Results 21 to 30 of about 3,469,712 (175)
Visualization and deep-learning-based malware variant detection using OpCode-level features
, 2021 Malicious software (malware) is a major threat to the systems and networks’ security. Although anti-malware products are used to protect systems and networks against malware attacks, obfuscated malware that is capable of evading analysis and detection by
Alhashmi, A. +9 more
core +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
Cryptography, 2021 According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems.
Hossein Sayadi +6 more
doaj +1 more source
Android malware category detection using a novel feature vector-based machine learning model
Cybersecurity, 2023 Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android ...
Hashida Haidros Rahima Manzil +1 more
doaj +1 more source
Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique [PDF]
, 2020 Malware is a software or computer program that is used to carry out malicious activity. Malware is made with the aim of harming user’s device because it can change user’s data, use up bandwidth and other resources without user\u27s permission.
Almaarif, Ahmad +3 more
core +1 more source
IoT Malware Detection with Machine Learning [PDF]
, 2022 Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these IoT devices can also contain security vulnerabilities, which allow attackers to compromise them using malware.
Buttyán Levente, Ferenc Rudolf
core
A survey of IoT malware and detection methods based on static features
ICT Express, 2020 Due to a lack of security design as well as the specific characteristics of IoT devices such as the heterogeneity of processor architecture, IoT malware detection has to deal with very unique challenges, especially on detecting cross-architecture IoT ...
Quoc-Dung Ngo +3 more
doaj +1 more source
Tina-Rezaei/malware-detection: first realease of my paper implementation
, 2020 This is the first release of "a PE Header-based Method for Malware Detection Using Clustering and Deep Embedding ...
Tina-Rezaei
core +1 more source
BinSlayer: Accurate Comparison of Binary Executables [PDF]
, 2013 As the volume of malware inexorably rises, comparison of binary code is of increasing importance to security analysts as a method of automatically classifying new malware samples; purportedly new examples of malware are frequently a simple evolution of ...
Martial Bourquin +5 more
core +1 more source
SMASH: A Malware Detection Method Based on Multi-Feature Ensemble Learning
IEEE Access, 2019 With the increasing variants of malware, it is of great significance to detect malware and ensure system security effectively. The existing malware dynamic detection methods are vulnerable to evasion attacks.
Yusheng Dai +4 more
doaj +1 more source