Unmasking Deception Strategies For Attack Detection

DOI

10.2991/978-94-6463-471-6_133How to use a DOI?

Keywords

machine learning; Naïve Bayes; SVM; digital assaults; and phishing attacks

Abstract

In light of the increasing complexity of digital security risks, This work aims to utilize machine learning algorithms, particularly Support Vector Machines (SVM) and Naive Bayes, to bolster the identification and mitigation of deceptive practices in digital environments. The utilization of SVM and Naive Bayes algorithms in tandem offers a comprehensive approach to deception detection, leveraging the strengths of each algorithm to create a more robust and accurate system. Support Vector Machines represent a subset of supervised machine learning methods utilized for tasks involving classification and regression. The core aim of an SVM is to identify the optimal hyperplane that effectively segregates distinct classes within the feature space. SVMs are effective in high-dimensional spaces and excel especially in handling intricate data distributions. Conversely, Naive Bayes operates as a probabilistic classification algorithm rooted in Bayes’ theorem. Naive Bayes is often surprisingly effective, especially in text classification and spam filtering. Naive Bayes is computationally efficient and requires a relatively small amount of training data. It's particularly well-suited for situations where the independence assumption does not markedly impact the classification performance. By this work on deception identification the accuracy that was generated when compare to other works is 90%, and time taken to predict is 12ms with the precision of 0.88.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. Nivetha
AU  - R. Tamilkodi
AU  - Repaka Subbarao
AU  - Pitta Rupesh Sai Teja
AU  - Chinta Sowmya
AU  - Chamarthi Leela Sri Krishna Pavan Murthy
PY  - 2024
DA  - 2024/07/30
TI  - Unmasking Deception Strategies For Attack Detection
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1377
EP  - 1386
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_133
DO  - 10.2991/978-94-6463-471-6_133
ID  - Nivetha2024
ER  -