Results 31 to 40 of about 134,256 (265)
IEEE Access, 2019 Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern.
Nir Nissim +6 more
doaj +1 more source
Malware Analysis and Detection Using Machine Learning Algorithms
Symmetry, 2022 One of the most significant issues facing internet users nowadays is malware. Polymorphic malware is a new type of malicious software that is more adaptable than previous generations of viruses.
M. Akhtar, Tao Feng
semanticscholar +1 more source
A Hybrid Approach for Android Malware Detection and Family Classification.
International Journal of Interactive Multimedia and Artificial Intelligence, 2021 With the increase in the popularity of mobile devices, malicious applications targeting Android platform have greatly increased. Malware is coded so prudently that it has become very complicated to identify.
Meghna Dhalaria, Ekta Gandotra
doaj +1 more source
A Survey of Malware Detection Using Deep Learning [PDF]
arXiv.orgThe problem of malicious software (malware) detection and classification is a complex task, and there is no perfect approach. There is still a lot of work to be done. Unlike most other research areas, standard benchmarks are difficult to find for malware
A. Bensaoud, J. Kalita, Mahmoud Bensaoud
semanticscholar +1 more source
2011 6th International Conference on Malicious and Unwanted Software, 2011 Malware behavior detectors observe the behavior of suspected malware by emulating its execution or executing it in a sandbox or other restrictive, instrumented environment. This assumes that the process, or process family, being monitored will exhibit the targeted behavior if it contains malware.
Ramilli, Marco +2 more
openaire +2 more sources
Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph [PDF]
, 2015 As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns.
Jang, Jae-wook +4 more
core +3 more sources
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
Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications
Italian National Conference on Sensors, 2023 Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection.
S. S. Shafin, G. Karmakar, I. Mareels
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
Detecting Obfuscated Malware using Memory Feature Engineering
International Conference on Information Systems Security and Privacy, 2022 : Memory analysis is critical in detecting malicious processes as it can capture various characteristics and behav-iors. However, while there is much research in the field, there are also some significant obstacles in malware detection, such as detection ...
Tristan Carrier +3 more
semanticscholar +1 more source