spectrum sensing

We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson-Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions.

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This dataset accurately models the internal behavior of an IoT spectrum sensor (belonging to the ElectroSense platform and consisting of a Raspberry Pi 3 with a software-defined radio kit) when it is functioning normally and under attack. To accomplish it, the system calls of the IoT sensor are monitored under normal behavior, gathered, cleaned, and stored in a centralized directory. Then, the device is infected with current malware affecting IoT devices, such as the Bashlite botnet, Thetick backdoor, Bdvl rootkit, and a Ransomware proof of concept.

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MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U.

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The MATLAB program provides the performance of a sliding window based detection for a pulse radar signal.

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The MATLAB program provides the performance of a sliding window based detection for a pulse radar signal.

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