Results 121 to 130 of about 5,547 (215)

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer. [PDF]

PLoS One, 2021
Elkabbash ET, Elkabbash ET, Mostafa RR, Barakat SI.   +3 more
europepmc   +1 more source

An adaptive neuro-fuzzy inference system for multinomial malware classification

Journal of Nigerian Society of Physical Sciences
Malware detection and classification are important requirements for information security because malware poses a great threat to computer users. As the growth of technology increases, malware is getting more sophisticated and thereby more difficult to ...
Amos Orenyi Bajeh, Mary Olayinka Olaoye, Fatima Enehezei Usman-Hamza, Ikeola Suhurat Olatinwo, Peter ogirima Sadiku, Abdulkadir Bolakale Sakariyah   +5 more
doaj   +1 more source

Humans vs. Machines in Malware Classification.

, 2023
Today, the classification of a file as either benign or malicious is performed by a combination of deterministic indicators (such as antivirus rules), Machine Learning classifiers, and, more importantly, the judgment of human experts. However, to compare the difference between human and machine intelligence in malware analysis, it is first necessary to
Aonzo, Simone, Han, Yufei, Mantovani, Alessandro, Balzarotti, Davide   +3 more
openaire   +2 more sources

Behaviour-aware Malware Classification: Dynamic Feature Selection [PDF]

Despite the continued advancements in security research, malware persists as being a major threat in this digital age. Malware detection is a primary defence strategy for most networks but the identification of malware strains is becoming increasingly ...
Shi, Q, Vu Dinh, P, Phan Huy, D, Tran Nguyen, N, Shone, N, Nguyen Viet, H   +5 more
core   +1 more source

Deep visualization classification method for malicious code based on Ngram-TFIDF

Tongxin xuebao
With the continuous increase in the scale and variety of malware, traditional malware analysis methods, which relied on manual feature extraction, become time-consuming and error-prone, rendering them unsuitable.
WANG Jinwei, CHEN Zhengjia, XIE Xue, LUO Xiangyang, MA Bin   +4 more
doaj   +2 more sources

Multinomial malware classification via low-level features

, 2018
Because malicious software or (”malware”) is so frequently used in a cyber crimes, malware detection and relevant research became a serious issue in the information security landscape.
Dyrkolbotn, Geir Olav, Banin, Sergii
core  

OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning. [PDF]

Sensors (Basel), 2020
Niu W, Cao R, Zhang X, Ding K, Zhang K, Li T.   +5 more
europepmc   +1 more source

Dual Convolutional Malware Network (DCMN): An Image-Based Malware Classification Using Dual Convolutional Neural Networks

Malware attacks have a cascading effect, causing financial harm, compromising privacy, operations and interrupting. By preventing these attacks, individuals and organizations can safeguard the valuable assets of their operations, and gain more trust.
Nader Bakir, Bassam Al-Masri, Khouloud Samrouth, Ali El-Zaart   +3 more
core   +1 more source

MalSort: Lightweight and efficient image-based malware classification using masked self-supervised framework with Swin Transformer

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications.
Song, Ruixin, Shi, Xipeng, Li, Qingru, Wang, Fangwei, Yang, Fang, Tan, Zhiyuan, Wang, Changguang   +6 more
core   +1 more source

Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]

Sci Rep
Tawfik M, Tarazi H, Dalalah A, Bajeszeyadaljunaeidia, Shaban WM, Fathi IS.   +5 more
europepmc   +1 more source