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ARTIFICIAL INTELLIGENCE BASED ADVANCED ANOMALY DETECTION FOR TELECOM NETWORKS USING HYBRID DEEP LEARNING AND ENSEMBLE MODELS
Authors
DINESH KARTHIK S,ABIJITH R,SOWMIYA G
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Keywords
Telecom Networks, Anomaly Detection, Artificial Intelligence, Deep Learning, LSTM, Autoencoder, Network Security, Intrusion Detection, Time Series Analysis, Predictive Maintenance, 5G Networks, Machine Learning, Big Data Analytics, Network Monitoring.
Abstract
This study examines the growing complexity of modern telecommunication networks and the increasing risks associated with cyber threats. With the expansion of technologies such as 5G, cloud computing, and IoT, network infrastructures have become more vulnerable to attacks like intrusions and service disruptions. Traditional intrusion detection approaches often fail to identify new and evolving threats due to their reliance on predefined rules
Traditional intrusion detection systems (IDS) rely on static rule-based and signature-based mechanisms that struggle to cope with modern cyber threats. These systems are limited in their ability to detect unknown attacks, adapt to dynamic traffic patterns, and scale with increasing data volumes. Furthermore, high false alarm rates significantly reduce operational efficiency and increase the workload of network administrators.
This research proposes a Artificial Intelligence driven anomaly detection framework tailored for telecom network environments. The proposed system integrates advanced preprocessing techniques, hybrid feature selection using Weighted Adaptive Feature Selection (WAFS), Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and a hybrid ensemble model combining a Deep Neural Network (DeepAnomNet) and Random Forest classifier.
The UNSW-NB15 dataset is employed for training and evaluation as it represents modern network traffic and diverse attack scenarios. The model is evaluated using multiple performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Results demonstrate significant improvements in detection capability and reduction in false positives compared to conventional machine learning models.
A real-time Telecom Security Information and Event Management (SIEM) dashboard is also developed to visualize anomaly probability and threat severity. The proposed system offers a scalable, adaptive, and intelligent solution for securing next-generation telecom infrastructures.
IJCRT's Publication Details
Unique Identification Number - IJCRT26A4415
Paper ID - 307508
Author type - Indian Author
Page Number(s) - m183-m194
Pubished in - Volume 14 | Issue 4 | April 2026
DOI (Digital Object Identifier) -
No Of Downloads - 6
Author Country - India, 605801, Nagalkudi, Nagalkudi, 605801, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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DINESH KARTHIK S,ABIJITH R,SOWMIYA G, "ARTIFICIAL INTELLIGENCE BASED ADVANCED ANOMALY DETECTION FOR TELECOM NETWORKS USING HYBRID DEEP LEARNING AND ENSEMBLE MODELS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 4, pp.m183-m194, April 2026, Available at :http://www.ijcrt.org/papers/IJCRT26A4415.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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