  UAVNs (unmanned aerial vehicle networks) may become vulnerable to threats and
attacks due to their characteristic features such as highly dynamic network
topology, open-air wireless environments, and high mobility. Since previous
work has focused on classical and metaheuristic-based approaches, none of these
approaches have a self-adaptive approach. In this paper, the challenges and
weaknesses of previous methods are examined in the form of a table.
Furthermore, we propose an agent-based self-protective method (ASP-UAVN) for
UAVNs that is based on the Human Immune System (HIS). In ASP-UAS, the safest
route from the source UAV to the destination UAV is chosen according to a
self-protective system. In this method, a multi-agent system using an
Artificial Immune System (AIS) is employed to detect the attacking UAV and
choose the safest route. In the proposed ASP-UAVN, the route request packet
(RREQ) is initially transmitted from the source UAV to the destination UAV to
detect the existing routes. Then, once the route reply packet (RREP) is
received, a self-protective method using agents and the knowledge base is
employed to choose the safest route and detect the attacking UAVs. The proposed
ASP-UAVN has been validated and evaluated in two ways: simulation and
theoretical analysis. The results of simulation evaluation and theory analysis
showed that the ASP-UAS increases the Packet Delivery Rate (PDR) by more than
17.4, 20.8, and 25.91%, and detection rate by more than 17.2, 23.1, and 29.3%,
and decreases the Packet Loss Rate (PLR) by more than 14.4, 16.8, and 20.21%,
the false-positive and false-negative rate by more than 16.5, 25.3, and 31.21%
those of SUAS-HIS, SFA and BRUIDS methods, respectively.
