Intrusion Detection in Communication Networks Using Different Classifiers

  • Karuna S. BhosaleEmail author
  • Maria Nenova
  • Georgi Iliev
Conference paper


In this paper, we have executed Intrusion detection over computer networks utilizing different machine learning classifier algorithms. Here, we have compared the five different classifiers with the feature extraction technique using KDD Container 99 dataset. We compared the Navies Bayes Classifier, CNN Classifier, SVM Classifier, ANN Classifier, and KNN Classifier for obtaining better results compared with existing techniques and system. In this paper, we have used the Feature Extraction algorithm along with this classifier and compared these five classifiers’ results with each other. In this system, we have used the DDOS attacks created by the LOIC tools and executed the real-time execution of our proposed system. Here we have done the comparative study with all five classifiers using the performance matrix. In this paper, we proposed the Feature Extraction Algorithm in addition with better Classifiers using KDD Container 99 Dataset. We have measured the value of precision, recall, sensitivity, specificity, F1-score, Accuracy and Time.


CNN Naïve Bayes CNN SVM ANN KNN KDD container 99 dataset LOIC tool Feature extraction technique 


  1. 1.
    Saravanan S, Dr Chandrasekaran RM (2015 January) Intrusion detection system using Bayesian approach. Int J Eng Innov Technol (IJEIT) 4(7)Google Scholar
  2. 2.
    Mrutyunjaya Panda and Manas Ranjan Patra (2007 December) Network intrusion detection using Naïve Bayes IJCSNS Int J Comput Sci Network Security, 7(12)Google Scholar
  3. 3.
    Sandhya G (2014) Intrusion detection in wireless sensor network using genetic K-means algorithm. In: 2014 IEEE international conference on advanced communications, control, and computing technologiesGoogle Scholar
  4. 4.
    Jha J (2013) Intrusion detection system using support vector machine. In: International conference & workshop on advanced computing 2013 (ICWAC 2013) –
  5. 5.
    Ranade S (2016) Intrusion detection system using SVM classification. Int J Innov Res Comput Commun EngGoogle Scholar
  6. 6.
    Elike Hodo, Xavier Bellekens, Andrew Hamilton (2016) Threat analysis of IoT networks using artificial neural network intrusion detection systemGoogle Scholar
  7. 7.
    Dubey R (2013) KNN based classifier systems for intrusion detection. Int J Adv Comput Technol 2(4):44–48Google Scholar
  8. 8.
    Sodiya AS (2014) Neural network-based intrusion detection systems. Int J Comput Appl 106(18):0975–8887Google Scholar
  9. 9.
    Yi P (2014) A new intrusion detection system based on KNN classification algorithm in wireless sensor network. J Electr Comput Eng 2014:1–8CrossRefGoogle Scholar
  10. 10.
    Vani R (2017 October) Towards efficient intrusion detection using deep learning techniques: a review. Int J Adv Res Comput Commun Eng ISO 3297:2007 Certified 6(10)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Karuna S. Bhosale
    • 1
    Email author
  • Maria Nenova
    • 1
  • Georgi Iliev
    • 1
  1. 1.Faculty of TelecommunicationTechnical University of SofiaSofiaBulgaria

Personalised recommendations