# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 1 Introduction
This means that if the attacker is closely located to the near-user side with a high SNR value, the attacker can intercept the FH signal and trace the hopping pattern. Once a hopping pattern is reproducible, an attacker can generate FH signals similar to those of the authenticated user. The two hopping patterns become undiscernible and the attacker can pretend to be the user. In this case, the received signal can be demodulated to proceed to the MAC layer inspection step. The MAC layer authentication system should discern the attacker unless even the digital key is exposed to the attacker. That is, if the attacker knew the digital key of the network system, the attacker would be able to pretend to be the authenticated user, which is the case in deceptive jamming attacks [18] or man-in-the-middle attacks [19]. These attacks are not easily detectable and can flood fake data to mislead the network system [18]. To prevent such attacks, a non-replicable authentication system that can detect an attacker who even knows the digital key is required. This study aims to propose an enhanced solution to the physical layer authentication problem in the case in which the attacker can reproduce the hopping pattern. The scenario of the problem is shown in Figure 1. It is assumed that the user, attacker, and receiver exist in the FHSS network. The goal of the attacker is to deceive the receiver by emitting the imitated FH signal based on the replicated hopping pattern. The primary goal of the receiver is to decide if the signal received came from the user or from the attacker. The novel receiver algorithm we propose in this study is an RF fingerprinting-based emitter identification (RFEI) method that targets the physical layer of the FHSS network. By examining the emitter ID on the received FH signal, the receiver can decide if the current FH signal is emitting from one of the allowed users. If the emitter ID of the current FH signal is not included in the set of authenticated user IDs, the receiver can reject the current FH signal before it is passed to the MAC layer. The RFEI method can achieve system enhancement by being applied to the user authentication process. As the key of the RFEImethod, that is, the SF, is generated by the process tolerances during the manufacturing process, the attacker cannot reproduce it. By detecting these attackers based on the SFs, non-replicable authentication systems can be achieved wherein the receiver can reject FH signals even if an attacker knows the hopping pattern and the digital key. The RFEI method consists of four steps: SF extraction (SFE, Section 3.1), time-frequency feature extraction (TFFE, Section 3.2), user emitter classification (UEC, Section 3.3), and attacker emitter detection (AED, Section 3.4). As a preprocessing step, the target hop signal is down-converted to the baseband based on the hopping pattern known to the receiver. The baseband hop signal is passed to the SFE step to extract the analog SFs, i.e., rising transient (RT), steady state (SS), and falling transient (FT) signals are extracted. The SF is provided to the TFFE step to transform the SF into the time-frequency domain, i.e., the spectrogram. The spectrogram is provided to the UEC stage to train and test the spectrogram on a custom deep inception network (DIN)-based classifier. In addition, the ensemble approach is applied to exploit the multimodality of the analog SFs. Finally, the classifier output vector is provided to the AED step in which a detection algorithm is applied to detect the FH signal of the attacker. The novelties of this study are that (1) RF fingerprinting methods were evaluated targeting for FH signals, (2) the ensemble approach was applied to utilize the multimodality of SFs, and (3) the RFEI framework was employed to identify users and detect attackers simultaneously. The RFEI algorithm was evaluated on a few SFs and ensemble-based approaches. The algorithm compares to well-designed baselines inspired by recent approaches described in the RF fingerprinting literature [4; 5; 7; 8].