PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection.

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Electrical Characterization of Germanium Nanowires Using a Symmetric Hall Bar Configuration: Size and Shape Dependence.

The fabrication of individual nanowire-based devices and their comprehensive electrical characterization remains a major challenge. Here, we present a symmetric Hall bar configuration for highly p-type germanium nanowires (GeNWs), fabricated by a top-down approach using electron beam lithography and inductively coupled plasma reactive ion etching. The configuration allows two equivalent measurement sets to check the homogeneity of GeNWs in terms of resistivity and the Hall coefficient. The highest Hall mobility and carrier concentration of GeNWs at 5 K were in the order of 100 cm2/(Vs) and 4×1019cm-3, respectively. With a decreasing nanowire width, the resistivity increases and the carrier concentration decreases, which is attributed to carrier scattering in the region near the surface. By comparing the measured data with simulations, one can conclude the existence of a depletion region, which decreases the effective cross-section of GeNWs. Moreover, the resistivity of thin GeNWs is strongly influenced by the cross-sectional shape.

Demonstration of a Broadband Photodetector Based on a Two-Dimensional Metal-Organic Framework.

Metal-organic frameworks (MOFs) are emerging as an appealing class of highly tailorable electrically conducting materials with potential applications in optoelectronics. Yet, the realization of their proof-of-concept devices remains a daunting challenge, attributed to their poor electrical properties. Following recent work on a semiconducting Fe3 (THT)2 (NH4 )3 (THT: 2,3,6,7,10,11-triphenylenehexathiol) 2D MOF with record-high mobility and band-like charge transport, here, an Fe3 (THT)2 (NH4 )3 MOF-based photodetector operating in photoconductive mode capable of detecting a broad wavelength range from UV to NIR (400-1575 nm) is demonstrated. The narrow IR bandgap of the active layer (≈0.45 eV) constrains the performance of the photodetector at room temperature by band-to-band thermal excitation of charge carriers. At 77 K, the device performance is significantly improved; two orders of magnitude higher voltage responsivity, lower noise equivalent power, and higher specific detectivity of 7 × 108 cm Hz1/2 W-1 are achieved under 785 nm excitation. These figures of merit are retained over the analyzed spectral region (400-1575 nm) and are commensurate to those obtained with the first demonstrations of graphene- and black-phosphorus-based photodetectors. This work demonstrates the feasibility of integrating conjugated MOFs as an active element into broadband photodetectors, thus bridging the gap between materials' synthesis and technological applications.

Effective Hexagonal Boron Nitride Passivation of Few-Layered InSe and GaSe to Enhance Their Electronic and Optical Properties.

Indium selenide (InSe) and gallium selenide (GaSe), members of the III-VI chalcogenide family, are emerging two-dimensional (2D) semiconductors with appealing electronic properties. However, their devices are still lagging behind because of their sensitivity to air and device fabrication processes which induce structural damage and hamper their intrinsic properties. Thus, in order to obtain high-performance and stable devices, effective passivation of these air-sensitive materials is strongly required. Here, we demonstrate a hexagonal boron nitride (hBN)-based encapsulation technique, where 2D layers of InSe and GaSe are covered entirely between two layers of hBN. To fabricate devices out of fully encapsulated 2D layers, we employ the lithography-free via-contacting scheme. We find that hBN acts as an excellent encapsulant and a near-ideal substrate for InSe and GaSe by passivating them from the environment and isolating them from the charge disorder at the SiO2 surface. As a result, the encapsulated InSe devices are of high quality and ambient-stable for a long time and show an improved two-terminal mobility of 30-120 cm2 V-1 s-1 as compared to mere ∼1 cm2 V-1 s-1 for unencapsulated devices. On employing this technique to GaSe, we obtain a strong and reproducible photoresponse. In contrast to previous studies, where either good performance or long-term stability was achieved, we demonstrate a combination of both in our devices. This work thus provides a systematic study of fully encapsulated devices based on InSe and GaSe, which has not been reported until now. We believe that this technique can open ways for fundamental studies as well as toward the integration of these materials in technological applications.

High-mobility band-like charge transport in a semiconducting two-dimensional metal-organic framework.

Metal-organic frameworks (MOFs) are hybrid materials based on crystalline coordination polymers that consist of metal ions connected by organic ligands. In addition to the traditional applications in gas storage and separation or catalysis, the long-range crystalline order in MOFs, as well as the tunable coupling between the organic and inorganic constituents, has led to the recent development of electrically conductive MOFs as a new generation of electronic materials. However, to date, the nature of charge transport in the MOFs has remained elusive. Here we demonstrate, using high-frequency terahertz photoconductivity and Hall effect measurements, Drude-type band-like transport in a semiconducting, π-d conjugated porous Fe3(THT)2(NH4)3 (THT, 2,3,6,7,10,11-triphenylenehexathiol) two-dimensional MOF, with a room-temperature mobility up to ~ 220 cm2 V-1 s-1. The temperature-dependent conductivity reveals that this mobility represents a lower limit for the material, as mobility is limited by impurity scattering. These results illustrate the potential for high-mobility semiconducting MOFs as active materials in thin-film optoelectronic devices.