[HTML][HTML] The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

B Naik, A Mehta, H Yagnik, M Shah - Complex & Intelligent Systems, 2022 - Springer
Given the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity
experts are seriously considering artificial intelligence as a potential field that can aid …

Analysis of support vector machine-based intrusion detection techniques

BS Bhati, CS Rai - Arabian Journal for Science and Engineering, 2020 - Springer
From the last few decades, people do various transaction activities like air ticket reservation,
online banking, distance learning, group discussion and so on using the internet. Due to …

[HTML][HTML] A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks

M Alqahtani, A Gumaei, H Mathkour… - Sensors, 2019 - mdpi.com
An Intrusion detection system is an essential security tool for protecting services and
infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few …

Mitigating cyber threats through integration of feature selection and stacking ensemble learning: the LGBM and random forest intrusion detection perspective

AK Mishra, S Paliwal - Cluster Computing, 2023 - Springer
The network traffic has observed astounding expansion and is set to explode in the next few
years. Security attacks are becoming more and more synchronized as attackers are involved …

Anomaly detection for insider attacks from untrusted intelligent electronic devices in substation automation systems

X Wang, C Fidge, G Nourbakhsh, E Foo, Z Jadidi… - IEEE …, 2022 - ieeexplore.ieee.org
In recent decades, cyber security issues in IEC 61850-compliant substation automation
systems (SASs) have become growing concerns. Many researchers have developed various …

USWAVG-BS: Under-Sampled Weighted AVeraGed BorderlineSMOTE to handle data intrinsic difficulties

S Mostafaei, A Ahmadi, J Shahrabi - Expert Systems with Applications, 2023 - Elsevier
In two-class classification problems, learning from imbalanced data is a challenging task
due to the bias of machine learning algorithms towards the majority class. It has been shown …

Diverse analysis of data mining and machine learning algorithms to secure computer network

N Kumar, U Kumar - Wireless Personal Communications, 2022 - Springer
Network attacks are becoming more complex, making it more difficult to detect intrusions.
Various research have been done over the years, employing different categorization …

Artificial intelligence for classification and regression tree based feature selection method for network intrusion detection system in various telecommunication …

N Kumar, U Kumar - Computational Intelligence, 2024 - Wiley Online Library
Now a days, secure data communication over computer network system is a major issue in
which impact of feature reduction plays a vital role to secure network by early detection of …

[HTML][HTML] Intrusion detection in machine learning based E-shaped structure with algorithms, strategies and applications in wireless sensor networks

S Kannadhasan, R Nagarajan - Heliyon, 2024 - cell.com
In the everyday world of computer applications, from the cloud to the Internet of Things,
distributed sensor networks are essential (IoT). These computer application devices are …

Intelligent and improved self-adaptive anomaly based intrusion detection system for networks

Z Chiba, N Abghour, K Moussaid… - International Journal …, 2019 - search.proquest.com
With the advent of digital technology, computer networks have developed rapidly at an
unprecedented pace contributing tremendously to social and economic development. They …