Results 41 to 50 of about 12,725 (233)
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
Malware detection and classification using low-level features [PDF]
, 2023 Nowadays, computers and computer systems are involved in most areas of our lives. Employees and users of manufacturing and transportation, banking and healthcare, education, and entertainment rely on computers and networks which allow for better, faster,
Banin, Sergii
core +1 more source
Learning and classification of malware behavior
, 2022 S.108-125Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature ...
Düssel, P. +4 more
core +1 more source
Impact Analysis of Malware Based on Call Network API with Heuristic Detection Method [PDF]
, 2020 Malware is a program that has a negative influence on computer systems that don't have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis.
Suryati, One Tika +3 more
core +1 more source
Performance Monitoring Counter Based Intelligent Malware Detection and Design Alternatives
IEEE Access, 2022 Hardware solutions for malware detection are becoming increasingly important as software-based solutions can be easily compromised by intelligent malware.
Jordan Pattee +2 more
doaj +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
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection [PDF]
, 2021 peer reviewedA well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally.
DAOUDI, Nadia +3 more
core +1 more source
Trends in Android Malware Detection
Journal of Digital Forensics, Security and Law, 2013 This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and
Kaveh Shaerpour +2 more
openaire +3 more sources
Metamorphic Detection of Repackaged Malware [PDF]
2021 IEEE/ACM 6th International Workshop on Metamorphic Testing (MET), 2021 Machine learning-based malware detection systems are often vulnerable to evasion attacks, in which a malware developer manipulates their malicious software such that it is misclassified as benign. Such software hides some properties of the real class or adopts some properties of a different class by applying small perturbations.
Shirish Kumar Singh, Gail E. Kaiser
openaire +2 more sources
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