Results 11 to 20 of about 4,236,332 (208)
MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.
PLoS ONE, 2022 With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention.
Yuying Liu +4 more
doaj +2 more sources
A Survey on Malware Detection with Graph Representation Learning [PDF]
ACM Computing Surveys, 2023 Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization ...
Tristan Bilot +3 more
semanticscholar +1 more source
A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks
Information, 2023 Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware.
Parvez Faruki +5 more
doaj +1 more source
Continuous Learning for Android Malware Detection [PDF]
USENIX Security Symposium, 2023 Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps.
Yizheng Chen +2 more
semanticscholar +1 more source
Technique for IoT malware detection based on control flow graph analysis
Радіоелектронні і комп'ютерні системи, 2022 The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats.
Kira Bobrovnikova +4 more
doaj +1 more source
Malware Detection Using Deep Learning and Correlation-Based Feature Selection
Symmetry, 2023 Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical.
E. Alomari +6 more
semanticscholar +1 more source
IEEE Access, 2021 Malware variants are the major emerging threats that face cybersecurity due to the potential damage to computer systems. Many solutions have been proposed for detecting malware variants.
Abdulbasit A. Darem +5 more
doaj +1 more source
The rise of obfuscated Android malware and impacts on detection methods [PDF]
PeerJ Computer Science, 2022 The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious
Wael F. Elsersy +2 more
doaj +2 more sources
Adaptive secure malware efficient machine learning algorithm for healthcare data
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed +8 more
wiley +1 more source
HMLET: Hunt Malware Using Wavelet Transform on Cross-Platform
IEEE Access, 2022 As the importance of cyberspace grows, malicious software (malware) is threatening not only individuals but also countries. In addition, numerous malware is still circulating in cyberspace, and as technology advances, new or advanced malware are emerging.
Sangmin Park +2 more
doaj +1 more source