Spread spectrum communication plays a vital role in safeguarding the Internet of Things systems, due to its inherently low probability of interception and anti-jamming capability. However, conventional spread spectrum systems based on pseudorandom sequences are disadvantageous in limited sequence length and deterministic periodicity, making them vulnerable to brute-force attacks. To address these limitations and enhance the randomness of the spread spectrum sequences, a novel wireless channel randomness integrated spread spectrum sequence generation method is proposed in this work. Taking advantages of the intrinsic randomness, temporal variations, and unpredictability of wireless channel fadings, the proposed approach converts the extracted channel features into ordered sequences, which are then used to control the selection of irreducible generating polynomials for spread spectrum sequence generation. The proposed method improves the randomness and secrecy of the integrated spread spectrum sequence. Theoretical analysis and simulation results demonstrate that the proposed sequences not only achieve higher randomness entropy compared to the traditionalm-sequence, but also pass the National Institute of Standards and Technology randomness tests. Furthermore, performance evaluations under various signal-to-interference ratio conditions show improved autocorrelation properties and largely lower bit error rates, validating the effectiveness of the proposed method in improving the anti-jamming capability.

Physical-layer key generation (PLKG) has attracted significant attention due to its lightweight and strong randomness, making it highly suitable for drones with energy and computational constraints. However, when drone communications rely on relay nodes, untrusted relay nodes may launch attacks such as eavesdropping, tampering, replay and man-in-the-middle, leading to key leakage. To address this problem, we propose a drone -physical unclonable functions (PUFs) - PLKG (DPPLKG) scheme that enhances resistance to untrusted relay attacks. In DPPLKG, legitimate drones are equipped with paired PUF hardware, and artificial noise is injected during the PLKG process to reduce the accuracy of untrusted relay channel estimation. The generated physical-layer key serves as the PUFs challenge, and the unique hardware response of PUFs generates the final session key, ensuring consistent and secure key generation among legitimate drones. After analysis, DPPLKG can effectively resist various threats such as relay eavesdropping, tampering, replay, and impersonation attacks. The simulation results show that DPPLKG outperforms traditional PLKG in terms of key generation rate, bit error rate, and key entropy. It can also resist Doppler frequency changes caused by drone mobility and hardware noise caused by temperature changes in PUF devices. In addition, the proposed scheme has an acceptable delay time, making it a practical and efficient solution for achieving drone secure communications in the presence of untrusted relays.