Krishna Gopal Sharma

Work place: Dr. A.P.J. Abdul Kalam Technical University / CSE, Lucknow, India



Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Theoretical Computer Science


Krishna Gopal Sharma is a research scholar in the department of CSE, from G. B. T. U. Lucknow (now known as Dr. A.P.J. Abdul Kalam Technical University, Lucknow). He completed his M. Tech from the same university. He has more than 18 years of experience in academics. He has publications in various journals and conferences of international repute. He is a Lifetime Member of the Computer Society of India.

Author Articles
Predicting Intrusion in a Network Traffic Using Variance of Neighboring Object’s Distance

By Krishna Gopal Sharma Yashpal Singh

DOI:, Pub. Date: 8 Apr. 2023

Activities in network traffic can be broadly classified into two categories: normal and malicious. Malicious activities are harmful and their detection is necessary for security reasons. The intrusion detection process monitors network traffic to identify malicious activities in the system. Any algorithm that divides objects into two categories, such as good or bad, is a binary class predictor or binary classifier. In this paper, we utilized the Nearest Neighbor Distance Variance (NNDV) classifier for the prediction of intrusion. NNDV is a binary class predictor and uses the concept of variance on the distance between objects. We used KDD CUP 99 dataset to evaluate the NNDV and compared the predictive accuracy of NNDV with that of the KNN or K Nearest Neighbor classifier. KNN is an efficient general purpose classifier, but we only considered its binary aspect. The results are quite satisfactory to show that NNDV is comparable to KNN. Many times, the performance of NNDV is better than KNN. We experimented with normalized and unnormalized data for NNDV and found that the accuracy results are generally better for normalized data. We also compared the accuracy results of different cross validation techniques such as 2 fold, 5 fold, 10 fold, and leave one out on the NNDV for the KDD CUP 99 dataset. Cross validation results can be helpful in determining the parameters of the algorithm.

[...] Read more.
Other Articles