Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Analysis about Planning and Modification of the Upper and Lower Structures of the 38 Floor Apartment SkyHouse Project with Equivalent Static Analysis

The modeled building structure is a regular building, with the number of levels being varied. The structural model is divided into 38-level portals. This research uses the help of the SAP2000 v21 program to facilitate the earthquake analysis process. The results of the study that will be compared are displacements between levels and base shear that occur due to earthquake forces. The results of the analysis have shown that static analysis produces greater results for the structural models compared to dynamic analysis. The difference in displacement between levels produced by the two methods in the three structural models is still included in the displacement limits between levels of permission required in SNI 1726-2012, so that the three models can still be analyzed by static analysis and dynamic analysis. Because the results of displacement and base shear in static analysis are greater than dynamic analysis, static analysis is safer if used for earthquake force loading in general structural calculations. Although in earthquake analysis, dynamic analysis is a more accurate analysis because the analysis process is closer to the actual situation.

Return of CFA: call-site sensitivity can be superior to object sensitivity even for object-oriented programs

In this paper, we challenge the commonly-accepted wisdom in static analysis that object sensitivity is superior to call-site sensitivity for object-oriented programs. In static analysis of object-oriented programs, object sensitivity has been established as the dominant flavor of context sensitivity thanks to its outstanding precision. On the other hand, call-site sensitivity has been regarded as unsuitable and its use in practice has been constantly discouraged for object-oriented programs. In this paper, however, we claim that call-site sensitivity is generally a superior context abstraction because it is practically possible to transform object sensitivity into more precise call-site sensitivity. Our key insight is that the previously known superiority of object sensitivity holds only in the traditional k -limited setting, where the analysis is enforced to keep the most recent k context elements. However, it no longer holds in a recently-proposed, more general setting with context tunneling. With context tunneling, where the analysis is free to choose an arbitrary k -length subsequence of context strings, we show that call-site sensitivity can simulate object sensitivity almost completely, but not vice versa. To support the claim, we present a technique, called Obj2CFA, for transforming arbitrary context-tunneled object sensitivity into more precise, context-tunneled call-site-sensitivity. We implemented Obj2CFA in Doop and used it to derive a new call-site-sensitive analysis from a state-of-the-art object-sensitive pointer analysis. Experimental results confirm that the resulting call-site sensitivity outperforms object sensitivity in precision and scalability for real-world Java programs. Remarkably, our results show that even 1-call-site sensitivity can be more precise than the conventional 3-object-sensitive analysis.

The Static Behavior of a Ship Deck Panel Made of Composite Materials

A study on the static analysis of a naval panel made of composite sandwich materials is presented. By using FEM, the modeling of a naval floor with a length of 5 m and a width of 2.5 m is performed. Two distinct cases, have been performed: the first model consists of the plate and stiffeners made of steel and the second model concerns a panel made of composite material sandwich type steel / SANFoam103 / steel, and the stiffeners made of steel. A parametric study has been performed. The thickness of the steel faces have 6 mm, and for the core of SANFoam have been selected the thicknesses 5 mm, 10 mm, 20 mm, 40 mm.