Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques

A Abid, F Jemili, O Korbaa - Cluster Computing, 2024 - Springer
Intrusion detection in industrial control systems (ICS) is crucial for maintaining secu rity in
modern industries. However, the rapid growth of data generated from various sources …

Systematic analysis of deep learning model for vulnerable code detection

MTB Nazim, MJH Faruk, H Shahriar… - 2022 IEEE 46th …, 2022 - ieeexplore.ieee.org
Software vulnerabilities have become a serious problem with the emergence of new
applications that contain potentially vulnerable or malicious code that can compromise the …

Textural feature based intelligent approach for neurological abnormality detection from brain signal data

MNA Tawhid, S Siuly, K Wang, H Wang - Plos one, 2022 - journals.plos.org
The diagnosis of neurological diseases is one of the biggest challenges in modern
medicine, which is a major issue at the moment. Electroencephalography (EEG) recordings …

Towards data fusion-based big data analytics for intrusion detection

F Jemili - Journal of Information and Telecommunication, 2023 - Taylor & Francis
Intrusion detection is seen as the most promising way for computer security. It is used to
protect computer networks against different types of attacks. The major problem in the …

Research on mechanical equipment fault diagnosis method based on deep learning and information fusion

D Jiang, Z Wang - Sensors, 2023 - mdpi.com
Solving the problem of the transmission of mechanical equipment is complicated, and the
interconnection between equipment components in a complex industrial environment can …

Utilizing ML and DL algorithms for alert classification in intrusion detection and prevention systems: A detailed review

U Dixit, S Bhatia, P Bhatia - 2022 2nd International Conference …, 2022 - ieeexplore.ieee.org
Intrusion detection/prevention systems have attracted much interest in recent years due to
increased online connectivity. In recent years due to COVID pandemic and due to the …

Data fusion and network intrusion detection systems

R Ahmad, I Alsmadi - Cluster Computing, 2024 - Springer
The increasing frequency and sophistication of cyber-attacks pose significant threats to
organizational entities and critical national infrastructure, leading to substantial financial and …

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Z Latif, Q Umer, C Lee, K Sharif, F Li, S Biswas - Sensors, 2022 - mdpi.com
Software-defined networking (SDN) has gained tremendous growth and can be exploited in
different network scenarios, from data centers to wide-area 5G networks. It shifts control logic …

[PDF][PDF] Multi-Valued Neutrosophic Convolutional LSTM for Intrusion Detection.

V Chinnasamy, S Rajasekaran - International Journal of Intelligent …, 2023 - inass.org
Cyber security is an essential area of study because of the positive effects that networks
have on modern society. As it becomes simpler for cybercriminals to launch novel assaults …

A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence

MH Homaei, OM Gutiérrez, JCS Núñez… - arXiv preprint arXiv …, 2023 - arxiv.org
The potential of digital twin technology is yet to be fully realized due to its diversity and
untapped potential. Digital twins enable systems' analysis, design, optimization, and …