Results 21 to 30 of about 465,682 (204)

Disarming visualization-based approaches in malware detection systems [PDF]

, 2022
Visualization-based approaches have recently been used in conjunction with signature-based techniques to detect variants of malware files. Indeed, it is sufficient to modify some byte of executable files to modify the signature and, thus, to elude a ...
Qian, Chenyi, Lax, Gianluca, Fisichella, Marco, Saidia Fascí, Lara   +3 more
core   +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

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

A New Learning Approach to Malware Classification Using Discriminative Feature Extraction

IEEE Access, 2019
With the development of the Internet, malware has become one of the most significant threats. Recognizing specific types of malware is an important step toward effective removal.
Ya-shu Liu, Yu-Kun Lai, Zhi-Hai Wang, Han-Bing Yan   +3 more
doaj   +1 more source

Features Engineering for Malware Family Classification Based API Call

Computers, 2022
Malware is used to carry out malicious operations on networks and computer systems. Consequently, malware classification is crucial for preventing malicious attacks.
Ammar Yahya Daeef, Ali Al-Naji, Javaan Chahl   +2 more
doaj   +1 more source

On Visual Hallmarks of Robustness to Adversarial Malware

CoRR, 2018
A central challenge of adversarial learning is to interpret the resulting hardened model. In this contribution, we ask how robust generalization can be visually discerned and whether a concise view of the interactions between a hardened decision map and input samples is possible.
Alex Huang, Abdullah Al-Dujaili, Erik Hemberg, Una-May O'Reilly   +3 more
openaire   +2 more sources

Efficient Windows malware identification and classification scheme for plant protection information systems

Frontiers in Plant Science, 2023
Due to developments in science and technology, the field of plant protection and the information industry have become increasingly integrated, which has resulted in the creation of plant protection information systems.
Zhiguo Chen, Zhiguo Chen, Shuangshuang Xing, Shuangshuang Xing, Xuanyu Ren, Xuanyu Ren   +5 more
doaj   +1 more source

Demystifying Malware Image Visualization: Enhancing Reproducibility and Explainability [PDF]

, 2023
openL'aumento persistente dei malware riscontrati nel mondo digitale ha rappresentato un ostacolo continuo per i ricercatori nell'ambito della sicurezza informatica, dall'avvento dei personal computer fino ai giorni nostri.
BROSOLO, MATTEO
core  

Design of an Automated Malware Analysis System [PDF]

, 2010
Malware, malicious software written for the purpose of causing computer-related damage, has existed for many years. Only recently have the monetary advantages of perpetrating malware begun to gain interest from increasingly malicious groups.
Nabholz, Bradley J
core   +1 more source