File processing security detection in multi-cloud environments: a process mining approach

X Zhang, L Cui, W Shen, J Zeng, L Du, H He… - Journal of Cloud …, 2023 - Springer
Cloud computing has gained popularity in recent years, but with its rise comes concerns
about data security. Unauthorized access and attacks on cloud-based data, applications …

IoT-based DDoS on cyber physical systems: Research challenges, datasets and future prospects

M Snehi, A Bhandari - 2022 IEEE International IOT, Electronics …, 2022 - ieeexplore.ieee.org
The fusion of widespread IoT devices, highly adopted cloud services, and advanced network
technologies has laid the foundation of Cyber-Physical Systems. However, the architecture …

[HTML][HTML] Service Constraint NCBQ trust orient secure transmission with IoT devices for improved data security in cloud using blockchain

R Premkumar, SS Priya - Measurement: Sensors, 2022 - Elsevier
Problem of secure transmission in cloud systems has been well studied; there exist number
of approaches discussed around the problem. Number of threats has been identified in …

Multi-objective seagull optimization algorithm with deep learning-enabled vulnerability detection for secure cloud environments

M Aljebreen, MA Alohali, H Mahgoub, SS Aljameel… - Sensors, 2023 - mdpi.com
Cloud computing (CC) is an internet-enabled environment that provides computing services
such as networking, databases, and servers to clients and organizations in a cost-effective …

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques-Recent Research Advancements

A Abdallah, A Alkaabi, G Alameri, SH Rafique… - IEEE …, 2024 - ieeexplore.ieee.org
In the rapidly evolving landscape of computing and networking, the concepts of cloud
networks have gained significant prominence. Although the cloud network offers on-demand …

Feature selection algorithm characterization for NIDS using machine and deep learning

J Verma, A Bhandari, G Singh - 2022 IEEE International IOT …, 2022 - ieeexplore.ieee.org
Data dimensionality is increasing at a rapid rate, posing difficulties for traditional mining and
learning algorithms. Commercial NIDS models make use of statistical measures to analyze …

[PDF][PDF] A Time Series Intrusion DetectionMethod Based on SSAE, TCN and Bi-LSTM.

Z He, X Wang, C Li - Computers, Materials & Continua, 2024 - cdn.techscience.cn
In the fast-evolving landscape of digital networks, the incidence of network intrusions has
escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion …

Intrusion Detection Techniques Analysis in Cloud Computing

W Qi, W Wu, H Wang, L Ou, N Hu… - 2023 IEEE 12th …, 2023 - ieeexplore.ieee.org
The rapid growth and widespread usage of cloud computing have brought security concerns
to the forefront. To tackle these concerns, extensive research and development efforts have …

Network Intrusion Detection System Employing Big Data and Intelligent Learning Methods

J Verma, A Bhandari, G Singh - 2022 4th International …, 2022 - ieeexplore.ieee.org
With the steadily increasing use of computer networks and the exponential growth of data
generated from multiple sources, several other practitioners of security management …

Ethical Hacking through Foot Printing: A Machine Learning Strategy

J Verma, V Baggan, I Kaur, M Sethi… - 2023 7th …, 2023 - ieeexplore.ieee.org
Due to the increased reliance on technology in modern times, the likelihood of cyberattacks
has increased. With the advent of remote working and internet business, the threat …