Novel feature extraction, selection and fusion for effective malware family classification

M Ahmadi, D Ulyanov, S Semenov, M Trofimov… - Proceedings of the sixth …, 2016 - dl.acm.org
Modern malware is designed with mutation characteristics, namely polymorphism and
metamorphism, which causes an enormous growth in the number of variants of malware …

[HTML][HTML] Fusing feature engineering and deep learning: A case study for malware classification

D Gibert, J Planes, C Mateu, Q Le - Expert Systems with Applications, 2022 - Elsevier
Abstract Machine learning has become an appealing signature-less approach to detect and
classify malware because of its ability to generalize to never-before-seen samples and to …

Profile hidden Markov models and metamorphic virus detection

S Attaluri, S McGhee, M Stamp - Journal in computer virology, 2009 - Springer
Metamorphic computer viruses “mutate” by changing their internal structure and,
consequently, different instances of the same virus may not exhibit a common signature …

Forensic analysis of ransomware families using static and dynamic analysis

KP Subedi, DR Budhathoki… - 2018 IEEE Security and …, 2018 - ieeexplore.ieee.org
Forensic analysis of executables or binary files is the common practice of detecting malware
characteristics. Reverse engineering is performed on executables at different levels such as …

Design of evaluation system for digital education operational skill competition based on blockchain

B Wu, Y Li - 2018 IEEE 15th international conference on e …, 2018 - ieeexplore.ieee.org
By letting students simulate operations and games on a digital education operation system,
schools are able to inspect learning achievement and teaching quality. In digital education …

Using multi-features and ensemble learning method for imbalanced malware classification

Y Zhang, Q Huang, X Ma, Z Yang… - 2016 IEEE Trustcom …, 2016 - ieeexplore.ieee.org
The ever-growing malware threats in the cyber spacecalls for techniques that are more
effective than widely deployedsignature-based detection system. To counter large volumes …

Mdfrcnn: Malware detection using faster region proposals convolution neural network

M Deore, U Kulkarni - 2022 - reunir.unir.net
Technological advancement of smart devices has opened up a new trend: Internet of
Everything (IoE), where all devices are connected to the web. Large scale networking …

Based on multi-features and clustering ensemble method for automatic malware categorization

Y Zhang, C Rong, Q Huang, Y Wu… - 2017 IEEE Trustcom …, 2017 - ieeexplore.ieee.org
Automatic malware categorization plays an important role in combating the current large
volume of malware and aiding the corresponding forensics. Generally, there are lot of …

Change point detection with machine learning for rapid ransomware detection

A Melaragno, W Casey - 2022 IEEE Intl Conf on Dependable …, 2022 - ieeexplore.ieee.org
Ransomware has been an ongoing issue since the early 1990s. In recent times ransomware
has spread from traditional computational resources to cyber-physical systems and …

Statistical signatures for fast filtering of instruction-substituting metamorphic malware

MR Chouchane, A Walenstein, A Lakhotia - Proceedings of the 2007 …, 2007 - dl.acm.org
Introducing program variations via metamorphic transformations is one of the methods used
by malware authors in order to help their programs slip past defenses. A method is …