ECFGM: enriched control flow graph miner for unknown vicious infected code detection

Vicious codes, especially viruses, as a kind of impressive malware have caused many disasters and continue to exploit more vulnerabilities. These codes are injected inside benign programs in order to abuse their hosts and ease their propagation. The offsets of injected virus codes are unknown and their targets usually are latent until they are executed and activated, what in turn makes viruses very hard to detect. In this paper enriched control flow graph miner, ECFGM in short, is presented to detect infected files corrupted by unknown viruses. ECFGM uses enriched control flow graph model to represent the benign and vicious codes. This model has more information than traditional control flow graph (CFG) by utilizing statistical information of dependent assembly instructions and API calls. To the best of our knowledge, the presented approach in this paper, for the first time, can recognize the offset of infected code of unknown viruses in the victim files. The main contributions of this paper are two folds: first, the presented model is able to detect unknown vicious code using ECFG model with reasonable complexity and desirable accuracy. Second, our approach is resistant against metamorphic viruses which utilize dead code insertion, variable renaming and instruction reordering methods.     

Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.     

Preparation and characterization of poly(vinyl alcohol)/gum tragacanth/cellulose nanocomposite film

The effects of gum tragacanth obtained from two species of Astragalus Gossypinus (GT-G) and A. Parrowianus (GT-P) at two levels of 10% and 30% combined with cellulose nanofibers (CNF; 5%) on the physico-mechanical and structural properties of polyvinyl alcohol (PVA) nanocomposite film were investigated in this study. The water solubility and water vapor permeability of the films decreased with increasing the content of both gums, especially in the film containing 30% GT-P. The highest values of the tensile strength (39.3 MPa) and elongation at break (445%) belonged to the treatment containing 10% GT-P (90/10P/0). The FTIR and DSC analyses confirmed good interactions between GT and PVA in the 90/10P/0 treatment. SEM images indicated the dense structure of this film as the optimum treatment. Although the presence of CNF in the films containing GT-G improved some properties, especially the Young modulus, it impaired all the functional properties of nanocomposite GT-P film.     

Analysis and forecasting of IPO underpricing

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an important parameter in decisions of investors and issuers. Considering the probability of offering withdrawal facilitates more precise estimation of underpricing. In this paper, the effective factors on probability of IPO withdrawal and underpricing in Tehran Stock Exchange have been characterized using regression, and then neural network is applied to estimate the probability of IPO withdrawal and underpricing. To evaluate the performance of our applied method, fuzzy regression is employed and compared with neural network. According to the obtained empirical results, neural network demonstrates better accuracy than fuzzy regression. The results indicate that there is a meaningful relationship between underpricing and probability of withdrawal, and the probability of IPO withdrawal plays an important role in precise evaluation of underpricing.     

Transient growth of a micro-void in an infinite medium under thermal load with modified Zerilli–Armstrong model

In this paper, the transient growth of a spherical micro-void under remote thermal load in an infinite medium is investigated. After developing the governing equations in the problem domain, the coupled nonlinear set of equations is solved through a numerical scheme. It is shown that a small cavity can grow rapidly as the temperature increases in a remote distance and may damage the material containing preexisting micro-voids. Conducting a transient thermal analysis simultaneously with a structural one reveals that the material may experience a peak in the radial stress distribution, which is five times larger compared to the steady-state one, and shows the importance of employing a time-dependent approach in this problem. Furthermore, utilizing a sensible yield criterion, i.e., the modified Zerilli–Armstrong model, discloses that there is a large discrepancy in the results assuming perfectly plastic constitutive model. It is verified that the obtained results do not violate the proportional loading conditions that is the basis for development of the governing formulation in this work. The monotonic alteration of the plastic strain components versus time proves that we do not encounter any elastic unloading during the void growth, which is a basic assumption in the present work. Some numerical examples are also presented to investigate the features of the presented model.     

Integrated Inspection Scheduling and Maintenance Planning for Infrastructure Systems

Operation and maintenance of an infrastructure system rely on information collected on its components, which can provide the decision maker with an accurate assessment of their condition states. However, resources to be invested in data gathering are usually limited and observations should be collected based on their Value of Information (VoI). Assessing the VoI is computationally intractable for most applications involving sequential decisions, such as long‐term infrastructure maintenance. In this article, we propose an approach for integrating adaptive maintenance planning based on Partially Observable Markov Decision Process (POMDP) and inspection scheduling based on a tractable approximation of VoI. Two alternative myopic approaches, namely pessimistic and optimistic, are introduced, and compared theoretically and by numerical examples.     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.     

ERES: an extended regular expression signature for polymorphic worm detection

Journal of Computer Virology and Hacking Techniques - The quick spreading of modern sophisticated polymorphic worms poses a serious threat to the internet security. So far, several signature...     

Hybrid solar parabolic dish power plant and high‐temperature phase change material energy storage system

In this study, a conventional steam power plant with two regenerative boilers is considered, and one of its boilers is replaced with parabolic solar dish collectors and storing the produced thermal energy by the phase change material (PCM) in a storage tank. The results show the necessity of the existence of an auxiliary fired‐gas boiler to provide constant load during the whole 24 hours. The performance of the proposed hybridized system is evaluated through energy and exergy analyses. It was demonstrated that substituting solar collectors with one of the boilers marginally lowers the energy efficiency but increases the exergy efficiency of the whole power plant up to 41.76%. Moreover, it is found out that this hybridization decreases the total irreversibility of the power plant in comparison with the base case, from 51.1 to 47.2 MW. The parametric analysis states that raising the mass flow rate of the heat transfer fluid in the solar collectors not only enhances the system performance but also increases the volume of the PCM tank.