Results 121 to 130 of about 1,811,594 (209)

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition. [PDF]

Comput Intell Neurosci, 2022
Naeem MR, Amin R, Alshamrani SS, Alshehri A.   +3 more
europepmc   +1 more source

MtNet: A Multi-Task Neural Network for Dynamic Malware Classification [PDF]

, 2020
. In this paper, we propose a new multi-task, deep learning architecture for malware classification for the binary (i.e. malware versus benign) malware classification task. All models are trained with data extracted from dynamic analysis of malicious and
Jack W Stokes, Wenyi Huang
core  

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

A few-shot malware classification approach for unknown family recognition using malware feature visualization

, 2021
With the ever-increasing threat of malware attacks, building an effective malware classifier to detect malware promptly is of utmost importance. Malware is constantly growing and evolving with the use of sophisticated obfuscation techniques.
Khandhar, Shubham (author)
core  

Exploring discriminatory features for automated malware classification

, 2013
. The ever-growing malware threat in the cyber space calls for tech-niques that are more effective than widely deployed signature-based detection systems and more scalable than manual reverse engineering by forensic experts.
Nathan Brown, Deguang Kong, Guanhua Yan
core   +1 more source

DeepGray: Malware Classification Using Grayscale Images with Deep Learning

Proceedings of the International Florida Artificial Intelligence Research Society Conference
In the ever-evolving landscape of cybersecurity, the threat posed by malware continues to loom large, necessitating innovative and robust approaches for its effective detection and classification. In this paper, we introduce a novel method, DeepGray, for
Haodi Jiang, Harshitha Polsani, Yuexin Liu   +2 more
doaj   +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

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

Malware Generation and Classification using PixelCNN

Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems ...
Karumudi, Mounika Krishna Teja
core   +1 more source

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

Sci Rep
Tawfik M, Tawfik M, Tarazi H, Dalalah A, Bajeszeyadaljunaeidia, Shaban WM, Fathi IS.   +6 more
europepmc   +1 more source