scholarly journals Basic Static and Dynamic Analysis: Malware Analysis Day 1

2018 ◽  
Author(s):  
Lauren Pearce
Keyword(s):  
Dynamic Analysis ◽  
Malware Analysis ◽  
Static And Dynamic Analysis

Vulnerability Assessment and Malware Analysis of Android Apps Using Machine Learning

2021 ◽  
pp. 255-277
Author(s):  
Pallavi Khatri ◽  
Animesh Kumar Agrawal ◽  
Aman Sharma ◽  
Navpreet Pannu ◽  
Sumitra Ranjan Sinha
Keyword(s):  
Machine Learning ◽  
Dynamic Analysis ◽  
Mobile Devices ◽  
Personal Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Malware Analysis ◽  
Static And Dynamic Analysis ◽  
Android Apps ◽  
Learning Techniques

Mobile devices and their use are rapidly growing to the zenith in the market. Android devices are the most popular and handy when it comes to the mobile devices. With the rapid increase in the use of Android phones, more applications are available for users. Through these alluring multi-functional applications, cyber criminals are stealing personal information and tracking the activities of users. This chapter presents a two-way approach for finding malicious Android packages (APKs) by using different Android applications through static and dynamic analysis. Three cases are considered depending upon the severity level of APK, permission-based protection level, and dynamic analysis of APK for creating the dataset for further analysis. Subsequently, supervised machine learning techniques such as naive Bayes multinomial text, REPtree, voted perceptron, and SGD text are applied to the dataset to classify the selected APKs as malicious, benign, or suspicious.

Malware Behaviour Visualization

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Syed Zainudeen Mohd Shaid ◽  
Mohd Aizaini Maarof
Keyword(s):  
Dynamic Analysis ◽  
Visual Cues ◽  
New Technique ◽  
Malware Analysis ◽  
Test Results ◽  
Static And Dynamic Analysis ◽  
Manual Analysis ◽  
Detection And Identification ◽  
A New Technique ◽  
Visualization Techniques

The number of unique malware variants released each year is on the rise. Researchers may often need to use manual static and dynamic analysis to study new malware samples. Manual analysis of malware samples takes time. The more time taken to analyse a malware sample, the larger the damage that a malware can inflict. A lot of techniques have been devised by researchers to facilitate malware analysis and one of them is through malware visualization. Malware visualization is a field that focuses on representing malware features in the form of visual cues or images. This could be used to convey more information about a particular malware. Existing malware visualization techniques lack focus in visualizing malware behaviour in such a way that could enable better analysis of malware samples. In this paper, a new technique for malware visualization called ‘Malware Behaviour Image’ is presented. From the test results, the proposed technique is able to accurately capture and highlight malicious behaviour of malware samples, and can be used for malware analysis, detection and identification of malware variants.

scholarly journals Hypervisor-assisted dynamic malware analysis

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Roee S. Leon ◽  
Michael Kiperberg ◽  
Anat Anatey Leon Zabag ◽  
Nezer Jacob Zaidenberg
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Cyber Security ◽  
Malware Analysis ◽  
Analysis Tools ◽  
Significant Performance

AbstractMalware analysis is a task of utmost importance in cyber-security. Two approaches exist for malware analysis: static and dynamic. Modern malware uses an abundance of techniques to evade both dynamic and static analysis tools. Current dynamic analysis solutions either make modifications to the running malware or use a higher privilege component that does the actual analysis. The former can be easily detected by sophisticated malware while the latter often induces a significant performance overhead. We propose a method that performs malware analysis within the context of the OS itself. Furthermore, the analysis component is camouflaged by a hypervisor, which makes it completely transparent to the running OS and its applications. The evaluation of the system’s efficiency suggests that the induced performance overhead is negligible.

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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