A review on the long short-term memory model
Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …
neurocomputing fields. According to several online sources, this model has improved …
Leveraging computational intelligence techniques for defensive deception: a review, recent advances, open problems and future directions
PV Mohan, S Dixit, A Gyaneshwar, U Chadha… - Sensors, 2022 - mdpi.com
With information systems worldwide being attacked daily, analogies from traditional warfare
are apt, and deception tactics have historically proven effective as both a strategy and a …
are apt, and deception tactics have historically proven effective as both a strategy and a …
Dynamic analysis for IoT malware detection with convolution neural network model
Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected
society where all things are connected and exchange information through the Internet. IoT …
society where all things are connected and exchange information through the Internet. IoT …
[PDF][PDF] Detecting Obfuscated Malware using Memory Feature Engineering.
T Carrier, P Victor, A Tekeoglu, AH Lashkari - Icissp, 2022 - scitepress.org
Memory analysis is critical in detecting malicious processes as it can capture various
characteristics and behaviors. However, while there is much research in the field, there are …
characteristics and behaviors. However, while there is much research in the field, there are …
[HTML][HTML] An inception V3 approach for malware classification using machine learning and transfer learning
Malware instances have been extremely used for illegitimate purposes, and new variants of
malware are observed every day. Machine learning in network security is one of the prime …
malware are observed every day. Machine learning in network security is one of the prime …
A novel malware classification and augmentation model based on convolutional neural network
The rapid development and widespread use of the Internet have led to an increase in the
number and variety of malware proliferating via the Internet. Malware is the general …
number and variety of malware proliferating via the Internet. Malware is the general …
Efficient spam and phishing emails filtering based on deep learning
Nowadays, spam emails represent a severe threat to security and cause a big waste in
transmission time and users' time spent in browsing unsolicited bulk emails (UBE). This is in …
transmission time and users' time spent in browsing unsolicited bulk emails (UBE). This is in …
Malware detection using memory analysis data in big data environment
Malware is a significant threat that has grown with the spread of technology. This makes
detecting malware a critical issue. Static and dynamic methods are widely used in the …
detecting malware a critical issue. Static and dynamic methods are widely used in the …
A multikernel and metaheuristic feature selection approach for IoT malware threat hunting in the edge layer
H Haddadpajouh, A Mohtadi… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Internet-of-Things (IoT) devices are increasingly targeted, partly due to their presence in a
broad range of applications (including home and corporate environments). In this article, we …
broad range of applications (including home and corporate environments). In this article, we …
Data augmentation and transfer learning to classify malware images in a deep learning context
In the past few years, malware classification techniques have shifted from shallow traditional
machine learning models to deeper neural network architectures. The main benefit of some …
machine learning models to deeper neural network architectures. The main benefit of some …