Adaptive Artificial Immune Networks for Mitigating DoS flooding Attacks
<|reference_start|>Adaptive Artificial Immune Networks for Mitigating DoS flooding Attacks: Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD'99, CAIDA'07 and CAIDA'08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.<|reference_end|>
@article{vidal2024adaptive,
    title={Adaptive Artificial Immune Networks for Mitigating DoS flooding Attacks},
    author={Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier Garc'ia
  Villalba},
    journal={J. Maestre Vidal, A. L. Sandoval Orozco, L. J. Garc\'ia Villalba:
  Adaptive Artificial Immune Networks for Mitigating DoS Flooding Attacks.
  Swarm and Evolutionary Computation. Vol. 38, pp. 3894-108, February 2018},
    year={2024},
    doi={10.1016/j.swevo.2017.07.002},
    archivePrefix={arXiv},
    eprint={2402.07714},
    primaryClass={cs.CR}
}