Physical-Layer Authentication Using Channel State Information and Machine Learning

Strong authentication in an interconnected wireless environment continues to be an important, but sometimes elusive goal. Research in physical-layer authentication using channel features holds promise as a technique to improve network security for a variety of devices. We propose the use of machine learning and measured multiple-input multiple-output communications channel information to make a decision on whether or not to authenticate a particular device. This work analyzes the use of received channel state information from the wireless environment and demonstrates the employment of a generative adversarial neural network (GAN) trained with received channel data to authenticate a transmitting device. We compared a variety of machine learning techniques and found that the local outlier factor (LOF) algorithm reached 100% accuracy at lower signal to noise ratios (SNR) than other algorithms. However, before LOF reached 100%, we also show that the GAN was more accurate at lower SNR levels.Comment: Submitted to 14th International Conference on Signal Processing and Communication Systems (ICSPCS) 202

A signature-based data security and authentication framework for internet of things applications

Internet of things (IoT) is the next big revolution in modernized network technologies connecting a massive number of heterogeneous smart appliances and physical objects. Owing to these technologies' novelty, various issues are characterized by security concerns are the most prioritized issue. A review of existing security approaches highlights that they are very particular about the solution towards a specific attack and cannot resist any unknown attacker. Therefore, this manuscript presents a novel computational model that introduces a unique authentication process using a simplified encryption strategy. The simulated study outcome shows that the proposed system offers efficient security and efficient data transmission performance in the presence of an unknown adversary. Hence, the study outcome exhibits better effects than frequently used security solutions when implemented in a vulnerable IoT environment