Journal of ISMAC
IRO Journals
Volume 3 — Issue 1 — March 2021

Naive Bayes and Entropy based Analysis and Classification of Humans and Chat Bots

https://doi.org/10.36548/jismac.2021.1.004
Pages: 40-49
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S. Smys
Professor, Department of CSE, RVS Technical Campus, Coimbatore, India
Haoxiang Wang
Director and Lead Executive Faculty Member, GoPerception Laboratory, Cornell University, Ithaca, USA
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How to Cite

Smys, S., and Haoxiang Wang. 2021. “Naive Bayes and Entropy Based Analysis and Classification of Humans and Chat Bots”. Journal of ISMAC 3 (1): 40-49. https://doi.org/10.36548/jismac.2021.1.004.
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Keywords

— Naive Bayes Classifier
— Entropy classifier
— chatbots
— malware
— spam
— classification
Published: 07-04-2021

Abstract

Internet users are largely threatened by abuse and manipulation of several automated chat service programs called as chat bots. Malware and spam is distributed by the popular chat networks using chat bots. The commercial chat network is surveyed in this paper with a series of measurements. A series of 15 advanced to simple chatbots are used for this purpose. When compared to the bot behavior, the complexity of human behavior is high. A classification system is proposed for accurate distinguishing between human user and chatbots based on the measurements obtained from the study. Na誰ve Bayes Classifier and entropy classifier are used for the purpose of classification. Chat bot detection is performed with improved efficiency and accuracy using these classifiers. The speed of Na誰ve Bayes Classifier and accuracy of entropy classifier compliments each other in the process of detection of chat bots. The improved efficiency of the proposed system is proved by testing and comparison with the existing schemes.

References

  1. Parimala, M., Swarna Priya, R. M., Praveen Kumar Reddy, M., Lal Chowdhary, C., Kumar Poluru, R., & Khan, S. (2021). Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach. Software: Practice and Experience, 51(3), 550-570.
  2. Tyagi, P., & Tripathi, R. C. (2019, February). A review towards the sentiment analysis techniques for the analysis of twitter data. In Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE).
  3. Karanja, E. M., Masupe, S., & Jeffrey, M. G. (2020). Analysis of internet of things malware using image texture features and machine learning techniques. Internet of Things, 9, 100153.
  4. Bird, J. J., Ekárt, A., Buckingham, C. D., & Faria, D. R. (2019, July). High resolution sentiment analysis by ensemble classification. In Intelligent Computing-Proceedings of the Computing Conference (pp. 593-606). Springer, Cham.
  5. Pophale, S., Gandhi, H., & Gupta, A. K. (2021). Emotion Recognition Using Chatbot System. In Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (pp. 579-587). Springer, Singapore.
  6. Ahuja, R., Chug, A., Gupta, S., Ahuja, P., & Kohli, S. (2020). Classification and clustering algorithms of machine learning with their applications. In Nature-Inspired Computation in Data Mining and Machine Learning (pp. 225-248). Springer, Cham.
  7. Ismail, Z., Jantan, A., Yusoff, M. N., & Kiru, M. U. (2021). The effects of feature selection on the classification of encrypted botnet. Journal of Computer Virology and Hacking Techniques, 17(1), 61-74.
  8. Bird, J. J., Ekárt, A., Buckingham, C. D., & Faria, D. R. Ensemble Classification in Multi-level Sentiment Analysis for Cross-Domain Application.
  9. Victor, D. B., Kawsher, J., Labib, M. S., & Latif, S. (2020, November). Machine Learning Techniques for Depression Analysis on Social Media-Case Study on Bengali Community. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1118-1126). IEEE.
  10. Tamizharasi, B., Livingston, L. J., & Rajkumar, S. (2020, December). Building a Medical Chatbot using Support Vector Machine Learning Algorithm. In Journal of Physics: Conference Series (Vol. 1716, No. 1, p. 012059). IOP Publishing.
  11. Leonova, V. (2020, June). Review of Non-English Corpora Annotated for Emotion Classification in Text. In International Baltic Conference on Databases and Information Systems (pp. 96-108). Springer, Cham.
  12. Vijayasekaran, G., & Rosi, S. (2018). Spam and email detection in big data platform using naives bayesian classifier. International Journal of Computer Science and Mobile Computing, 7(4), 53-58.
  13. Jacob, I. J. (2020). Performance evaluation of caps-net based multitask learning architecture for text classification. Journal of Artificial Intelligence, 2(01), 1-10.
  14. Joseph, S. I. T., & Thanakumar, I. (2019). Survey of data mining algorithm’s for intelligent computing system. Journal of trends in Computer Science and Smart technology (TCSST), 1(01), 14-24.
  15. Manoharan, S. (2020). Geospatial and social media analytics for emotion analysis of theme park visitors using text mining and gis. Journal of Information Technology, 2(02), 100-107.
  16. Sungheetha, A., & Sharma, R. (2020). Transcapsule model for sentiment classification. Journal of Artificial Intelligence, 2(03), 163-169.