[7] Lakhina, A. et al. (2004) Diagnosing network-wide traffic anomalies. In:
ACSIGCOMM computer communication review. ACM, 2004. p. 219-230
[8] Xu, W., et al. (2009) Detecting large-scale system problems by mining console logs. In: Proceedings of the
ACM SIGOPS 22nd symposium on Operating systems principles. ACM, 2009. p. 117-132.
[9] Solaimani, M. et al. (2014) Spark-based anomaly detection over multisource VMware performance data in realtime. In: 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS). IEEE, 2014. p. 1-
8.
[10] Ahmed, M. et al. (2016) A survey of network anomaly detection techniques. Journal of Network and
Computer Applications, 2016, 60: 19-31
[11] Ye, N. et al. (2000) "A markov chain model of temporal behavior for anomaly detection". In: Proceedings of
the 2000 IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop. West Point,
NY, 2000. p. 169.
[12] Gao, C. (2009) "Host anomalies detection using logistic regression modeling". In: 2009 First International
Workshop on Education Technology and Computer Science. IEEE, 2009. p. 655-659.
[13] Steinward, I., et al. (2005) "A classification framework for anomaly detection". Journal of Machine Learning
Research, 2005, 6.Feb: 211-232.
[14] Erboy, M.O. et al., (2020) Available at the link: https://drive.google.com/-
drive/folders/1x1t3bUJZ7Tvl7rnqqD1ojKnbSuoKnGH?usp=sharing
[15] Baeth, M.J. et al. (2019). Detecting misinformation in social networks using provenance data, CONCURR
COMP-PRACT E. Journal ISSN: 1532- 0626, Vol: 31, Issue:3.
[16] Baeth M. J. et al. (2018) "An approach to custom privacy policy violation detection
problems using big social provenance data", CONCURR COMP-PRCT E. Journal ISSN: 1532-0626,
Vol.:30, Issue: 21.
[17] Baeth, M.J. et al. (2017). "Detecting misinformation in social networks using provenance data", SKG'17.
[18] Baeth, M.J. et al. (2015). On the Detection of Information Pollution and Violation of Copyrights in the Social
Web, SOCA'15.
[19] Dundar, B. et al. (2016) A Big Data Processing Framework for Self Healing Internet of Things Applications,
SKG'16.
[20] K. V. Daya Sagar , P Sai Durga , G. Kavya , K Sri Sravya , K. Krishna Veni,"Mobile based home mechanisation framework using
IoT for smart cities",International Journal of Engineering & Technology, 7 (2.7) (2018) 266-269.
[21] K Sai Prasanthi , K.V.Daya Sagar ,"Survey on secure protocols for data sharing through edge of cloud assisted internet of
things",International Journal of Engineering & Technology, 7 (2.7) (2018) 92-95.
[22] K. V. Daya Sagar, U. Abbulu,K. Chaitanya Kumar Reddy,"Using Fuzzy Clustering Techniques in
[23] KV Daya Sagar1, Ch Shyam Krishna, G. Lalith Kumar, P. Surya Teja, G. Charless Babu,"A Method for finding threated web sites
through crime data mining and sentiment analysis",International Journal of Engineering & Technology, 7 (2.7) (2018) 62-65.
[24] A.Yasaswini, K.V. DayaSagar , K.ShriVishnu,V.HariNandan, PVRD. Prasadara Rao,"Automation of an IoT hub using artificial
intelligence techniques",International Journal of Engineering & Technology, 7 (2.7) (2018) 25-27.
[25] .K.V.Daya Sagar,M.Rupesh Chowdary,S.Mahesh,"Smart Crop Monitoring and FarmingUsing Internet of Things with Cloud",Jour
of Adv Research in Dynamical & Control Systems, Vol. 10, 02-Special Issue, 2018.
[26] .Rakesh shirsanth,Dr.K.V.Daya Sagar,"A Review of fine grained access control techniques",International Journal of Engineering
& Technology, 7 (2.7) (2018) 20-24.
[27] .K.V.Daya Sagar,Dr.S.Narayana,Dr.K.RaghavaRao,G.Bhavya Deepika,M.SaiKiran Reddy,Developing Smart Kitchen Inventory
tracking using Internet of Things,Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 02-Special Issue, 2018.
[28] .K. V. Daya Sagar*, Akella Pavan Kumar, Goli Sai Ankush, Thota Harika,Madireddy Saranya and Dasaraju
Hemanth,"Implementation of IoT based Railway Calamity Avoidance System using Cloud Computing Technology",Indian Journal of
Science and Technology, Vol 9(17), DOI: 10.17485/ijst/2016/v9i17/93020, May 2016,ISSN : 0974-6846.
[29] K.V.Daya Sagar, KPR Susmithanjali, K.Alekhya,” An Enhanced Finger Print And Fusing Face Authentication For ATM Cash
Withdrawal By Using SVM And Convocational Neural Networks”, INTERNATIONAL JOURNAL OF SCIENTIFIC &
TECHNOLOGY RESEARCH VOLUME 9, ISSUE 04, APRIL 2020 ISSN 2277-8616
[30] K.V.Daya Sagar1, Smt. P.S.G.Arunasri2, Smt.Sridevi Sakamuri3, Smt.J.Kavitha4,Dr.DBK Kamesh5,”Collaborative Filtering and
Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19
Pandemic”, IOP Conf. Series: Materials Science and Engineering 981 (2020) 022009,IOP Publishing,doi:10.1088/1757-
899X/981/2/022009
[31] K.V.Daya Sagar1, Smt.J Kavitha2, Dr.Balabrahmeswara Kadaru3,M.Venkateswara Rao4, Dr.D.B.K.Kamesh5,”Detecting Faults
within a Cloud Using Machine Learning Techniques”, IOP Conf. Series: Materials Science and Engineering 981 (2020) 022029 ,IOP
Publishing,doi:10.1088/1757-899X/981/2/022029
[2] Thottan, M. et al. (2003) "Anomaly detection in IP networks". IEEE Transactions on signal processing, 2003,
51.8: 2191-2204.
[3] Kruegel, C. Et al. "Anomaly detection of web-based attacks." In: Proceedings of the 10th ACM conference on
Computer and communications security. ACM, 2003. p. 251-261.
[4] Leung, K., et al., (2005) "Unsupervised anomaly detection in network intrusion detection using clusters". In:
Proceedings of the Twenty-eighth Australasian conference on Computer Science-Volume 38. Australian
Computer Society, Inc., 2005. p. 333-342.
[5] Mahadevan, V. et al. (2010) Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition. IEEE, 2010. p. 1975-1981.
[6] Hauskrecht, M. et al. (2007) Evidence-based anomaly detection in clinical domains. In: AMIA Annual
Symposium Proceedings. American Medical Informatics Association, 2007. p. 319.
[7] Lakhina, A. et al. (2004) Diagnosing network-wide traffic anomalies. In:
ACSIGCOMM computer communication review. ACM, 2004. p. 219-230
[8] Xu, W., et al. (2009) Detecting large-scale system problems by mining console logs. In: Proceedings of the
ACM SIGOPS 22nd symposium on Operating systems principles. ACM, 2009. p. 117-132.
[9] Solaimani, M. et al. (2014) Spark-based anomaly detection over multisource VMware performance data in realtime. In: 2014 IEEE Symposium on Computational Intelligence in Cyber Security (CICS). IEEE, 2014. p. 1-
8.
[10] Ahmed, M. et al. (2016) A survey of network anomaly detection techniques. Journal of Network and
Computer Applications, 2016, 60: 19-31
[11] Ye, N. et al. (2000) "A markov chain model of temporal behavior for anomaly detection". In: Proceedings of
the 2000 IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop. West Point,
NY, 2000. p. 169.
[12] Gao, C. (2009) "Host anomalies detection using logistic regression modeling". In: 2009 First International
Workshop on Education Technology and Computer Science. IEEE, 2009. p. 655-659.
[13] Steinward, I., et al. (2005) "A classification framework for anomaly detection". Journal of Machine Learning
Research, 2005, 6.Feb: 211-232.
[14] Erboy, M.O. et al., (2020) Available at the link: https://drive.google.com/-
drive/folders/1x1t3bUJZ7Tvl7rnqqD1ojKnbSuoKnGH?usp=sharing
[15] Baeth, M.J. et al. (2019). Detecting misinformation in social networks using provenance data, CONCURR
COMP-PRACT E. Journal ISSN: 1532- 0626, Vol: 31, Issue:3.
[16] Baeth M. J. et al. (2018) "An approach to custom privacy policy violation detection
problems using big social provenance data", CONCURR COMP-PRCT E. Journal ISSN: 1532-0626,
Vol.:30, Issue: 21.
[17] Baeth, M.J. et al. (2017). "Detecting misinformation in social networks using provenance data", SKG'17.
[18] Baeth, M.J. et al. (2015). On the Detection of Information Pollution and Violation of Copyrights in the Social
Web, SOCA'15.
[19] Dundar, B. et al. (2016) A Big Data Processing Framework for Self Healing Internet of Things Applications,
SKG'16.
[20] K. V. Daya Sagar , P Sai Durga , G. Kavya , K Sri Sravya , K. Krishna Veni,"Mobile based home mechanisation framework using
IoT for smart cities",International Journal of Engineering & Technology, 7 (2.7) (2018) 266-269.
[21] K Sai Prasanthi , K.V.Daya Sagar ,"Survey on secure protocols for data sharing through edge of cloud assisted internet of
things",International Journal of Engineering & Technology, 7 (2.7) (2018) 92-95.
[22] K. V. Daya Sagar, U. Abbulu,K. Chaitanya Kumar Reddy,"Using Fuzzy Clustering Techniques in
[23] KV Daya Sagar1, Ch Shyam Krishna, G. Lalith Kumar, P. Surya Teja, G. Charless Babu,"A Method for finding threated web sites
through crime data mining and sentiment analysis",International Journal of Engineering & Technology, 7 (2.7) (2018) 62-65.
[24] A.Yasaswini, K.V. DayaSagar , K.ShriVishnu,V.HariNandan, PVRD. Prasadara Rao,"Automation of an IoT hub using artificial
intelligence techniques",International Journal of Engineering & Technology, 7 (2.7) (2018) 25-27.
[25] .K.V.Daya Sagar,M.Rupesh Chowdary,S.Mahesh,"Smart Crop Monitoring and FarmingUsing Internet of Things with Cloud",Jour
of Adv Research in Dynamical & Control Systems, Vol. 10, 02-Special Issue, 2018.
[26] .Rakesh shirsanth,Dr.K.V.Daya Sagar,"A Review of fine grained access control techniques",International Journal of Engineering
& Technology, 7 (2.7) (2018) 20-24.
[27] .K.V.Daya Sagar,Dr.S.Narayana,Dr.K.RaghavaRao,G.Bhavya Deepika,M.SaiKiran Reddy,Developing Smart Kitchen Inventory
tracking using Internet of Things,Jour of Adv Research in Dynamical & Control Systems, Vol. 10, 02-Special Issue, 2018.
[28] .K. V. Daya Sagar*, Akella Pavan Kumar, Goli Sai Ankush, Thota Harika,Madireddy Saranya and Dasaraju
Hemanth,"Implementation of IoT based Railway Calamity Avoidance System using Cloud Computing Technology",Indian Journal of
Science and Technology, Vol 9(17), DOI: 10.17485/ijst/2016/v9i17/93020, May 2016,ISSN : 0974-6846.
[29] K.V.Daya Sagar, KPR Susmithanjali, K.Alekhya,” An Enhanced Finger Print And Fusing Face Authentication For ATM Cash
Withdrawal By Using SVM And Convocational Neural Networks”, INTERNATIONAL JOURNAL OF SCIENTIFIC &
TECHNOLOGY RESEARCH VOLUME 9, ISSUE 04, APRIL 2020 ISSN 2277-8616
[30] K.V.Daya Sagar1, Smt. P.S.G.Arunasri2, Smt.Sridevi Sakamuri3, Smt.J.Kavitha4,Dr.DBK Kamesh5,”Collaborative Filtering and
Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19
Pandemic”, IOP Conf. Series: Materials Science and Engineering 981 (2020) 022009,IOP Publishing,doi:10.1088/1757-
899X/981/2/022009
[31] K.V.Daya Sagar1, Smt.J Kavitha2, Dr.Balabrahmeswara Kadaru3,M.Venkateswara Rao4, Dr.D.B.K.Kamesh5,”Detecting Faults
within a Cloud Using Machine Learning Techniques”, IOP Conf. Series: Materials Science and Engineering 981 (2020) 022029 ,IOP
Publishing,doi:10.1088/1757-899X/981/2/022029
Section
GE3- Computers & Information Technology