Presently in the technological era, Internet is the sole medium to access and run web based services such as a business, banking, education, e-commerce, weather forecasting, etc. Because of the increasing usage of such services and dependency on the Internet, many types of malware threats have emerged over the time that affects the timely delivery of these services. Distributed Denial of Service (DDoS) attack is one of such crucial threats. Many researchers have proposed diversified DDoS detection approaches using information theory based entropy and divergence detection metrics in the past. This paper proposes to use a novel LeCam divergence metric to detect different types of DDoS attacks based on flow similarity between the network traffic flows. The effectiveness of the proposed approach is corroborated using widely used benchmarked MIT Lincoln and CAIDA datasets along with DDoSTB dataset. The results show that the novel LeCam Divergence metric is more effective as compared to the traditional Kullbeck-Leibler, Bhattacharyya and Pearson Divergence measures.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    LeCAM: A novel metric for detecting DDoS attacks


    Contributors:


    Publication date :

    2019-06-01


    Size :

    3036048 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Exploring Realistic VANET Simulations for Anomaly Detection of DDoS Attacks

    Baharlouei, Hamideh / Makanju, Adetokunbo / Zincir-Heywood, Nur | IEEE | 2022




    Comparative analysis of the effectiveness of existing methods of networks security from DDoS attacks

    N. V. Evglevskaya / A. Yu. Zuev / A. O. Karasenko et al. | DOAJ | 2020

    Free access