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eXtreme gradient boosting algorithm with machine learning: A review
ZA Ali, ZH Abduljabbar, HA Tahir, AB Sallow… - Academic Journal of …, 2023 - cir.nii.ac.jp
< jats: p> The primary task of machine learning is to extract valuable information from the
data that is generated every day, process it to learn from it, and take useful actions. Original …
data that is generated every day, process it to learn from it, and take useful actions. Original …
Defense mechanisms against DDoS attacks in a cloud computing environment: State-of-the-art and research challenges
N Agrawal, S Tapaswi - IEEE Communications Surveys & …, 2019 - ieeexplore.ieee.org
The salient features of cloud computing (such as on-demand self-service, resource pooling,
broad network access, rapid elasticity, and measured service) are being exploited by …
broad network access, rapid elasticity, and measured service) are being exploited by …
Approximating XGBoost with an interpretable decision tree
O Sagi, L Rokach - Information sciences, 2021 - Elsevier
The increasing usage of machine-learning models in critical domains has recently stressed
the necessity of interpretable machine-learning models. In areas like healthcare, finary–the …
the necessity of interpretable machine-learning models. In areas like healthcare, finary–the …
An evolutionary SVM model for DDOS attack detection in software defined networks
Software-Defined Network (SDN) has become a promising network architecture in current
days that provide network operators more control over the network infrastructure. The …
days that provide network operators more control over the network infrastructure. The …
[PDF][PDF] An intrusion detection system for sdn using machine learning
Software Defined Networking (SDN) has emerged as a promising and exciting option for the
future growth of the internet. SDN has increased the flexibility and transparency of the …
future growth of the internet. SDN has increased the flexibility and transparency of the …
A tree-based stacking ensemble technique with feature selection for network intrusion detection
Several studies have used machine learning algorithms to develop intrusion systems (IDS),
which differentiate anomalous behaviours from the normal activities of network systems. Due …
which differentiate anomalous behaviours from the normal activities of network systems. Due …
An efficient XGBoost–DNN-based classification model for network intrusion detection system
P Devan, N Khare - Neural Computing and Applications, 2020 - Springer
There is a steep rise in the trend of the utility of Internet technology day by day. This
tremendous increase ushers in a massive amount of data generated and handled. For …
tremendous increase ushers in a massive amount of data generated and handled. For …
Detection of illicit accounts over the Ethereum blockchain
S Farrugia, J Ellul, G Azzopardi - Expert Systems with Applications, 2020 - Elsevier
The recent technological advent of cryptocurrencies and their respective benefits have been
shrouded with a number of illegal activities operating over the network such as money …
shrouded with a number of illegal activities operating over the network such as money …
Security threats, defense mechanisms, challenges, and future directions in cloud computing
S El Kafhali, I El Mir, M Hanini - Archives of Computational Methods in …, 2022 - Springer
Several new technologies such as the smart cities, the Internet of Things (IoT), and 5G
Internet need services offered by cloud computing for processing and storing more …
Internet need services offered by cloud computing for processing and storing more …
A secure ai-driven architecture for automated insurance systems: Fraud detection and risk measurement
The private insurance sector is recognized as one of the fastest-growing industries. This
rapid growth has fueled incredible transformations over the past decade. Nowadays, there …
rapid growth has fueled incredible transformations over the past decade. Nowadays, there …