A graph mining approach for detecting unknown malwares

Nowadays malware is one of the serious problems in the modern societies. Although the signature based malicious code detection is the standard technique in all commercial antivirus softwares, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new malwares (unknown malwares). Since most of malwares have similar behavior, a behavior based method can detect unknown malwares. The behavior of a program can be represented by a set of called API's (application programming interface). Therefore, a classifier can be employed to construct a learning model with a set of programs' API calls. Finally, an intelligent malware detection system is developed to detect unknown malwares automatically. On the other hand, we have an appealing representation model to visualize the executable files structure which is control flow graph (CFG). This model represents another semantic aspect of programs. This paper presents a robust semantic based method to detect unknown malwares based on combination of a visualize model (CFG) and called API's. The main contribution of this paper is extracting CFG from programs and combining it with extracted API calls to have more information about executable files. This new representation model is called API-CFG. In addition, to have fast learning and classification process, the control flow graphs are converted to a set of feature vectors by a nice trick. Our approach is capable of classifying unseen benign and malicious code with high accuracy. The results show a statistically significant improvement over n-grams based detection method.     

Order‐reduction of parabolic PDEs with time‐varying domain using empirical eigenfunctions

A novel methodology for the order‐reduction of parabolic partial differential equation (PDE) systems with time‐varying domain is explored. In this method, a mapping functional is obtained, which relates the time‐evolution of the solution of a parabolic PDE with time‐varying domain to a fixed reference domain, while preserving space invariant properties of the initial solution ensemble. Subsequently, the Karhunen–Loève decomposition is applied to the solution ensemble on fixed spatial domain resulting in a set of optimal eigenfunctions. Further, the low dimensional set of empirical eigenfunctions is mapped on the original time‐varying domain by an appropriate mapping, resulting in the basis for the construction of the reduced‐order model of the parabolic PDE system with time‐varying domain. This methodology is used in three representative cases, one‐ and two‐dimensional (1‐D and 2‐D) models of nonlinear reaction‐diffusion systems with analytically defined domain evolutions, and the 2‐D model of the Czochralski crystal growth process with nontrivial geometry. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4142–4150, 2013     

An Efficient Finite Difference Method for The Time‐Delay Optimal Control Problems With Time‐Varying Delay

In this paper, an efficient finite difference method is presented for the solution of time‐delay optimal control problems with time‐varying delay in the state. By using the Pontryagin's maximum principle, the original time‐delay optimal control problem is first transformed into a system of coupled two‐point boundary value problems involving both delay and advance terms. Then the derived system is converted into a system of linear algebraic equations by using a second‐order finite difference formula and a Hermite interpolation polynomial for the first‐order derivatives and delay terms, respectively. The convergence analysis of the proposed approach is provided. The new scheme is also successful for the optimal control of time‐delay systems affected by external persistent disturbances. Numerical examples are included to demonstrate the validity and applicability of the new technique. Some comparative results are included to illustrate the effectiveness of the proposed method.     

Application of genetic algorithm to computer-aided process planning in preliminary and detailed planning

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.     

Comparison of antireflection surfaces based on two-dimensional binary gratings and thin-film coatings

A comparison of antireflection surfaces based on the two-dimensional binary gratings and thin-film coatings is presented. First, a two-dimensional hybrid binary grating is proposed and analyzed by use of a vector-based implementation of the rigorous coupled-wave analysis method. The optimum parameters of the structure are determined and the effects that changing them have on spectral characteristics of the structure are studied. Then this structure is compared with multilayer thin-film antireflection filters. These filters are designed by genetic algorithm and needle methods, which are powerful methods for multilayer filter design. The comparison results show that the sensitivity of the grating to changes in the incident wavelength is high. However, a reflectance of the order of 10(-3)% at the design wavelength can be achieved. The sensitivity of designed antireflection thin-film filters to wavelength changes is lower, however, and the minimum achievable reflectance is higher.     

Hierarchy-Debug: a scalable statistical technique for fault localization

Considering the fact that faults may be revealed as undesired mutual effect of program predicates on each other, a new approach for localizing latent bugs, namely Hierarchy-Debug, is presented in this paper. To analyze the vertical effect of predicates on each other and on program termination status, the predicates are fitted into a logistic lasso model. To support scalability, a hierarchical clustering algorithm is applied to cluster the predicates according to their presence in different executions. Considering each cluster as a pseudo-predicate, a distinct lasso model is built for intermediate levels of the hierarchy. Then, we apply a majority voting technique to score the predicates according to their lasso coefficients at different levels of the hierarchy. The predicates with relatively higher scores are ranked as fault relevant predicates. To provide the context of failure, faulty sub-paths are identified as sequences of fault relevant predicates. The grouping effect of Hierarchy-Debug helps programmers to detect multiple bugs. Four case studies have been designed to evaluate the proposed approach on three well-known test suites, Space, Siemens, and Bash. The evaluations show that Hierarchy-Debug produces more precise results compared with prior fault localization techniques on the subject programs.     

Effect of process variables on product yield distribution in thermal catalytic cracking of naphtha to light olefins over Fe/HZSM-5

The effect of temperature, WHSV and Fe loading over HZSM-5 catalyst in thermal-catalytic cracking (TCC) of naphtha for the production of light olefins has been studied. The response surface defined by three most significant parameters is obtained from Box-Behnken design method and the optimal parameter set is found. The results show that ethylene increases with temperature, while propylene shows an optimum at 650 °C. Moderate WHSV is favorable for maximum production of light olefins. Addition of Fe to HZSM-5 has a favorable effect on the production of light olefins up to 6% of loading. Excess amount of loading decreases the conversion of naphtha, which leads to a drop in light olefin yields. The yield of light olefins (ethylene and propylene) at 670 °C, 44 hr⁻¹ and 6 wt% Fe has been increased to 5.43 wt% compared to the unmodified HZSM-5 and reaches to 42.47 wt%.     

Improving the mechanical,thermal and electrical properties of polyurethane- graphene oxide nanocomposites synthesized by in-situ polymerization of ester-based polyol with hexamethylene diisocyanate

Many polyols or diols have been used for the synthesis of polyurethanes (PU), however, to the best of our knowledge, PU-graphene oxide (GO) nanocomposites synthesized with ester-based polyols have been rarely studied. In this work ester-based polyol synthesized by the reaction of adipic acid and 1,4 butane diol, was in-situ polymerized with hexamethylene diisocyanate (HDI) and GO to prepare PU-GO nanocomposites. The content of GO was changed from 1 to 2.5 wt% and its effect on the mechanical, thermal and electrical properties of the samples were examined. The presence of GO more than 1.5% in the nanocomposites resulted in brittle samples and reduced the tensile strength, however, the Young’s modulus of the samples containing 1 and 1.5% was increased to 11 and 12.08-fold (275 and 302 MPa) compared to the neat PU (25 MPa), respectively. The shore A hardness of the samples was increased from 86 for PU to 96 for PUGO-1.5. The abrasion resistance of the samples was decreased by increasing the GO content. Results of the thermogravimetric analysis showed that higher amounts of GO increase the thermal stability of the samples. The chemical and physical interactions between the surface of GO nanolayers and the PU chains were confirmed by FTIR spectroscopy. The dynamic mechanical analysis of the samples showed that GO nanolayers decreased the molecular motions of the PU chains in the nanocomposites which were noticed by shifting the glass transition to the higher temperatures.     

Second-order data obtained from differential pulse voltammetry: Determination of tryptophan at a gold nanoparticles decorated multiwalled carbon nanotube modified glassy carbon electrode

Three-way data obtained from different pulse heights of differential pulse voltammetry (DPV) was analyzed using multivariate curve resolution by alternating least squares (MCR-ALS) algorithm. Differential pulse voltammograms of tryptophan were recorded at a gold nanoparticles decorated multiwalled carbon nanotube modified glassy carbon electrode (GCE/MWCNTs-nanoAu). The determination of tryptophan was performed even in the presence of unexpected electroactive interference(s). Both the simulated and experimental data were non-bilinear. Therefore a potential shift algorithm was used to correct the observed shift in the data. After correction, the data was augmented and MCR-ALS was applied to the augmented data. A relative error of prediction of less than 8% for the determination of the simulated analyte of interest and tryptophan in synthetic samples indicated that the methodology employing voltammetry and second-order calibration could be applied to complex analytical systems.     

Adaptive robust control of uncertain systems with state and input delay

In this paper, adaptive robust control of uncertain systems with multiple time delays in states and input is considered. It is assumed that the parameter uncertainties are time varying norm-bounded whose bounds are unknown but their functional properties are known. To overcome the effect of input delay on the closed loop system stability, new Lyapunov Krasovskii functional will be introduced. It is shown that the proposed adaptive robust controller guarantees globally uniformly exponentially convergence of all system solutions to a ball with any certain convergence rate. Moreover, if there is no disturbance in the system, asymptotic stability of the closed loop system will be established. The proposed design condition is formulated in terms of linear matrix inequality (LMI) which can be easily solved by LMI Toolbox in Matlab. Finally, an illustrative example is included to show the effectiveness of results developed in this paper.