Real Time Decision Model Considering the Performance of Datalink
ABSTRACT
Pilot real time decision has a significant influence on the aircraft battlefield survivability. The ability of datalink is transmitting and sharing information which will provide support to pilot to make real time decision. In this paper, an algorithm is proposed to build a pilot real time decision model considering the performance of datalink. Firstly, an active deception jamming model is built which is used between the aircraft and the radar guided missile.
Secondly, a Link16 model is built by OPENT and the throughput of two different slot assignment schemes is obtained by the simulation. Thirdly, a pilot real time decision model is built based on the influence diagram. The example shows that: the throughput will increase as the repetition rate increases which will lead that the pilot has a low level mental pressure and will obtain an optimal decision and the aircraft also has a high battlefield survivability.
app.blackhole.run
app.blackhole.run
ABSTRACT
Pilot real time decision has a significant influence on the aircraft battlefield survivability. The ability of datalink is transmitting and sharing information which will provide support to pilot to make real time decision. In this paper, an algorithm is proposed to build a pilot real time decision model considering the performance of datalink. Firstly, an active deception jamming model is built which is used between the aircraft and the radar guided missile.
Secondly, a Link16 model is built by OPENT and the throughput of two different slot assignment schemes is obtained by the simulation. Thirdly, a pilot real time decision model is built based on the influence diagram. The example shows that: the throughput will increase as the repetition rate increases which will lead that the pilot has a low level mental pressure and will obtain an optimal decision and the aircraft also has a high battlefield survivability.
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True Skip Intrusion Detection and Avionics Network Cyber-Attack Simulation
MIL-STD-1553 is a communication bus that has been used by many military avionics platforms such as the F-15 and F-35 fighter jets for almost 50 years. Recently, it has become clear that the lack of security on MIL-STD-1553 and the requirement for internet communication between planes has revealed numerous potential attack vectors for malicious parties. Prevention of these attacks by modernizing the MIL-STD-1553 is not practical due to the military applications and existing far-reaching installations of the bus. We present a software system that can simulate bus transmissions to create easy, replicable, and large datasets of MIL-STD-1553 communications. We also propose an intrusion detection system (IDS) that can identify anomalies and the precise type of attack using recurrent neural networks with a reinforcement learning true-skip data selection algorithm. Our IDS outperforms existing algorithms designed for MIL-STD-1553 in binary anomaly detection tasks while also performing attack classification and minimizing computational resource cost. Our simulator can generate more data with higher fidelity than existing methods and integrate attack scenarios with greater detail. Furthermore, the simulator and IDS can be combined to form a web-based attack-defense game.
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