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A Self-Adaptive Intrusion Detection System for Securing UAV-to-UAV Communications Based on the Human Immune System in UAV Networks

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Abstract

Today, the use of Unmanned aerial vehicles (UAVs) technology has made significant progress due to its widespread use. UAVs are widely used in agriculture, commercial, military, civilian, and environmental applications. Therefore, for better efficiency, the UAV must be able to communicate effectively through the communication of the UAV system with the UAV and other communication of the UAV with the network infrastructure through the communication of the UAV with the infrastructure. In particular, security issues are a serious concern in such networks because the top-secret information exchanged between UAVs is prone to various attacks such as Sybil, blackhole and Flooding attacks. This paper proposes a method called SID-UAV that is resistant to malicious UAVs that threaten UAV to UAV communications. The SID-UAV method uses a self-matching system that detects the safest path between UAVs. This method includes various phases, including the decision-making phase, the path discovery and investigation phase, the destructive UAV response phase, and the information database registration phase. Also, in the SID-UAV method, three types of modules of route investigation, decision module and defense module are considered. Each of these modules is distributed in different parts of UAV networks and has sub-modules. These sub-modules have their own tasks and to perform and process information quickly, it is connected to the knowledge base to quickly record information and use the stored information. The performance of the SID-UAV method in NS-3 has been evaluated and tested. The evaluation results show the superiority of SID-UAV method over BRUIDS, SFA and SUAS-HIS methods in Packet Delivery Ratio (PDR), Packet Lost Ratio (PLR), Average False Positive (AFP), Average False Negative (AFN) and Average Detection Ratio (ADR).

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

All code for data analysis associated with the current submission is available from the corresponding author upon reasonable request.

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Funding

This study has received no funding from any organizations.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Shahid Beheshti University, G. C. Evin, Tehran, 1983969411, Iran

    Reza Fotohi

  2. Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Masoud Abdan

  3. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Sanaz Ghasemi

Authors
  1. Reza Fotohi
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  2. Masoud Abdan
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  3. Sanaz Ghasemi
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Contributions

Reza Fotohi: Formal analysis, Conceptualization, Methodology, Data curation, Investigation, Validation, Writing—original draft.

Masoud Abdan: Data curation, Methodology, Writing—review & editing, Supervision.

Sanaz Ghasemi: Formal analysis, Data curation, Methodology, Conceptualization, Writing—review & editing, Supervision.

Corresponding author

Correspondence to Reza Fotohi.

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Fotohi, R., Abdan, M. & Ghasemi, S. A Self-Adaptive Intrusion Detection System for Securing UAV-to-UAV Communications Based on the Human Immune System in UAV Networks. J Grid Computing 20, 22 (2022). https://doi.org/10.1007/s10723-022-09614-1

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  • DOI: https://doi.org/10.1007/s10723-022-09614-1

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