Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control

Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects

Stationary, Oscillatory, Spatio-Temporal Patterns and Existence of Global Solutions in Reaction-Diffusion Models of Three Species

The goal of my Ph.D. research is to analyze three species models in order to describe the behavior of an ecological community. In particular, two reaction-diffusion systems describing different local interactions between three species have been considered to obtain species coexistence, diversity, and distribution patterns. The first analyzed model describes intraguild predation: there are an IG-predator species, an IG-prey species, and a common resource species, which is shared by both of them. The IGP interaction is of Lotka-Volterra type, coupled with nonlinear diffusion, since we assume that the IG-prey moves towards lower density areas of the IG-predator. In this model, the extinction of species has been surveyed. Performing the linear stability analysis in the neighborhood of the coexistence point, the conditions for the occurrence of Hopf instability have been established. Cross-diffusion is able to induce Turing instability for this system, which would not admit this bifurcation in presence of only classical diffusion terms. Moreover, the effect of each parameter on Turing and Turing-Hopf instability has been detected. Numerical solutions of the system have been computed using spectral method, showing the rich dynamics of the model, including the Turing pattern, time oscillation pattern, Turing-Hopf pattern, and chaotic behavior. The weakly nonlinear analysis also has been employed to predict the amplitude of patterned solutions have been compared with numerical spectral solutions of the reaction-diffusion system. Furthermore, we have used multiscale methods to determine normal form of the model around Turing-Hopf codimension-2 points. Finally, by utilizing the fixed point argument and energy estimate, the existence of the global solution to the system has been established, assuming some conditions on initial data. The second three species model describes the dynamics of two predators competing with each other to feed on the same prey. The functional response of predators is the Holling type. This local dynamics has been coupled with linear cross-diffusion terms taking into account the movement of each species towards lower-density areas of the other species. We have applied linear analysis of the system with and without diffusion to obtain the necessary conditions of stability and the occurrence of Hopf and Turing instability. In particular, weakly nonlinear analysis, Turing regions, and maximum growth rate have been investigated. Using a numerical finite elements method, Turing patterns have also been displayed and compared with WNL solutions. Finally, to prove the existence of global in time of the solutions, a rectangular invariant method has been presented for a particular case

Emerging Role of Enhancer RNAs as Potential Diagnostic and Prognostic Biomarkers in Cancer

Enhancers are distal cis-acting elements that are commonly recognized to regulate gene expression via cooperation with promoters. Along with regulating gene expression, enhancers can be transcribed and generate a class of non-coding RNAs called enhancer RNAs (eRNAs). The current discovery of abundant tissue-specific transcription of enhancers in various diseases such as cancers raises questions about the potential role of eRNAs in disease diagnosis and therapy. This review aimed to demonstrate the current understanding of eRNAs in cancer research with a focus on the potential roles of eRNAs as prognostic and diagnostic biomarkers in cancers