Android Botnets

2021 ◽  
pp. 75-92
Author(s):  
Ahmad Karim ◽  
Victor Chang ◽  
Ahmad Firdaus
Keyword(s):  
Dynamic Analysis ◽  
High Accuracy ◽  
Remote Access ◽  
Learning Approaches ◽  
Analysis Model ◽  
Hybrid Analysis ◽  
Static And Dynamic Analysis ◽  
Mobile Botnet ◽  
Wide Scale ◽  
Access Information

Mobile botnets are gaining popularity with the expressive demand of smartphone technologies. Similarly, the majority of mobile botnets are built on a popular open source OS, e.g., Android. A mobile botnet is a network of interconnected smartphone devices intended to expand malicious activities, for example; spam generation, remote access, information theft, etc., on a wide scale. To avoid this growing hazard, various approaches are proposed to detect, highlight and mark mobile malware applications using either static or dynamic analysis. However, few approaches in the literature are discussing mobile botnet in particular. In this article, the authors have proposed a hybrid analysis framework combining static and dynamic analysis as a proof of concept, to highlight and confirm botnet phenomena in Android-based mobile applications. The validation results affirm that machine learning approaches can classify the hybrid analysis model with high accuracy rate (98%) than classifying static or dynamic individually.

scholarly journals Android Botnets

2020 ◽  
Vol 32 (3) ◽  
pp. 50-67
Author(s):  
Ahmad Karim ◽  
Victor Chang ◽  
Ahmad Firdaus
Keyword(s):  
Dynamic Analysis ◽  
Remote Access ◽  
Learning Approaches ◽  
Analysis Model ◽  
Hybrid Analysis ◽  
Static And Dynamic Analysis ◽  
Mobile Botnet ◽  
Mobile Malware ◽  
Wide Scale ◽  
Access Information

Mobile botnets are gaining popularity with the expressive demand of smartphone technologies. Similarly, the majority of mobile botnets are built on a popular open source OS, e.g., Android. A mobile botnet is a network of interconnected smartphone devices intended to expand malicious activities, for example; spam generation, remote access, information theft, etc., on a wide scale. To avoid this growing hazard, various approaches are proposed to detect, highlight and mark mobile malware applications using either static or dynamic analysis. However, few approaches in the literature are discussing mobile botnet in particular. In this article, the authors have proposed a hybrid analysis framework combining static and dynamic analysis as a proof of concept, to highlight and confirm botnet phenomena in Android-based mobile applications. The validation results affirm that machine learning approaches can classify the hybrid analysis model with high accuracy rate (98%) than classifying static or dynamic individually.

Zeroth-Order Shear Deformation Theory for Laminated Composite Plates

2003 ◽  
Vol 70 (3) ◽  
pp. 374-380 ◽  
Author(s):  
M. C. Ray
Keyword(s):  
Dynamic Analysis ◽  
Shear Deformation ◽  
Deformation Theory ◽  
Shear Deformation Theory ◽  
Laminated Composite ◽  
Composite Plates ◽  
High Accuracy ◽  
Laminated Composite Plates ◽  
Zeroth Order ◽  
Static And Dynamic Analysis

In this paper a zeroth-order shear deformation theory has been derived for static and dynamic analysis of laminated composite plates. The responses obtained by the theory for symmetric and antisymmetric laminates are compared with the existing solutions. The comparison firmly establishes that this new shear deformation theory can be used for both thick and thin laminated composite plates with high accuracy.

Dynamic Analysis of Cage Stress in Tapered Roller Bearings Using Component-Mode-Synthesis Method

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Tomoya Sakaguchi ◽  
Kazuyoshi Harada
Keyword(s):  
Rigid Body ◽  
Dynamic Analysis ◽  
Boundary Point ◽  
Axial Load ◽  
Synthesis Method ◽  
Load Condition ◽  
Component Mode Synthesis ◽  
Analysis Model ◽  
Tapered Roller ◽  
Tapered Roller Bearings

In order to investigate cage stress in tapered roller bearings, a dynamic analysis tool considering both the six degrees of freedom of motion of the rollers and cage and the elastic deformation of the cage was developed. Cage elastic deformation is equipped using a component-mode-synthesis (CMS) method. Contact forces on the elastically deforming surfaces of the cage pocket are calculated at all node points of finite-elements on it. The location and pattern of the boundary points required for the component-mode-synthesis method were examined by comparing cage stresses in a static condition of pocket forces and constraints calculated by using the finite-element and the CMS methods. These results indicated that one boundary point lying at the center on each bar is appropriate for the effective dynamic analysis model focusing on the cage stress, especially at the pocket corners of the cages, which are actually broken. A behavior measurement of a polyamide cage in a tapered roller bearing was conducted for validating the analysis model. It was confirmed in both the experiment and analysis that the cage whirled under a large axial load condition and the cage center oscillated in a small amplitude under a small axial load condition. In the analysis, the authors discussed the four models including elastic bodies having a normal eigenmode of 0, 8 or 22, and rigid-body. There were small differences among the cage center loci of the four models. These two cages having normal eigenmodes of 0 and rigid-body whirled with imperceptible fluctuations. At least approximately 8 normal eigenmodes of cages should be introduced to conduct a more accurate dynamic analysis although the effect of the number of normal eigenmodes on the stresses at the pocket corners was insignificant. From the above, it was concluded to be appropriate to introduce one boundary point lying at the center on each pocket bar of cages and approximately 8 normal eigenmodes to effectively introduce the cage elastic deformations into a dynamic analysis model.

scholarly journals An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 344
Author(s):  
Jeyaprakash Hemalatha ◽  
S. Abijah Roseline ◽  
Subbiah Geetha ◽  
Seifedine Kadry ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Malware Detection ◽  
Class Imbalance ◽  
Learning Model ◽  
Computational Time ◽  
Learning Approaches ◽  
Security Threat ◽  
Static And Dynamic Analysis ◽  
Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

scholarly journals Static and dynamic analysis of homogeneous Micropolar-Cosserat panels

2021 ◽  
pp. 1-53
Author(s):  
S. K. Singh ◽  
A. Banerjee ◽  
R. K. Varma ◽  
S. Adhikari ◽  
S. Das
Keyword(s):  
Dynamic Analysis ◽  
Static And Dynamic Analysis

A New Approach to the Rigid Finite Element Method in Modeling Spatial Slender Systems

2018 ◽  
Vol 18 (02) ◽  
pp. 1850017 ◽  
Author(s):  
Iwona Adamiec-Wójcik ◽  
Łukasz Drąg ◽  
Stanisław Wojciech
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Dynamic Analysis ◽  
Equations Of Motion ◽  
Nonlinear Dynamic Analysis ◽  
New Approach ◽  
Static And Dynamic Analysis ◽  
Rigid Finite Element Method ◽  
Longitudinal Flexibility ◽  
Element Method

The static and dynamic analysis of slender systems, which in this paper comprise lines and flexible links of manipulators, requires large deformations to be taken into consideration. This paper presents a modification of the rigid finite element method which enables modeling of such systems to include bending, torsional and longitudinal flexibility. In the formulation used, the elements into which the link is divided have seven DOFs. These describe the position of a chosen point, the extension of the element, and its orientation by means of the Euler angles Z[Formula: see text]Y[Formula: see text]X[Formula: see text]. Elements are connected by means of geometrical constraint equations. A compact algorithm for formulating and integrating the equations of motion is given. Models and programs are verified by comparing the results to those obtained by analytical solution and those from the finite element method. Finally, they are used to solve a benchmark problem encountered in nonlinear dynamic analysis of multibody systems.

Static and dynamic analysis of a gradient-elastic bar in tension

2002 ◽  
Vol 72 (6-7) ◽  
pp. 483-497 ◽  
Author(s):  
K. G. Tsepoura ◽  
S. Papargyri-Beskou ◽  
D. Polyzos ◽  
D. E. Beskos
Keyword(s):  
Dynamic Analysis ◽  
Static And Dynamic Analysis ◽  
Elastic Bar
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