Autors: Nenova, M. V., Bosale, K. S., Iliev, G. L.
Title: Intrusion Detection in Communication Networks Using Different Classifiers
Keywords: CNN Naïve Bayes CNN SVM ANN KNN KDD container 99 dataset LOIC tool Feature extraction technique

Abstract: 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..

References

    Issue

    Techno-Societal 2018, Springer, pp. 19-29, 2020, India, Springer, ISBN 978-3-030-16961-9

    Цитирания (Citation/s):
    1. Reddy SaiSindhuTheja, Gopal K. Shyam, An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment, Applied Soft Computing Journal, Volume 100, 106997, ISSN 1568-4946, 2021. - 2021 - в издания, индексирани в Scopus или Web of Science
    2. Jaw E, Wang X. Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach. Symmetry. 2021; 13(10):1764. https://doi.org/10.3390/sym13101764. - 2021 - в издания, индексирани в Scopus или Web of Science
    3. Awotunde, Joseph Bamidele, and Sanjay Misra. "Feature Extraction and Artificial Intelligence-Based Intrusion Detection Model for a Secure Internet of Things Networks." Illumination of Artificial Intelligence in Cybersecurity and Forensics. Springer, Cham, 2022. 21-44. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus