Results 11 to 20 of about 465,682 (204)

Malware Behaviour Visualization

Jurnal Teknologi, 2014
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.
Syed Zainudeen Mohd Shaid, Mohd Aizaini Maarof   +1 more
openaire   +2 more sources

PAFE: A lightweight visualization-based fast malware classification method

Heliyon
With the development of automated malware toolkits, cybersecurity faces evolving threats. Although visualization-based malware analysis has proven to be an effective method, existing approaches struggle with challenging malware samples due to alterations
Sicong Li, Jian Wang, Shuo Wang, Yafei Song   +3 more
doaj   +2 more sources

A Survey of Visualization Systems for Malware Analysis.

, 2015
published
Markus Wagner 0008, Fabian Fischer 0001, Robert Luh, Andrea Haberson, Alexander Rind, Daniel A. Keim, Wolfgang Aigner   +6 more
core   +5 more sources

Visualization techniques for malware behavior analysis [PDF]

SPIE Proceedings, 2011
Malware spread via Internet is a great security threat, so studying their behavior is important to identify and classify them. Using SSDT hooking we can obtain malware behavior by running it in a controlled environment and capturing interactions with the target operating system regarding file, process, registry, network and mutex activities.
André R. A. Grégio, Rafael D. C. Santos   +1 more
openaire   +3 more sources

DaViz: Visualization for Android Malware Datasets [PDF]

, 2022
With millions of Android malware samples available, researchers have a large amount of data to perform malware detection and classification, specially with the help of machine learning. Thus far, visualization tools focus on single samples or one-to-many comparison, but not a many-to-many approach.
Concepción Miranda, Tomás, Lalande, Jean-François, Viet Triem Tong, Valérie, Wilke, Pierre   +3 more
openaire   +2 more sources

Malware detection based on semi-supervised learning with malware visualization

Mathematical Biosciences and Engineering, 2021
The traditional signature-based detection method requires detailed manual analysis to extract the signatures of malicious samples, and requires a large number of manual markers to maintain the signature library, which brings a great time and resource costs, and makes it difficult to adapt to the rapid generation and mutation of malware.
Tan Gao, Lan Zhao, Xudong Li, Wen Chen
openaire   +3 more sources

Adaptive secure malware efficient machine learning algorithm for healthcare data

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed, Abdullah Lakhan, Dilovan Asaad Zebari, Karrar Hameed Abdulkareem, Jan Nedoma, Radek Martinek, Usman Tariq, Majed Alhaisoni, Prayag Tiwari   +8 more
wiley   +1 more source

MalView: Interactive Visual Analytics for Comprehending Malware Behavior

IEEE Access, 2022
Malicious applications are usually comprehended through two major techniques, namely static and dynamic analyses. Through static analysis, a given malicious program is parsed, and some representative artifacts (e.g., control-flow graphs) are produced ...
Huyen N. Nguyen, Faranak Abri, Vung Pham, Moitrayee Chatterjee, Akbar Siami Namin, Tommy Dang   +5 more
doaj   +1 more source

IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories

Applied Sciences, 2022
The smart factory environment has been transformed into an Industrial Internet of Things (IIoT) environment, which is an interconnected and open approach.
Ho-myung Kim, Kyung-ho Lee
doaj   +1 more source

Visualizing compiled executables for malware analysis [PDF]

2009 6th International Workshop on Visualization for Cyber Security, 2009
Reverse engineering compiled executables is a task with a steep learning curve. It is complicated by the task of translating assembly into a series of abstractions that represent the overall flow of a program. Most of the steps involve finding interesting areas of an executable and determining their overall functionality.
Daniel Quist, Lorie M. Liebrock
openaire   +2 more sources