Results 11 to 20 of about 1,517 (174)

Malware Family Classification Model Based on MobileNet [PDF]

Jisuanji gongcheng, 2020
The existing malicious code classification method based on Convolutional Neural Network(CNN) has the problem of large computational resource consumption.In order to reduce the computational quantity and parameter quantity in the classification process ...
ZENG Yaqin, ZHANG Linlin, ZHANG Ruonan, YANG Bo
doaj   +1 more source

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

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

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

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   +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

An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy, 2021
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 ...
Jeyaprakash Hemalatha, S. Abijah Roseline, Subbiah Geetha, Seifedine Kadry, Robertas Damaševičius   +4 more
doaj   +1 more source

Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm

IEEE Access, 2020
Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems.
S. Abijah Roseline, S. Geetha, Seifedine Kadry, Yunyoung Nam   +3 more
doaj   +1 more source

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   +1 more source

A Malware Detection Method of Code Texture Visualization Based on an Improved Faster RCNN Combining Transfer Learning

IEEE Access, 2020
Today, with the continuous promotion and development of IoT and 5G technology, Cyberspace has become an important pillar of economic and social development, and also a foundational domain of national security.
Yuntao Zhao, Wenjie Cui, Shengnan Geng, Bo Bo, Yongxin Feng, Wenbo Zhang   +5 more
doaj   +1 more source