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Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
Cloud-based multiclass anomaly detection and categorization using ensemble learning
The world of the Internet and networking is exposed to many cyber-attacks and threats. Over
the years, machine learning models have progressed to be integrated into many scenarios …
the years, machine learning models have progressed to be integrated into many scenarios …
Evaluation of visible contamination on power grid insulators using convolutional neural networks
The contamination of insulators increases their surface conductivity, resulting in a higher
chance of shutdowns occurring. To measure contamination, equivalent salt deposit density …
chance of shutdowns occurring. To measure contamination, equivalent salt deposit density …
PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets
Class imbalance learning is one of the most important topics in the field of machine learning
and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the …
and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the …
A hierarchical intrusion detection model combining multiple deep learning models with attention mechanism
H Xu, L Sun, G Fan, W Li, G Kuang - IEEE Access, 2023 - ieeexplore.ieee.org
In order to ensure the security of computer systems and networks, it is very important to
design and implement intrusion detection systems that can detect and mitigate network …
design and implement intrusion detection systems that can detect and mitigate network …
TCN enhanced novel malicious traffic detection for IoT devices
With the development of IoT technology, more and more IoT devices are connected to the
network. Due to the hardware constraints of IoT devices themselves, it is difficult for …
network. Due to the hardware constraints of IoT devices themselves, it is difficult for …
CMTSNN: A deep learning model for multiclassification of abnormal and encrypted traffic of Internet of Things
With the increasing types and number of Internet of Things (IoT) devices and malicious
programs and the popularization of encryption technology in the communication process …
programs and the popularization of encryption technology in the communication process …
[HTML][HTML] Electricity theft detection based on hybrid random forest and weighted support vector data description
Q Cai, P Li, R Wang - International Journal of Electrical Power & Energy …, 2023 - Elsevier
Improving the detection rate of electricity theft users in smart grids is crucial to the safe
operation of the power system and the economic efficiency of the grid. The traditional …
operation of the power system and the economic efficiency of the grid. The traditional …
Reinforcement learning meets network intrusion detection: a transferable and adaptable framework for anomaly behavior identification
Anomaly detection plays an essential role in network security and traffic classification. Many
studies have focused on anomaly detection to improve network security, including machine …
studies have focused on anomaly detection to improve network security, including machine …
[HTML][HTML] Automatic decision tree-based nidps ruleset generation for dos/ddos attacks
As the occurrence of Denial of Service and Distributed Denial of Service (DoS/DDoS)
attacks increases, the demand for effective defense mechanisms increases. Recognition of …
attacks increases, the demand for effective defense mechanisms increases. Recognition of …