Tight arms race: Overview of current malware threats and trends in their detection
Cyber attacks are currently blooming, as the attackers reap significant profits from them and
face a limited risk when compared to committing the “classical” crimes. One of the major …
face a limited risk when compared to committing the “classical” crimes. One of the major …
An overview of deep learning architecture of deep neural networks and autoencoders
The recent wide applications of deep learning in multiple fields has shown a great progress,
but to perform optimally, it requires the adjustment of various architectural features and …
but to perform optimally, it requires the adjustment of various architectural features and …
A new convolutional neural network-based system for NILM applications
F Ciancetta, G Bucci, E Fiorucci, S Mari… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Electrical load planning and demand response programs are often based on the analysis of
individual load-level measurements obtained from houses or buildings. The identification of …
individual load-level measurements obtained from houses or buildings. The identification of …
[图书][B] Deep reinforcement learning
M Sewak - 2019 - Springer
Reinforcement Learning has evolved a long way with the enhancements from deep
learning. Recent research efforts into combining deep learning with Reinforcement Learning …
learning. Recent research efforts into combining deep learning with Reinforcement Learning …
[HTML][HTML] A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
M Pawlicki, R Kozik, M Choraś - Neurocomputing, 2022 - Elsevier
The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
Malware detection using machine learning and deep learning
Research shows that over the last decade, malware have been growing exponentially,
causing substantial financial losses to various organizations. Different anti-malware …
causing substantial financial losses to various organizations. Different anti-malware …
Multilayer framework for botnet detection using machine learning algorithms
A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet
can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and …
can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and …
Robust android malware detection system against adversarial attacks using q-learning
Since the inception of Andoroid OS, smartphones sales have been growing exponentially,
and today it enjoys the monopoly in the smartphone marketplace. The widespread adoption …
and today it enjoys the monopoly in the smartphone marketplace. The widespread adoption …
Classifying malware images with convolutional neural network models
A Bensaoud, N Abudawaood, J Kalita - International Journal of …, 2020 - airitilibrary.com
Due to increasing threats from malicious software (malware) in both number and complexity,
researchers have developed approaches to automatic detection and classification of …
researchers have developed approaches to automatic detection and classification of …
GA-StackingMD: Android malware detection method based on genetic algorithm optimized stacking
N Xie, Z Qin, X Di - Applied Sciences, 2023 - mdpi.com
With the rapid development of network and mobile communication, intelligent terminals such
as smartphones and tablet computers have changed people's daily life and work. However …
as smartphones and tablet computers have changed people's daily life and work. However …