Results 21 to 30 of about 46,394 (182)
Research on the Construction of Malware Variant Datasets and Their Detection Method
Applied Sciences, 2022 Malware detection is of great significance for maintaining the security of information systems. Malware obfuscation techniques and malware variants are increasingly emerging, but their samples and API (application programming interface) sequences are ...
Faming Lu +4 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
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
The Malaise of the Administrative Process [PDF]
, 1962 Computer viruses uses a few different techniques, with various intentions, toinfect files. However, what most of them have in common is that they wantto avoid detection by anti-malware software.
Arding, Petter, Hedelin, Hugo
core +3 more sources
A multilabel fuzzy relevance clustering system for malware attack attribution in the edge layer of cyber-physical networks [PDF]
, 2020 The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users’ systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would ...
Alaeiyan, M +4 more
core +2 more sources
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
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 2023 The "Malware Detection on Application using Machine Learning" project is a focused initiative aimed at enhancing the security of mobile applications through advanced detection mechanisms. As the threat landscape for mobile app-based malware continues to evolve, this project leverages the power of machine learning to develop robust and adaptive ...
openaire +1 more source
Detecting Environment-Sensitive Malware [PDF]
, 2011 The execution of malware in an instrumented sandbox is a widespread approach for the analysis of malicious code, largely because it sidesteps the difficulties involved in the static analysis of obfuscated code. As malware analysis sandboxes increase in popularity, they are faced with the problem of malicious code detecting the instrumented environment ...
Lindorfer M. +2 more
openaire +1 more source
Android Malware Characterization using Metadata and Machine Learning Techniques [PDF]
, 2017 Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to ...
Guzmán, Antonio +3 more
core +2 more sources
A Study of the Relationship Between Antivirus Regressions and Label Changes [PDF]
, 2013 AntiVirus (AV) products use multiple components to detect malware. A component which is found in virtually all AVs is the signature-based detection engine: this component assigns a particular signature label to a malware that the AV detects.
Cukier, M. +4 more
core +1 more source