Results 31 to 40 of about 81,305 (190)
Malware Classification based on Call Graph Clustering [PDF]
, 2010 Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection.
Kinable, Joris, Kostakis, Orestis
core +1 more source
Unsupervised Anomaly-based Malware Detection using Hardware Features [PDF]
, 2014 Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's execution pattern (
Sethumadhavan, Simha +2 more
core +4 more sources
Python and Malware: Developing Stealth and Evasive Malware without Obfuscation [PDF]
Proceedings of the 18th International Conference on Security and Cryptography, 2021 To appear in SECRYPT ...
Koutsokostas, Vasilios +1 more
openaire +2 more sources
Packed malware variants detection using deep belief networks [PDF]
MATEC Web of Conferences, 2020 Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years.
Zhang Zhigang +3 more
doaj +1 more source
Obfuscation-based malware update: A comparison of manual and automated methods [PDF]
, 2017 Indexación: Scopus; Web of Science.This research presents a proposal of malware classification and its update based on capacity and obfuscation. This article is an extension of [4]a, and describes the procedure for malware updating, that is, to take ...
Barría, C. +4 more
core +2 more sources
Applied Sciences, 2021 Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and ...
Dong-Kyu Chae +4 more
doaj +1 more source
Survey of Machine Learning Techniques for Malware Analysis [PDF]
, 2018 Coping with malware is getting more and more challenging, given their relentless growth in complexity and volume. One of the most common approaches in literature is using machine learning techniques, to automatically learn models and patterns behind ...
Aniello, Leonardo +2 more
core +2 more sources
Future Internet, 2023 Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware ...
Jinting Zhu +4 more
doaj +1 more source
IEEE AccessCloud computing is integral to modern IT infrastructure, with Linux-based virtual machines (VMs) comprising 95% of public cloud environments. This widespread use makes Linux VMs a prime target for cyberattacks, particularly advanced malware designed for ...
Tom Landman, Nir Nissim
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 +3 more
doaj +1 more source