Secure automated threat detection and prevention (SATDP)

  • Authors

    • CH Ramaiah
    • D Adithya Charan
    • R Syam Akhil
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.11760
  • Artificial intelligence, intrusion detection System, network security, machine learning, (supervised and unsupervised) learning.
  • Secure automated threat detection and prevention is the more effective procedure to reduce the workload of analyst by scanning the network, server functions& then informs the analyst if any suspicious activity is detected in the network. It monitors the system continuously and responds according to the threat environment. This response action varies from phase to phase. Here suspicious activities are detected by the help of an artificial intelligence which acts as a virtual analyst concurrently with network intrusion detection system to defend from the threat environment and taking appropriate measures with the permission of the analyst. In its final phase where packet analysis is carried out to surf for attack vectors and then categorize supervised and unsupervised data.  Where the unsupervised data will be decoded or converted to supervised data with help of analyst feedback and then auto-update the algorithm (virtual analyst). So that it evolves the algorithm (with active learning mechanism) itself by time and become more efficient, strong. So, it can able to defend form similar or same kind of attacks.

     

     

  • References

    1. [1] Chandola V, Banerjee A & Kumar V, “Anomaly detection: A surveyâ€, ACMComput. Surv., Vol.41, No.3, (2009), pp.15:1–15:58.

      [2] Aggarwal CC, “Outlier ensembles: Position paperâ€, SIGKDD Explor. Newsl., Vol.14, No.2, (2013), pp.49–58.

      [3] ling Shyu M, ching Chen S, Sarinnapakorn K & Chang L, “A lanomaly detection scheme based on principal component classifierâ€, Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, in conjunction with the Third IEEE I international Conference on Data Mining, (2003), pp.172–179.

      [4] Hawkins S, He H, Williams G & Baxter R, “Outlier detection using replicator neural networksâ€, Data Warehousing and Knowledge Discovery, ser. Lecture Notes in Computer Science, Springer Berlin Heidelberg, Vol.2454, (2002), pp.170–180.

      [5] Scholz M & Vigário R, “Nonlinear PCA: a new hierarchical ap-proachâ€, Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN), (2002), pp.439–444.

      [6] Schubert E, Wojdanowski R, Zimek A & Kriegel H, “On evaluation of outlier rankings and outlier scoresâ€, Proceedings of the Twelfth SIAM International Conference on Data Mining, (2012), pp.1047–1058.

      [7] Zimek A, Campello RJ & Sander J, “Ensembles for unsupervised outlier detection: Challenges and research questions a position paperâ€, SIGKDD Explor. Newsl., Vol.15, No.1, (2014), pp.11–22.

      [8] Pelleg D & Moore AW, “Active learning for anomaly and rare-category detectionâ€, Advances in Neural Information Processing Systems, (2004), pp.1073–1080.

      [9] Yen TF, “Detecting stealthy malware using behavioral features in network trafficâ€, Ph.D. dissertation, Carnegie Mellon University, (2011).

      [10] When Big Data Met Security: Is The New Era Beginning? Chuck Hollis, VP – CTO, EMC Corporation, 2011.

      http://chucksblog.emc.com/chucks_blog/2011/08/when-big-data- met-security-is-the-new-era-beginning.html

      [11] Vijayarani S &Dhayanand S, “Liver Disease Prediction using SVM and Naïve Bayes Algorithmsâ€, International Journal of Sci-ence, Engineering and Technology Research, Vol.4, (2015), pp.816-820.

      [12] Wang J, Jebara T & Chang SF, “Semi-supervised learning using greedy max- cutâ€, Journal of Machine Learning Research, Vol.14, No.1, (2013), pp.771-800.

      [13] Utkin V & Zhuk YA, “An one-class classification support vector machine model by interval-valued training dataâ€, Knowledge-Based Systems, Vol.120, (2017), pp.43-56.

      [14] Chang A, “R for Machine Learning, Prediction: Machine Learn-ing and Statisticsâ€, MIT OpenCourseWare, (2012), pp.1-8.

      Sharma V, Rai S & Dev A, “A Comprehensive Study of Artificial Neural Networksâ€, International Journal of Advanced Researchin Computer Science and Software Engineering, Vol.2, No.10, (2012), pp.278-284.
  • Downloads

  • How to Cite

    Ramaiah, C., Adithya Charan, D., & Syam Akhil, R. (2018). Secure automated threat detection and prevention (SATDP). International Journal of Engineering & Technology, 7(2.20), 86-89. https://doi.org/10.14419/ijet.v7i2.20.11760