Comprehensive Study of Tensor Flow with Parameter Variation

Authors

  • Gandam Vijay Kumar  Professor & Principal, Department of Electronics & Communication Engineering, Sri Chaitanya Technical Campus, Hyderabad, India
  • Dr. Md Ateeq Ur Rehman  Professor of CSE, Department of Computer Science and Engineering, Bhoj Reddy Engineering College for Women, Hyderabad, India

Keywords:

Intrusion Detection Systems, TensorFlow, Data Mining, Machine Learning, Classification, Regression

Abstract

Intrusion detection systems (IDS) have evolved significantly since their inception by James Anderson in 1980. This paper explores the integration of TensorFlow, a powerful machine learning framework, with various data mining techniques to enhance the performance of IDS. We review a range of data mining methods, including clustering, Bayesian networks, Hidden Markov Models, decision trees, support vector machines, genetic algorithms, and fuzzy logic, and their applications in intrusion detection. The study highlights how TensorFlow can be utilized for both classification and regression tasks to improve detection accuracy and system efficiency. We discuss practical implementations using TensorFlow for handling large-scale datasets and optimizing model parameters. The findings suggest that TensorFlow, when combined with effective data mining techniques, provides a robust framework for developing advanced IDS capable of addressing contemporary cybersecurity threats.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-10-30

Issue

Volume 10, Issue 5, September-October-2023

Section

Research Articles

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How to Cite

[1]
Gandam Vijay Kumar, Dr. Md Ateeq Ur Rehman "Comprehensive Study of Tensor Flow with Parameter Variation" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 5, pp.312-321, September-October-2023.