scholarly journals An Effectual Identification Manual Malware Analysis Using Static Method

2013 ◽  
Vol 1 (2013) ◽  
Keyword(s):  
Static Method ◽  
Malware Analysis ◽  
Identification Manual

Virtualization Evolution For Transparent Malware Analysis

2012 ◽  
Vol 2 (6) ◽  
pp. 101-104
Author(s):  
Leenu Singh Leenu Singh ◽  
◽  
Syed Imtiyaz Hassan
Keyword(s):  
Malware Analysis

REVERSE ENGINEERING ANALYSIS FORENSIC MALWARE WEBC2-DIV

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Raditya Faisal Waliulu ◽  
Teguh Hidayat Iskandar Alam
Keyword(s):  
Reverse Engineering ◽  
Malware Analysis ◽  
Engineering Analysis ◽  
Know How

At this paper focus on Malicous Software also known as Malware APT1 (Advance Persistent Threat) codename WEBC2-DIV the most variants malware has criteria consists of Virus, Worm, Trojan, Adware, Spyware, Backdoor either Rootkit. Although, malware could avoidance scanning antivirus but reverse engineering could be know how dangerous malware infect computer client. Lately, malware attack as a form espionage (cyberwar) one of the most topic on security internet, because of has massive impact. Forensic malware becomes indicator successfull user to realized about malware infect. This research about reverse engineering. A few steps there are scanning, suspected packet in network and analysis of malware behavior and dissambler body malware.Keyword : forensic malware, Analysis, Advance Presistent Threat, Cyberwar, dissambler

scholarly journals Malware Detection Based on Code Visualization and Two-Level Classification

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 118
Author(s):  
Vassilios Moussas ◽  
Antonios Andreatos
Keyword(s):  
Web Applications ◽  
Detection System ◽  
Malware Detection ◽  
Image Features ◽  
Malicious Code ◽  
Malware Analysis ◽  
Malicious Software ◽  
Antivirus Software ◽  
Malware Classification ◽  
Artificial Neural Network Ann

Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

scholarly journals Pool Boiling Heat Transfer Coefficients in Mixtures of Water and Glycerin

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 830
Author(s):  
Viktor Vajc ◽  
Radek Šulc ◽  
Martin Dostál
Keyword(s):  
Heat Transfer ◽  
Dynamic Method ◽  
Water Mass ◽  
Heat Fluxes ◽  
Static Method ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Mean Relative Error ◽  
Mixture Effects ◽  
Mass Fractions

Heat transfer coefficients were investigated for saturated nucleate pool boiling of binary mixtures of water and glycerin at atmospheric pressure in a wide range of concentrations and heat fluxes. Mixtures with water mass fractions from 100% to 40% were boiled on a horizontal flat copper surface at heat fluxes from about 25 up to 270kWm−2. Experiments were carried out by static and dynamic method of measurement. Results of the static method show that the impact of mixture effects on heat transfer coefficient cannot be neglected and ideal heat transfer coefficient has to be corrected for all investigated concentrations and heat fluxes. Experimental data are correlated with the empirical correlation α=0.59q0.714+0.130ωw with mean relative error of 6%. Taking mixture effects into account, data are also successfully correlated with the combination of Stephan and Abdelsalam (1980) and Schlünder (1982) correlations with mean relative error of about 15%. Recommended coefficients of Schlünder correlation C0=1 and βL=2×10−4ms−1 were found to be acceptable for all investigated mixtures. The dynamic method was developed for fast measurement of heat transfer coefficients at continuous change of composition of boiling mixture. The dynamic method was tested for water–glycerin mixtures with water mass fractions from 70% down to 35%. Results of the dynamic method were found to be comparable with the static method. For water–glycerin mixtures with higher water mass fractions, precise temperature measurements are needed.

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.

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