Zero-day attack detection: a systematic literature review

R Ahmad, I Alsmadi, W Alhamdani… - Artificial Intelligence …, 2023 - Springer
With the continuous increase in cyberattacks over the past few decades, the quest to
develop a comprehensive, robust, and effective intrusion detection system (IDS) in the …

[HTML][HTML] A review of anomaly detection strategies to detect threats to cyber-physical systems

N Jeffrey, Q Tan, JR Villar - Electronics, 2023 - mdpi.com
Cyber-Physical Systems (CPS) are integrated systems that combine software and physical
components. CPS has experienced rapid growth over the past decade in fields as disparate …

Big data analytics deep learning techniques and applications: A survey

HA Selmy, HK Mohamed, W Medhat - Information Systems, 2023 - Elsevier
Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …

DDoS attacks in Industrial IoT: A survey

S Chaudhary, PK Mishra - Computer Networks, 2023 - Elsevier
As the IoT expands its influence, its effect is becoming macroscopic and pervasive. One of
the most discernible effects is in the industries where it is known as Industrial IoT (IIoT). IIoT …

Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

ZT Pritee, MH Anik, SB Alam, JR Jim, MM Kabir… - Computers & …, 2024 - Elsevier
In the continuously developing field of cyber security, user authentication and authorization
play a vital role in protecting personal information and digital assets from unauthorized use …

Deep Learning Approaches for Network Traffic Classification in the Internet of Things (IoT): A Survey

JH Kalwar, S Bhatti - arXiv preprint arXiv:2402.00920, 2024 - arxiv.org
The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive
influx of diverse network traffic from interconnected devices. Effectively classifying this …

Detection of botnet in IoT network through machine learning based optimized feature importance via ensemble models

SM din, R Sharma, F Rizvi, N Sharma - International Journal of Information …, 2024 - Springer
The number of cyberattacks has grown along with the expansion of the Internet of Things
(IoT), which necessitates detection of cyberattacks on IoT devices. Different machine …

[HTML][HTML] A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection

H Kim, S Park, H Hong, J Park, S Kim - Future Internet, 2024 - mdpi.com
As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT
devices are also diversifying. However, the proliferation of IoT devices, often intertwined with …

DTL-IDS: Deep transfer learning-based intrusion detection system in 5G networks

B Farzaneh, N Shahriar, AH Al Muktadir… - … on Network and …, 2023 - ieeexplore.ieee.org
In the complex landscape of modern networks, the necessity of Intrusion Detection System
(IDS) has become paramount. An IDS is a crucial cybersecurity tool that plays a pivotal role …

[HTML][HTML] Applying transfer learning approaches for intrusion detection in software-defined networking

HM Chuang, LJ Ye - Sustainability, 2023 - mdpi.com
In traditional network management, the configuration of routing policies and associated
settings on individual routers and switches was performed manually, incurring a …