Anomaly-Based Detection of System-Level Threats and Statistical Analysis

  • Himanshu MishraEmail author
  • Ram Kumar Karsh
  • K. Pavani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 767)


This paper presents various parameters for the analysis of threats to any network or system. These parameters are based on the anomalous behavior of the system. To characterize the behavior of the system connected to the Internet, we need to consider a number of incoming and outgoing packets, the process running in the background and system response which include CPU utilization and RAM utilization. Dataset is collected for the above-mentioned parameter under the normal condition and under the condition of any cyber-attack or threat. Based on the deviation in the values under two conditions, another statistical parameter entropy is calculated. This will helps us to identify the type of threats.


Entropy Threats Anomaly detection System response 


  1. 1.
    R. Sekar, A. Gupta, J. Frullo, T. Shanbhag, A. Tiwari, H. Yang, S. Zhou, Specification-based anomaly detection: a new approach for detecting network intrusions, in CCS’02: Proceedings of the 9th ACM Conference on Computer and Communications Security (2002)Google Scholar
  2. 2.
    R. Ravinder Reddy, Network intrusion anomaly detection using radial basis function networks. Int. J. Res. Comput. Sci. 1011–1014 (2017)Google Scholar
  3. 3.
    A.S. Navaz, V. Sangeetha, C. Prabhadevi, Entropy based anomaly detection system to prevent DDoS attacks in cloud. arXiv preprint arXiv 1308–6745 (2013)Google Scholar
  4. 4.
    M. Tavallaee, N. Stakhanova, A.A. Ghorbani, Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(5), 516–524 (2010)CrossRefGoogle Scholar
  5. 5.
    V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  6. 6.
    F. Sabahi, A. Movaghar, Intrusion detection: a survey, in ICSNC’08, 3rd International Conference on IEEE Systems and Networks Communications, 23–26 Oct 2008Google Scholar
  7. 7.
    S.T. Kung, C.C. Cheng, C.C. Liu, Y.C. Chen, Dynamic power saving by monitoring CPU utilization. U.S. Patent, 574,739, Jun 2003Google Scholar
  8. 8.
    R.K. Shymasundar, N.V. Narendra Kumar, P. Teltumde, Realizing software vault on Android through information-flow control, in 2017 IEEE Symposium on Computers and Communications (ISCC) (2017), pp. 1007–1014Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology, SilcharSilcharIndia

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