The rise of blockchain internet of things (biot): Secured, device-to-device architecture and simulation scenarios
Most Internet of Things (IoT) resources are exposed to security risks due to their essential
functionality. IoT devices, such as smartphones and tablets, have a limited network …
functionality. IoT devices, such as smartphones and tablets, have a limited network …
Applying generative machine learning to intrusion detection: A systematic mapping study and review
J Halvorsen, C Izurieta, H Cai… - ACM Computing …, 2024 - dl.acm.org
Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense,
alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to …
alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to …
Application of deep learning to cybersecurity: A survey
S Mahdavifar, AA Ghorbani - Neurocomputing, 2019 - Elsevier
Abstract Cutting edge Deep Learning (DL) techniques have been widely applied to areas
like image processing and speech recognition so far. Likewise, some DL work has been …
like image processing and speech recognition so far. Likewise, some DL work has been …
Deep learning for effective Android malware detection using API call graph embeddings
High penetration of Android applications along with their malicious variants requires efficient
and effective malware detection methods to build mobile platform security. API call …
and effective malware detection methods to build mobile platform security. API call …
DroidMalwareDetector: A novel Android malware detection framework based on convolutional neural network
AT Kabakus - Expert Systems with Applications, 2022 - Elsevier
Smartphones have become an integral part of our daily lives thanks to numerous reasons.
While benefitting from what they offer, it is critical to be aware of the existence of malware in …
While benefitting from what they offer, it is critical to be aware of the existence of malware in …
An improved two-hidden-layer extreme learning machine for malware hunting
Detecting unknown malware and their variants remains both an operational challenge and a
research challenge. In recent years, there have been attempts to design machine learning …
research challenge. In recent years, there have been attempts to design machine learning …
Static malware detection and attribution in android byte-code through an end-to-end deep system
Android reflects a revolution in handhelds and mobile devices. It is a virtual machine based,
an open source mobile platform that powers millions of smartphone and devices and even a …
an open source mobile platform that powers millions of smartphone and devices and even a …
Fusion of medical images using deep belief networks
Image fusion plays a significant role in various computer vision applications. However,
designing an efficient image fusion technique is still a challenging task. In this paper, a novel …
designing an efficient image fusion technique is still a challenging task. In this paper, a novel …
An enhanced stacked LSTM method with no random initialization for malware threat hunting in safety and time-critical systems
AN Jahromi, S Hashemi… - … on Emerging Topics …, 2020 - ieeexplore.ieee.org
Malware detection is an increasingly important operational focus in cyber security,
particularly, given the fast pace of such threats (eg, new malware variants introduced every …
particularly, given the fast pace of such threats (eg, new malware variants introduced every …
A network function virtualization system for detecting malware in large IoT based networks
N Guizani, A Ghafoor - IEEE Journal on Selected Areas in …, 2020 - ieeexplore.ieee.org
The exponential growth in the use of Internet of Things (IoT) devices has introduced
numerous challenges, in particular dealing with new security threats. In addition, for …
numerous challenges, in particular dealing with new security threats. In addition, for …