Enhancing Malware Analysis Sandboxes with Emulated User Behavior

2022 ◽  
pp. 102613
Author(s):  
Songsong Liu ◽  
Pengbin Feng ◽  
Shu Wang ◽  
Kun Sun ◽  
Jiahao Cao
Keyword(s):  
User Behavior ◽  
Malware Analysis

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

Predictability of User Behavior in Social Media: Bottom-Up v Top-Down Modeling

2013 ◽  
Author(s):  
David Darmon ◽  
Jared Sylvester ◽  
Michelle Girvan ◽  
William M. Rand
Keyword(s):  
Social Media ◽  
User Behavior ◽  
Top Down ◽  
Bottom Up

A Survey on Improving the Web Search Ranking by User Behavior Information

2010 ◽  
Author(s):  
Mohamed Husain ◽  
Amarjeet Singh ◽  
Manoj Kumar ◽  
Rakesh Ranjan
Keyword(s):  
Web Search ◽  
User Behavior ◽  
The Web ◽  
Web Search Ranking

Asking costs little? The impact of tasks in video QoE studies on user behavior and user ratings

2013 ◽  
Author(s):  
Andreas Sackl ◽  
Michael Seufert ◽  
Tobias Hoßfeld
Keyword(s):  
User Behavior ◽  
User Ratings ◽  
The Impact

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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