[HTML][HTML] Uniting cyber security and machine learning: Advantages, challenges and future research
Abstract Machine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the
implementation of some systems that can learn from the historical data, identify patterns and …
implementation of some systems that can learn from the historical data, identify patterns and …
[HTML][HTML] Android Malware Detection and Identification Frameworks by Leveraging the Machine and Deep Learning Techniques: A Comprehensive Review
The ever-increasing growth of online services and smart connectivity of devices have posed
the threat of malware to computer system, android-based smart phones, Internet of Things …
the threat of malware to computer system, android-based smart phones, Internet of Things …
Malware detection using image representation of malware data and transfer learning
With the increased proliferation of internet-enabled mobile devices and large internet use,
cybercrime incidents have grown exponentially, often leading to huge financial losses. Most …
cybercrime incidents have grown exponentially, often leading to huge financial losses. Most …
IIoT deep malware threat hunting: from adversarial example detection to adversarial scenario detection
B Esmaeili, A Azmoodeh… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Protecting widely used deep classifiers against black-box adversarial attacks is a recent
research challenge in many security-related areas, including malware classification. This …
research challenge in many security-related areas, including malware classification. This …
A new framework for visual classification of multi-channel malware based on transfer learning
Z Zhao, S Yang, D Zhao - Applied Sciences, 2023 - mdpi.com
With the continuous development and popularization of the Internet, there has been an
increasing number of network security problems appearing. Among them, the rapid growth …
increasing number of network security problems appearing. Among them, the rapid growth …
Dynamic extraction of initial behavior for evasive malware detection
Recently, malware has become more abundant and complex as the Internet has become
more widely used in daily services. Achieving satisfactory accuracy in malware detection is a …
more widely used in daily services. Achieving satisfactory accuracy in malware detection is a …
Hybrid learning with new value function for the maximum common induced subgraph problem
Abstract Maximum Common Induced Subgraph (MCIS) is an important NP-hard problem
with wide real-world applications. An efficient class of MCIS algorithms uses Branch-and …
with wide real-world applications. An efficient class of MCIS algorithms uses Branch-and …
SIHQR model with time delay for worm spread analysis in IIoT-enabled PLC network
G Wu, Y Zhang, H Zhang, S Yu, S Yu, S Shen - Ad Hoc Networks, 2024 - Elsevier
Abstract Industrial Internet of Things (IIoT) is a product of deep integration between Internet
of Things (IoT) and industrial control networks. As an important component of IIoT …
of Things (IoT) and industrial control networks. As an important component of IIoT …
Image‐Based Malware Classification Method with the AlexNet Convolutional Neural Network Model
Z Zhao, D Zhao, S Yang, L Xu - Security and Communication …, 2023 - Wiley Online Library
In recent years, malware has experienced explosive growth and has become one of the
most severe security threats. However, feature engineering easily restricts the traditional …
most severe security threats. However, feature engineering easily restricts the traditional …
Market Research on IIoT Standard Compliance Monitoring Providers and deriving Attributes for IIoT Compliance Monitoring
Adapting security architectures to common standards like IEC 62443 or ISO 27000 in the
Industrial Internet of Things (IIoT) involves complex processes and compliance reports …
Industrial Internet of Things (IIoT) involves complex processes and compliance reports …