Positioning remains a crucial aspect with wide-ranging applications, despite the availability of various solutions and extensive research. The lack of transparency surrounding the infrastructure topology and precise locations of base stations operated by telecommunication companies further complicates the positioning process. Moreover, as wireless networks continue to evolve with advancements like 5G and 6G, accurate positioning becomes increasingly important, leveraging vast amounts of data and artificial intelligence techniques. This paper proposes a Deep Neural Network (DNN) based approach called DSP for accurately determining the position of telecommunication operator serving cells in outdoor spaces. The proposed method incorporates fingerprint data collection, machine learning algorithms, and big data analysis to enhance accuracy and expand the range of evaluatable parameters. The data was collected and labeled using the drive test method. By employing deep learning techniques, the proposed method successfully predicts the location of the base station connected to the user device. Through effective data preprocessing and optimized hyperparameters, the average distance error was reduced to 20 meters.

Recent studies reveal that an adversary might trace the apparently insignificant traffic rate of source nodes over the net and turn such data to invaluable information so as to breach the privacy of the victim sources. Inhibiting the adversary of being able to extract information from the traffic rate of source nodes is a complicated task unless taking into consideration the flow conservation law effect of the transmitter queue. A reliable method of preserving the rate privacy that copes with the flow conservation law is to transmit original packets augmented with probabilistically dummy ones so as to change the observable aggregated traffic rate. Augmenting dummy packets, however, bears redundancy, and hence, requires extra resources in terms of bandwidth and buffer requirements, and more importantly suggests higher transmitting energy consumption. Grounded on the queueing and information theories, in this paper, we present an efficient method that minimally augments dummy packets to preserve the source rate privacy at a given degree while preserving the delay distribution of the original packets intact, and thus does not affect the quality of service parameters of the transmitted data in terms of delay and jitter. The presented method models the original packets and dummy ones with a preemptive resume 2-priority queueing system and then using information theory attempts to maximize the Fano lower bound of the best estimation of the adversary's speculation. All of the theoretically obtained results have been validated by conducting simulation experiments.

Multi-dimensional spatial analysis of image pixels have not been much investigated for the steganalysis of the LSB Steganographic methods. Pixel distribution based steganalysis methods could be thwarted by intelligently compensating statistical characteristics of image pixels, as reported in several papers. Simple LSB replacement methods have been improved by introducing smarter LSB embedding approaches, e.g. LSB matching and LSB+ methods, but they are basically the same in the sense of the LSB alteration. A new analytical method to detect LSB stego images is proposed in this paper. Our approach is based on the relative locations of image pixels that are essentially changed in an LSB embedding system. Furthermore, we introduce some new statistical features including “local entropies sum” and “clouds min sum” to achieve a higher performance. Simulation results show that our proposed approach outperforms some well-known LSB steganalysis methods, in terms of detection accuracy and the embedding rate estimation.