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

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. Naïve 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 Naïve 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.

[2]  G. Vijayasekaran,et al.  SPAM AND EMAIL DETECTION IN BIG DATA PLATFORM USING NAIVES BAYESIAN CLASSIFIER , 2018 .

[3]  Shruti Kohli,et al.  Classification and Clustering Algorithms of Machine Learning with their Applications , 2020, Nature-Inspired Computation in Data Mining and Machine Learning.

[4]  I. Jeena Jacob,et al.  Performance Evaluation of Caps-Net Based Multitask Learning Architecture for Text Classification , 2020, March 2020.

[5]  S. Rajkumar,et al.  Building a Medical Chatbot using Support Vector Machine Learning Algorithm , 2020, Journal of Physics: Conference Series.

[6]  Aman Jantan,et al.  The effects of feature selection on the classification of encrypted botnet , 2020, Journal of Computer Virology and Hacking Techniques.

[8]  A. Gupta,et al.  Emotion Recognition Using Chatbot System , 2020 .

[9]  Viktorija Leonova.  Review of Non-English Corpora Annotated for Emotion Classification in Text , 2020, DB&IS.

[10]  Shedden Masupe,et al.  Analysis of internet of things malware using image texture features and machine learning techniques , 2020, Internet Things.

[11]  M. Parimala,et al.  Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach , 2020, Softw. Pract. Exp..

[12]  R RajeshSharma,et al.  TransCapsule Model for Sentiment Classification , 2020 .

[13]  Anikó Ekárt,et al.  High Resolution Sentiment Analysis by Ensemble Classification , 2019, Advances in Intelligent Systems and Computing.

[14]  Iwin Thanakumar Joseph S Dr,et al.  SURVEY OF DATA MINING ALGORITHM’S FOR INTELLIGENT COMPUTING SYSTEM , 2019, Journal of Trends in Computer Science and Smart Technology.

[15]  R. C. Tripathi,et al.  A Review towards the Sentiment Analysis Techniques for the Analysis of Twitter Data , 2019 .