High-performance self-compacting concrete with recycled coarse aggregate: Soft-computing analysis of compressive strength

A Alyaseen, A Poddar, N Kumar, S Tajjour… - Journal of Building …, 2023 - Elsevier
The growth of cities and industrialization has led to an increase in demand for concrete,
resulting in resource depletion and environmental issues. Sustainable alternatives such as …

Anomaly detection in 6G networks using machine learning methods

MM Saeed, RA Saeed, M Abdelhaq, R Alsaqour… - Electronics, 2023 - mdpi.com
While the cloudification of networks with a micro-services-oriented design is a well-known
feature of 5G, the 6G era of networks is closely related to intelligent network orchestration …

MLTs-ADCNs: Machine learning techniques for anomaly detection in communication networks

HW Oleiwi, DN Mhawi, H Al-Raweshidy - IEEE Access, 2022 - ieeexplore.ieee.org
From a security perspective, the research of the jeopardized 6G wireless communications
and its expected ultra-densified ubiquitous wireless networks urge the development of a …

A hypertuned lightweight and scalable LSTM model for hybrid network intrusion detection

A Bibi, GA Sampedro, A Almadhor, AR Javed, T Kim - Technologies, 2023 - mdpi.com
Given the increasing frequency of network attacks, there is an urgent need for more effective
network security measures. While traditional approaches such as firewalls and data …

[PDF][PDF] Naive-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets

MB Ressan, RF Hassan - Indonesian Journal of Electrical …, 2022 - researchgate.net
This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate
model to detect different emotions in a tweet. The analysis takes place through several …

Machine learning algorithms for raw and unbalanced intrusion detection data in a multi-class classification problem

M Bacevicius, A Paulauskaite-Taraseviciene - Applied Sciences, 2023 - mdpi.com
Various machine learning algorithms have been applied to network intrusion classification
problems, including both binary and multi-class classifications. Despite the existence of …

Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection

F Safarov, M Basak, R Nasimov, A Abdusalomov… - Future Internet, 2023 - mdpi.com
In the rapidly evolving landscape of internet usage, ensuring robust cybersecurity measures
has become a paramount concern across diverse fields. Among the numerous cyber threats …

High density sensor networks intrusion detection system for anomaly intruders using the slime mould algorithm

MH Alwan, YI Hammadi, OA Mahmood, A Muthanna… - Electronics, 2022 - mdpi.com
The Intrusion Detection System (IDS) is an important feature that should be integrated in
high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic …

Intrusion detection based on ensemble learning for big data classification

F Jemili, R Meddeb, O Korbaa - Cluster Computing, 2024 - Springer
The escalating frequency and sophistication of cyber threats pose significant challenges to
traditional intrusion detection methods. Signature-based misuse detection, hybrid detection …

A Transformer-based network intrusion detection approach for cloud security

Z Long, H Yan, G Shen, X Zhang, H He… - Journal of Cloud …, 2024 - Springer
The distributed architecture of cloud computing necessitates robust defense mechanisms to
secure network-accessible resources against a diverse and dynamic threat landscape. A …