A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.

Today's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

Tunable control of antibody immobilization using electric field.

The controlled immobilization of proteins on solid-state surfaces can play an important role in enhancing the sensitivity of both affinity-based biosensors and probe-free sensing platforms. Typical methods of controlling the orientation of probe proteins on a sensor surface involve surface chemistry-based techniques. Here, we present a method of tunably controlling the immobilization of proteins on a solid-state surface using electric field. We study the ability to orient molecules by immobilizing IgG molecules in microchannels while applying lateral fields. We use atomic force microscopy to both qualitatively and quantitatively study the orientation of antibodies on glass surfaces. We apply this ability for controlled orientation to enhance the performance of affinity-based assays. As a proof of concept, we use fluorescence detection to indirectly verify the modulation of the orientation of proteins bound to the surface. We studied the interaction of fluorescently tagged anti-IgG with surface immobilized IgG controlled by electric field. Our study demonstrates that the use of electric field can result in more than 100% enhancement in signal-to-noise ratio compared with normal physical adsorption.

Effect of solution concentration, surface bias and protonation on the dynamic response of amplitude-modulated atomic force microscopy in water.

The dynamic response of amplitude-modulated atomic force microscopy (AM-AFM) is studied at the solid/water interface with respect to changes in ionic concentration, applied surface potential, and surface protonation. Each affects the electric double layer in the solution, charge on the tip and the sample surface, and thus the forces affecting the dynamic response. A theoretical model is developed to relate the effective stiffness and hydrodynamic damping of the AFM cantilever that is due to the tip/surface interaction with the phase and amplitude signals measured in the AM-AFM experiments. The phase and amplitude of an oscillating cantilever are measured as a function of tip-sample distance in three experiments: mica surface in potassium nitrate solutions with different concentrations, biased gold surface in potassium nitrate solution, and carboxylic acid-terminated self-assembled monolayers (SAMs) on gold in potassium nitrate pH buffers. Results show that, over the range where the higher harmonic modes of the oscillation are negligible, the effective stiffness of the AFM cantilever increases to a maximum as the tip approaches the surface before declining again as a result of the repulsive electrical double layer interaction. For attractive electrical double-layer interactions, the effective stiffness declines monotonically as the tip approaches the surface. Similarly, the hydrodynamic damping of the tip increases and then decreases as the tip approaches the solid/water interface, with the magnitude depending on the species present in the solution.

Double transfer printing of small volumes of liquids.

We report here a technique to print small volumes of liquid on a hydrophobic substrate. This process is based on the control of the critical parameters that govern a quasi-equilibrium liquid transfer process from one surface to another. We present a qualitative model that describes the physics of a transfer printing process between hydrophobic surfaces, and we use the parameters outlined in this model to manipulate the amount of liquid transferred between surfaces. We demonstrate the printing of discrete, small volumes (approximately 70 fL) of different liquid inks on a polymer substrate starting with volumes that are 8 orders of magnitude larger (a droplet of approximately 10 microL) in a simple two-step procedure.