Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection DOI Creative Commons
Ibrahim Mutambik

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(21), P. 9848 - 9848

Published: Oct. 28, 2024

The adoption and use of the Internet Things (IoT) have increased rapidly over recent years, cyber threats in IoT devices also become more common. Thus, development a system that can effectively identify malicious attacks reduce security has topic great importance. One most serious comes from botnets, which commonly attack by interrupting networks required for to run. There are number methods be used improve identifying unknown patterns networks, including deep learning machine approaches. In this study, an algorithm named genetic with hybrid learning-based anomaly detection (GA-HDLAD) is developed, aim improving botnets within environment. GA-HDLAD technique addresses problem high dimensionality using during feature selection. Hybrid detect botnets; approach combination recurrent neural (RNNs), extraction techniques (FETs), attention concepts. Botnet involve complex (HDL) method detect. Moreover, FETs model ensures features extracted spatial data, while temporal dependencies captured RNNs. Simulated annealing (SA) utilized select hyperparameters necessary HDL approach. experimentally assessed benchmark botnet dataset, findings reveal provides superior results comparison existing methods.

Language: Английский

MTCR-AE: A Multiscale Temporal Convolutional Recurrent Autoencoder for unsupervised malicious network traffic detection DOI
Mukhtar Ahmed, Jinfu Chen, Ernest Akpaku

et al.

Computer Networks, Journal Year: 2025, Volume and Issue: unknown, P. 111147 - 111147

Published: Feb. 1, 2025

Language: Английский

Citations

0
Generalizability Assessment of Learning‐Based Intrusion Detection Systems for IoT Security: Perspectives of Data Diversity DOI Open Access
Zakir Ahmad Sheikh, Narinder Verma, Yashwant Singh

et al.

Security and Privacy, Journal Year: 2025, Volume and Issue: 8(2)

Published: March 1, 2025

ABSTRACT Machine learning (ML) and deep (DL) models have become vital tools in Intrusion Detection Systems (IDS), yet their effectiveness depends heavily on the quality distribution of training data. This study investigates impact dataset size balance performance ML DL using CIC‐IDS 2017 dataset. Five subsets (20%, 40%, 60%, 80%, 100% dataset) were created to assess across varying sizes. Four models, including Random Forest (RF), Artificial Neural Network, Convolutional Network (CNN), CNN+Long‐Term Short Memory (CNN+LSTM), trained evaluated these subsets, focusing precision, recall, F1‐score. To test model generalizability, a synthetic 20 million over‐sampled samples was generated Synthetic Minority Oversampling Technique, followed by manual under‐sampling create balanced 1.5 with approximately 100 000 per attack class. Upon generalizability assessment already synthetically datasets, CNN+LSTM consistently outperformed other but utilized more time for testing each case. The RF showed weakest performances fastest both scenarios. Moreover, evaluate importance general particular, we also considered NSL‐KDD all four multiple classifications binary classification. Our results highlight dataset, structure models.

Language: Английский

Citations

0
Enhanced security framework for medical data embedding based on octonionic steganographic transforms and FPGA-accelerated integrity verification DOI
Mohamed Amine Tahiri,

Ilham Karmouni,

Ismail Mchichou

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 125, P. 480 - 495

Published: April 22, 2025

Language: Английский

Citations

0
Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection DOI Creative Commons
Ibrahim Mutambik

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(21), P. 9848 - 9848

Published: Oct. 28, 2024

The adoption and use of the Internet Things (IoT) have increased rapidly over recent years, cyber threats in IoT devices also become more common. Thus, development a system that can effectively identify malicious attacks reduce security has topic great importance. One most serious comes from botnets, which commonly attack by interrupting networks required for to run. There are number methods be used improve identifying unknown patterns networks, including deep learning machine approaches. In this study, an algorithm named genetic with hybrid learning-based anomaly detection (GA-HDLAD) is developed, aim improving botnets within environment. GA-HDLAD technique addresses problem high dimensionality using during feature selection. Hybrid detect botnets; approach combination recurrent neural (RNNs), extraction techniques (FETs), attention concepts. Botnet involve complex (HDL) method detect. Moreover, FETs model ensures features extracted spatial data, while temporal dependencies captured RNNs. Simulated annealing (SA) utilized select hyperparameters necessary HDL approach. experimentally assessed benchmark botnet dataset, findings reveal provides superior results comparison existing methods.

Language: Английский

Citations

2