HDM-Analyser: a hybrid analysis approach based on data mining techniques for malware detection

Today’s security threats like malware are more sophisticated and targeted than ever, and they are growing at an unprecedented rate. To deal with them, various approaches are introduced. One of them is Signature-based detection, which is an effective method and widely used to detect malware; however, there is a substantial problem in detecting new instances. In other words, it is solely useful for the second malware attack. Due to the rapid proliferation of malware and the desperate need for human effort to extract some kinds of signature, this approach is a tedious solution; thus, an intelligent malware detection system is required to deal with new malware threats. Most of intelligent detection systems utilise some data mining techniques in order to distinguish malware from sane programs. One of the pivotal phases of these systems is extracting features from malware samples and benign ones in order to make at least a learning model. This phase is called “Malware Analysis” which plays a significant role in these systems. Since API call sequence is an effective feature for realising unknown malware, this paper is focused on extracting this feature from executable files. There are two major kinds of approach to analyse an executable file. The first type of analysis is “Static Analysis” which analyses a program in source code level. The second one is “Dynamic Analysis” that extracts features by observing program’s activities such as system requests during its execution time. Static analysis has to traverse the program’s execution path in order to find called APIs. Because it does not have sufficient information about decision making points in the given executable file, it is not able to extract the real sequence of called APIs. Although dynamic analysis does not have this drawback, it suffers from execution overhead. Thus, the feature extraction phase takes noticeable time. In this paper, a novel hybrid approach, HDM-Analyser, is presented which takes advantages of dynamic and static analysis methods for rising speed while preserving the accuracy in a reasonable level. HDM-Analyser is able to predict the majority of decision making points by utilising the statistical information which is gathered by dynamic analysis; therefore, there is no execution overhead. The main contribution of this paper is taking accuracy advantage of the dynamic analysis and incorporating it into static analysis in order to augment the accuracy of static analysis. In fact, the execution overhead has been tolerated in learning phase; thus, it does not impose on feature extraction phase which is performed in scanning operation. The experimental results demonstrate that HDM-Analyser attains better overall accuracy and time complexity than static and dynamic analysis methods.     

Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.     

Zero-Degree algorithm for Internet GateWay placement in backbone wireless mesh networks

Internet GateWays (IGWs) are responsible for connecting the backbone wireless mesh networks (BWMNs) to the Internet/wired backbone. An IGW has more capabilities than a simple wireless mesh router (WMR) but is more expensive. Strategically placing the IGWs in a BWMN is critical to the Wireless Mesh Network (WMN) architecture. In order to solve the problem of IGWs placement in BWMNs, a novel algorithm is proposed in this paper. The new algorithm is involved in placing a minimum number of IGWs so that the Quality of Service (QoS) requirements are satisfied. Different from existing algorithms, this new algorithm incrementally identifies IGWs and prioritively assigns wireless mesh routers (WMRs) based on the computed degree of WMRs to identified IGWs. Performance evaluation results show that proposed algorithm outperforms other alternative algorithms by comparing the number of gateways placed in different scenarios. Furthermore, having control of the distribution of IGWs in order to locate them closest to available Internet/wired network connection points is an added advantage of this algorithm.     

Steganalysis and payload estimation of embedding in pixel differences using neural networks

In this paper a steganalysis technique is proposed for pixel-value differencing method. This steganographic method, which is immune against conventional attacks, performs the embedding in the difference of the values of pixel pairs. Therefore, the histogram of the differences of an embedded image is different as compared with a cover image. A number of characteristics are identified in the difference histogram that show meaningful alterations when an image is embedded. Five distinct multilayer perceptrons neural networks are trained to detect different levels of embedding. Every image is fed to all networks and a voting system categorizes the image as stego or cover. The implementation results indicate 88.6% success in correct categorization of the test images that contained more than 20% embedding. Furthermore, using a neural network an estimator is presented which gives an estimate of the amount of the MPVD embedding in an image. Implementation of the estimator showed an average accuracy of 88.3% in the estimation of the amount of embedding.     

A numerical method for solving a fractional partial differential equation through converting it into an NLP problem

In this paper, we propose a new approach for solving fractional partial differential equations, which is very easy to use and can also be applied to equations of other types. The main advantage of the method lies in its flexibility for obtaining the approximate solutions of time fractional and space fractional equations. Using this approach, we convert a fractional partial differential equation into a nonlinear programming problem. Several numerical examples are used to demonstrate the effectiveness and accuracy of the method.     

A voltage-dependent channel length extraction method for MOSFET’s

In this paper a new method for extraction of the channel length and channel resistance as a function of gate-voltage in MOSFET’s is introduced. The method is accurate and calculates the threshold voltages of all devices with different gate-lengths. The channel resistance is divided in two parts; the first part is a function of gate-voltage and threshold voltage difference (V_g − V_t) and the second part is only a function of gate-voltage. Further, the model determines the threshold voltage of short-channel devices independent of their parasitic resistances and implements the channel mobility as an arbitrary function of gate-voltage while the gate-voltage-dependent part of the resistance is uniquely separated from the first part of channel resistance for all devices.     

Effects of additives and temperature on dissolution rate and diffusivity of lime in Al2O3-CaO-SiO2 based slags

The dissolution rate of dense lime specimens in calcium aluminosilicate based melts was measured at 1430 °C to 1600 °C in air, using a rotating disk/cylinder technique. The measured dissolution rates were strongly dependent on the rotation speed with the results indicating mass transfer in the slag phase to be a rate-limiting step. At a given rotation speed, the slag chemistry and temperature had strong effects on the dissolution rate. The diffusivity of CaO in the slag was calculated from the dissolution rate and solubility data, using known mass-transfer correlations. Addition of CaF₂ MnO _x , FeO _x , and TiO₂ to the slag increased the CaO diffusivity, while SiO₂ had an opposite effect. Addition of CaF₂ had the strongest effect and increased the diffusivity by a factor of 3 to 5 in the temperature range of 1500 °C to 1600 °C. The deduced activation energy for diffusion of CaO in these slags ranged from about 53 to 246 kJ/mole, depending on the concentration of additives used.     

Omnidirectional broadband absorption enhancement in laterally assembled quadrangle silicon nanowire solar cells

In this paper, effects of geometry and spatial placement of quadrangle silicon nanowires (NWs) on the performance of laterally assembled NW solar cells are investigated. Two different approaches are proposed to reach the broadband absorption enhancement in these types of solar cells. In the first method, a semi-periodic array is used to have a better utilization of the optical antenna effect. The current density can be enhanced as a result of a compromise between the diffraction and optical antenna effects. In the second approach, multiple NWs with different geometries are employed to use the cavity modes of the various NWs. The best current density is achieved for the combination of both of these methods. The different parameters in each structure are selected using the optimization algorithm. Finally, a multilayer structure with the optimized dimensions is proposed to obtain the maximum achievable current density in ultrathin solar cells. The broadband absorption enhancement in the proposed structure is preserved for a wide range of incident angles. Using the multilayer NW arrays, it is possible to improve the absorption enhancement of solar cells without introducing more absorbing material.     

Additive manufacturing management: a review and future research agenda

Based on the phenomenal changes that additive manufacturing (AM) has brought to industries and markets, managerial approaches should be re-examined and developed to take advantage of emerging opportunities. This revolutionary technology is dramatically changing business and innovation models, shrinking supply chains and altering the global economy. For example, 3D printing shifts production locations closer to customers and leads to free-form product design as well as sustainable manufacturing. Several studies have been conducted on AM technology, but the research stream on AM management is still developing, with studies dispersed in journals across different research areas. Our study presents both systematic and quantitative analyses of the literature, including co-citation analysis, factor analysis and multidimensional scaling, to explore the structure of the AM research domains in the scope of management, business and economics. We found eight main research streams: AM technology selection, supply chain, product design and production cost models, environmental aspects, strategic challenges, manufacturing systems, open-source innovation and business models and economics. Finally, based on the results of our in-depth analysis of the literature, we found nine promising future research directions.     

Characterization of $${\hbox {BaTiO}_{3}}$$ piezoelectric perovskite material for multilayer actuators

In this study, we present the results of the manufacturing of \(\hbox {BaTiO}_{3}\) powder, which is meant for use in stacked-disk multilayer actuator production. The solid-state technique was used for powder preparation. The properties of barium titanate material, at each stage of its fabrication (powder, granulate, sintered material), influencing on its application for the stacked-disk multilayer actuator were determined. Particularly, the four parameters of \(\hbox {BaTiO}_{3}\) sinter affecting on the usability properties of actuators, not found before in the literature, were estimated. Parameters characterizing the extent of material sintering, SEM microstructures and electric properties of the fabricated pellets are presented and discussed. The dilatometric curve was executed using the high temperature dilatometer to determine at which temperature barium titanate pellets and beams should be sintered to receive full dense sinters. Parameters characterizing the extent of material sintering: the apparent density, the apparent porosity and the water absorbability were estimated. Finally, the problem of metal layer deposition on barium titanate ceramics during actuator fabrication is considered.