Intrusion detection in internet of things-based smart farming using hybrid deep learning framework

Kethineni, Keerthi; Pradeepini, G.

doi:10.1007/s10586-023-04052-4

Cited by 6 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author in [11], a new and efficient DL-related architecture, including three levels. This method contains an attached CNN technique by using a BiGRU approach for identifying organized invaders.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Aburasain

2024

IEEE Access

View full text Add to dashboard Cite

Agriculture has played a significant role in the lifespan of human beings for their survival and the better economic growth of the country. Due to its intensified growth, smart agriculture has been a popular domain recently. This aggregates the benefits of any computing technologies, like wireless sensor networks (WSN), drones, Internet of Things (IoT), remote sensing, etc. The IoT system places sensors on agricultural areas to accumulate critical information for the crops and fields to increase the overall rate of productivity. While broadcasting the sensed information in the domains to the target, there is a chance of the existence of cyber-threats, which intruders design to attain access to the content being transferred. Intrusion detection in IoT-based smart farming using deep learning (DL) leverages the power of deep neural networks (DNN) to safeguard agricultural systems from cyber threats. DL algorithms can autonomously detect unauthorized and anomalies activities by analyzing data streams from sensors, IoT devices, and farm management systems. With this motivation, this study designs an Enhanced Black Widow Optimization with Hybrid Deep Learning Enabled Intrusion Detection (EBWO-HDLID) technique in the IoT-based Smart Farming environment. The proposed EBWO-HDLID technique captures complex patterns and detects significant intrusions, assuring the security and reliability of smart farming. In the presented EBWO-HDLID approach, the bald eagle search (BES) algorithm can be used for the feature selection process. For the intrusion detection and classification process, the EBWO-HDLID technique applies the HDL classification model. Finally, the EBWO algorithm can be used for the parameter tuning of the HDL technique. The experimental validation process demonstrates the satisfactory performance of the EBWO-HDLID model on two benchmark datasets: ToN-IoT, and Edge-IIoTset datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Aburasain

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…An internal or external intrusion may occur. Users who have some level of authorized access within the network and want to increase their access rights to abuse prohibited privileges are known as internal invaders [12]. Users who are not part of the intended network and attempt to obtain illicit access to system data are known as external intruders.…”

Section: Introductionmentioning

confidence: 99%

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

Mahalingam,

Perumal,

Subburayalu

et al. 2023

Sensors

View full text Add to dashboard Cite

The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.

show abstract

Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM

et al. 2023

View full text Add to dashboard Cite

Intrusion detection in internet of things-based smart farming using hybrid deep learning framework

Cited by 6 publications

References 26 publications

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM

Contact Info

Product

Resources

About