A survey on deep learning for cybersecurity: Progress, challenges, and opportunities

M Macas, C Wu, W Fuertes - Computer Networks, 2022 - Elsevier
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …

Advancing cybersecurity: a comprehensive review of AI-driven detection techniques

AH Salem, SM Azzam, OE Emam, AA Abohany - Journal of Big Data, 2024 - Springer
As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important
than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly …

A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges

K Bayoudh - Information Fusion, 2024 - Elsevier
In recent years, deep learning algorithms have rapidly revolutionized artificial intelligence,
particularly machine learning, enabling researchers and practitioners to extend previously …

Balancing QoS and security in the edge: Existing practices, challenges, and 6G opportunities with machine learning

ZM Fadlullah, B Mao, N Kato - IEEE Communications Surveys & …, 2022 - ieeexplore.ieee.org
While the emerging 6G networks are anticipated to meet the high-end service quality
demands of the mobile edge users in terms of data rate and delay satisfaction, new attack …

A systematic review on machine learning and deep learning models for electronic information security in mobile networks

C Gupta, I Johri, K Srinivasan, YC Hu, SM Qaisar… - Sensors, 2022 - mdpi.com
Today's advancements in wireless communication technologies have resulted in a
tremendous volume of data being generated. Most of our information is part of a widespread …

A survey of machine and deep learning methods for privacy protection in the internet of things

E Rodríguez, B Otero, R Canal - Sensors, 2023 - mdpi.com
Recent advances in hardware and information technology have accelerated the proliferation
of smart and interconnected devices facilitating the rapid development of the Internet of …

Transfer-learning-based intrusion detection framework in IoT networks

E Rodríguez, P Valls, B Otero, JJ Costa, J Verdú… - Sensors, 2022 - mdpi.com
Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day
attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion …

Application of reinforcement learning and deep learning in multiple-input and multiple-output (MIMO) systems

M Naeem, G De Pietro, A Coronato - Sensors, 2021 - mdpi.com
The current wireless communication infrastructure has to face exponential development in
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …

Path signature-based xai-enabled network time series classification

L Sun, Y Wang, Y Ren, F Xia - Science China Information Sciences, 2024 - Springer
Classifying network time series (NTS) is crucial for automating network administration and
ensuring cyberspace security. It enables the detection of anomalies, the identification of …

Advances in Machine Learning-Driven Cognitive Radio for Wireless Networks: A Survey

NA Khalek, DH Tashman… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The next frontier in wireless connectivity lies at the intersection of cognitive radio (CR)
technology and machine learning (ML), where intelligent networks can provide pervasive …