BERTDeep-Ware: A Cross-architecture Malware Detection Solution for IoT Systems
Malware is widely regarded as one of the most severe security threats to modern
technologies. Detecting malware in the Internet of Things (IoT) infrastructures is a critical and …
technologies. Detecting malware in the Internet of Things (IoT) infrastructures is a critical and …
Malware detection in internet of things (IoT) devices using deep learning
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the
internet. With the increasing capacity of data on IoT devices, these devices are becoming …
internet. With the increasing capacity of data on IoT devices, these devices are becoming …
MTHAEL: Cross-architecture IoT malware detection based on neural network advanced ensemble learning
D Vasan, M Alazab, S Venkatraman… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The complexity, sophistication, and impact of malware evolve with industrial revolution and
technology advancements. This article discusses and proposes a robust cross-architecture …
technology advancements. This article discusses and proposes a robust cross-architecture …
A hybrid mechanism for advance IoT malware detection
A Khan, G Choudhary, SK Shandilya… - … Conference on IoT …, 2023 - Springer
IoT malware analysis is a challenging task for researchers worldwide because of its
difficulty. IoT devices lack homogeneous processor architecture and security design, making …
difficulty. IoT devices lack homogeneous processor architecture and security design, making …
Securing Edge Devices: Malware Classification with Dual-Attention Deep Network
G Alandjani - Applied Sciences, 2024 - mdpi.com
Featured Application The proposed method reveals the widespread real-world applicability
of malware detection, particularly in securing IoT devices. Its faster inference speed and high …
of malware detection, particularly in securing IoT devices. Its faster inference speed and high …
A hybrid DL-based detection mechanism for cyber threats in secure networks
The astonishing growth of sophisticated ever-evolving cyber threats and attacks throws the
entire Internet-of-Things (IoT) infrastructure into chaos. As the IoT belongs to the …
entire Internet-of-Things (IoT) infrastructure into chaos. As the IoT belongs to the …
Systemically evaluating the robustness of ML-based IoT malware detectors
The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the
front-line of malicious attacks caused by malicious software. Machine learning (ML) …
front-line of malicious attacks caused by malicious software. Machine learning (ML) …
A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks
The Internet of Things (IoT) is prone to malware assaults due to its simple installation and
autonomous operating qualities. IoT devices have become the most tempting targets of …
autonomous operating qualities. IoT devices have become the most tempting targets of …
LightGBM algorithm for malware detection
M Al-Kasassbeh, MA Abbadi, AM Al-Bustanji - … Computing: Proceedings of …, 2020 - Springer
Abstract In Zero-Day malware challenges, attackers take advantage of every second that the
anti-malware vendor delays identifying the attacking malware signature and provide the …
anti-malware vendor delays identifying the attacking malware signature and provide the …
Cross-architecture Intemet-of-Things malware detection based on graph neural network
C Li, G Shen, W Sun - 2021 International Joint Conference on …, 2021 - ieeexplore.ieee.org
The number of Internet of Things (IoT) devices has exploded in recent years. Due to the
simple implementation and difficult-to-patch firmware, IoT devices are vulnerable to malware …
simple implementation and difficult-to-patch firmware, IoT devices are vulnerable to malware …