Physical layer authentication using ensemble learning technique in wireless communications

Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect cyber-attacks. Still, the cyber-physical systems (CPS) have relatively large computational requirements and require more communication resources, thus making it impossible to achieve a low latency target. These methods perform well but only in stationary scenarios. We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios. The features are fed to ensemble learning algorithms, such as AdaBoost, LogitBoost and Gentle Boost, to classify data. The authentication of the received signal is considered a binary classification problem. The transmitted data is labeled as legitimate information, and spoofing data is illegitimate information. Therefore, this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks. It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile. The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time

Vascular and renal changes associated with hypertension in adult male rats exposed to maternal smoking during pregnancy

Bibliography: p. 85-94Some pages are in colour

Coping with extremes: the rumen transcriptome and microbiome co-regulate plateau adaptability of Xizang goat

Abstract The interactions between the rumen microbiota and the host are crucial for the digestive and absorptive processes of ruminants, and they are heavily influenced by the climatic conditions of their habitat. Owing to the harsh conditions of the high-altitude habitat, little is known about how ruminants regulate the host transcriptome and the composition of their rumen microbiota. Using the model species of goats, we examined the variations in the rumen microbiota, transcriptome regulation, and climate of the environment between high altitude (Lhasa, Xizang; 3650 m) and low altitude (Chengdu, Sichuan, China; 500 m) goats. The results of 16 S rRNA sequencing revealed variations in the abundance, diversity, and composition of rumen microbiota. Papillibacter, Quinella, and Saccharofermentans were chosen as potential microbes for the adaptation of Xizang goats to the harsh climate of the plateau by the Spearman correlation study of climate and microbiota. Based on rumen transcriptome sequencing analysis, 244 genes were found to be differentially expressed between Xizang goats and low-altitude goats, with 127 genes showing up-regulation and 117 genes showing down-regulation. SLC26A9, GPX3, ARRDC4, and COX1 were identified as potential candidates for plateau adaptation in Xizang goats. Moreover, the metabolism of fatty acids, arachidonic acids, pathway involving cytokines and their receptors could be essential for adaptation to plateau hypoxia and cold endurance. The expression of GPX3, a gene linked to plateau acclimatization in Xizang goats, was linked to the abundance of Anaerovibrio, and the expression of SLC26A9 was linked to the quantity of Selenomonas, according to ruminal microbiota and host Spearman correlation analysis. Our findings imply that in order to adapt harsh plateau conditions, Xizang goats have evolved to maximize digestion and absorption as well as to have a rumen microbiota suitable for the composition of their diet