6LoWPAN - Technical Features and Challenges in IoT: A Review DOI

Albahlool M Abood,

Walid K. Hasan, Haitham Khaled

et al.

Published: May 19, 2024

Language: Английский

Intelligent detection framework for IoT-botnet detection: DBN-RNN with improved feature set DOI
Sandip Bobade,

Ravindra Apare,

Ravindra H. Borhade

et al.

Journal of Information Security and Applications, Journal Year: 2025, Volume and Issue: 89, P. 103961 - 103961

Published: Jan. 12, 2025

Language: Английский

Citations

2
A novel approach of botnet detection using hybrid deep learning for enhancing security in IoT networks DOI Creative Commons
Shamshair Ali, Rubina Ghazal, Nauman Qadeer

et al.

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 103, P. 88 - 97

Published: June 12, 2024

Language: Английский

Citations

10
Machine learning and metaheuristic optimization algorithms for feature selection and botnet attack detection DOI

Mahdieh Maazalahi,

Soodeh Hosseini

Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Language: Английский

Citations

1
Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives DOI
Deepak Adhikari, Wei Jiang, Jinyu Zhan

et al.

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100665 - 100665

Published: Aug. 23, 2024

Language: Английский

Citations

8
Enhanced botnet detection in IoT networks using zebra optimization and dual-channel GAN classification DOI Creative Commons
Sk. Khaja Shareef,

R. Krishna Chaitanya,

Srinivasulu Chennupalli

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 26, 2024

The Internet of Things (IoT) permeates various sectors, including healthcare, smart cities, and agriculture, alongside critical infrastructure management. However, its susceptibility to malware due limited processing power security protocols poses significant challenges. Traditional antimalware solutions fall short in combating evolving threats. To address this, the research work developed a feature selection-based classification model. At first stage, preprocessing stage enhances dataset quality through data smoothing consistency improvement. Feature selection via Zebra Optimization Algorithm (ZOA) reduces dimensionality, while phase integrates Graph Attention Network (GAN), specifically Dual-channel GAN (DGAN). DGAN incorporates Node Networks Semantic capture intricate IoT device interactions detect anomalous behaviors like botnet activity. model's accuracy is further boosted by leveraging both structural semantic with Sooty Tern (STOA) for hyperparameter tuning. proposed STOA-DGAN model achieves an impressive 99.87% activity classification, showcasing robustness reliability compared existing approaches.

Language: Английский

Citations

6
Developing an Intelligent System for Efficient Botnet Detection in IoT Environment DOI
Ramesh Singh Rawat, Manoj Diwakar, Umang Garg

et al.

International Journal of Mathematical Engineering and Management Sciences, Journal Year: 2025, Volume and Issue: 10(2), P. 537 - 553

Published: Feb. 7, 2025

Smart technological instruments and Internet of Things (IoT) systems are now targeted by network attacks because their widespread rising use. Attackers can take over IoT devices via botnets, pre-configured attack vectors, use them to do harmful actions. Thus, effective machine learning is required solve these security issues. Additionally, deep with the necessary elements advised defend from threats. In order achieve proper detection hacks in future, relevant datasets must be used. The device's operation could occasionally delayed. sample dataset well structured for training model validating suggested create best protection system feasible detecting cyber risks. This paper focused on analyzing botnet traffic an environment using classifiers: Decision tree classifier, Naïve Bayes, K nearest neighbor, Convolution neural network, Recurrent Random Forest. We calculated each algorithm's Accuracy, True Positive, False Negative, Precision, Recall. obtained impressive results CNN, LSTM RNN classifiers. have also achieved a high rate.

Language: Английский

Citations

0
Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review DOI
Saeid Jamshidi, Amin Nikanjam,

Kawser Wazed Nafi

et al.

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101531 - 101531

Published: Feb. 1, 2025

Language: Английский

Citations

0
Securing the Internet of Things: A Comprehensive Review of Ransomware Attacks, Detection, Countermeasures, and Future Prospects DOI Creative Commons
Peizhi Yan,

Tala Talaei Khoei

Franklin Open, Journal Year: 2025, Volume and Issue: unknown, P. 100256 - 100256

Published: March 1, 2025

Language: Английский

Citations

0
Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification DOI

Ashwathy Anda Chacko,

E. Bijolin Edwin, M. Roshni Thanka

et al.

Iran Journal of Computer Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Language: Английский

Citations

0
A new a flow-based approach for enhancing botnet detection using convolutional neural network and long short-term memory DOI Creative Commons
Mehdi Asadi, Arash Heidari, Nima Jafari Navimipour

et al.

Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Language: Английский

Citations

0