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Hossein Doroud, Omar Alkhateeb, Elnaz Alizadeh Jarchlo and Falko Dressler, "L-IDS: A Lifelong Learning Approach for Intrusion Detection," Proceedings of IEEE International Wireless Communications & Mobile Computing Conference (IWCMC 2023), Marrakesh, Morocco, June 2023, pp. 482–487.


Intrusion detection systems (IDS) represent a class of defensive security tools with the purpose of protecting the network from intruders in the network administrators arsenal. Despite the high precision of traditional signature-based IDS, its effectiveness is still under question due to the growth of a number of encrypted attacks and the volume of network traffic. This is considered as one of the main motivations for researchers to develop anomaly-based IDS, which usually suffer from higher false positive rate. In this paper, we propose and implement a lifelong- learning anomaly detection IDS (L-IDS) with the capability of the network environment’s adaption to limit the false positive rate of anomaly detector in the range of signateure-based IDS. We consider Snort as a baseline and UNSW-NB15 as the ground truth in the evaluation of our proposal. We demonstrate how L-IDS achieves a higher level of precision in comparison with the existing signature-based IDS.

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Hossein Doroud
Omar Alkhateeb
Elnaz Alizadeh Jarchlo
Falko Dressler

BibTeX reference

    author = {Doroud, Hossein and Alkhateeb, Omar and Alizadeh Jarchlo, Elnaz and Dressler, Falko},
    doi = {10.1109/IWCMC58020.2023.10182443},
    title = {{L-IDS: A Lifelong Learning Approach for Intrusion Detection}},
    pages = {482--487},
    publisher = {IEEE},
    issn = {2376-6506},
    address = {Marrakesh, Morocco},
    booktitle = {IEEE International Wireless Communications \& Mobile Computing Conference (IWCMC 2023)},
    month = {6},
    year = {2023},

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Last modified: 2024-06-16