Lightweight models for detection of denial‐of‐service attack in wireless sensor networks

Abidoye, Ademola P.; Kabaso, Boniface

doi:10.1049/ntw2.12011

Cited by 8 publications

(3 citation statements)

References 39 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use network simulator NS3 for simulation of this scheme. It takes lot of time to train and test the model [21]. In 2021, Yasser Alharbi propose KNN algorithm for DOS attack detection.…”

Section: Literature Reviewmentioning

confidence: 99%

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Agarwal¹,

Sharma²

2022

IJCDS

View full text Add to dashboard Cite

Various types of security attacks are normal in vehicular communication networks. The current study uses a support vector machine to implement a Point to Point Critical Path Monitoring (P2PCPM) based Denial of Service (DOS) Attack detection technique for Vehicular Communication Network (VCN) resource management. Greatest quality of P2PCPM is that it eliminates attacked nodes from the network for the smooth process of vehicular communication. This scheme works well in terms of accuracy as well as attack detection rate. The whole simulation is made and tried by utilizing MATLAB Software. Simulation result shows 99% accuracy in case of security attack detection as well as reduced training and testing error upto 2%. Experimental results indicate that this scheme has a great efficiency and works well up to 1000 nodes, which is the limitation of current implementation. In future, simulation test may be done for unlimited nodes using similar or other techniques of attack detection.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

Agarwal¹,

Sharma²

2022

IJCDS

View full text Add to dashboard Cite

show abstract

“…Ademola P. Abidoye and Boniface Kabaso [16] presented a methodology for detecting along with eradicating DoS attacks in WSNs. The detection methodology was grounded on utilizing cluster heads that were observing the traffic intensity in clusters.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Intrusion Detection and Prevention System for DDOS Attack in WSN Using SS-LSACNN and TCSLR

Singh,

Sivashankar,

Kundan

et al. 2023

JCSANDM

View full text Add to dashboard Cite

Sensor Nodes (SNs) are utilized by Wireless Sensor Networks (WSNs) to recognize their environment; in addition, the WSN delivers data from sensing nodes to the sink. The WSNs are exposed to several security threats owing to the broadcast performance of transmission along with the increase in the growth of application regions. Countermeasures like Intrusion Detection and Prevention Systems (IDPS) should be adopted to overcome the aforementioned attacks. By implementing these systems, several intrusions can be detected in WSN; also, WSN can be prevented from various security attacks. Therefore, identifying the general attack that influences the SNs mentioned as Distributed Denial of Service (DDoS) attack and recuperating the data utilizing Soft Swish (SS)-Linear Scaling-centered Adam Convolution Neural Network (SS-LSACNN) along with Two’s Compliment Shift Reverse (TCSLR) operation are the intentions of this work. Firstly, for extracting the vital features, the data gathered as of the dataset are utilized. After that, the extracted features are pre-processed. It is then utilized for attack detection. The null features and the redundant data are removed in preprocessing. By employing the Correlation Coefficient-centered Synthetic Minority Oversampling Technique (CC-SMOTE) methodology, data separation regarding classes and data balancing was performed to prevent the imbalance issue. Subsequently, to provide the preprocessed data for attack detection, the Numeralization and feature scaling are executed. After that, by utilizing Chebyshev Distance (CD)-centric K-Means Algorithm (KMA), the real-time SNs are initialized as well as clustered. The data gathered as of the SNs are utilized for attack detection following the clustering phase. Following the detection phase, the data being attacked are amassed in the log file; similarly, the non-attacked data are inputted into the prevention phase. Next, the experiential analysis is carried out for examining the proposed system’s efficacy. The outcomes revealed that the proposed model exhibits 98.15% accuracy, 97.59% sensitivity, 95.72% specificity, and 95.48% F-measure, which displays the proposed model’s efficacy.

show abstract

“…At present, the research on network security is mostly based on the perspective of algorithms [10][11][12]. However, there are many perspectives on the research of network security, and it is also a direction that can combine the spread of malicious software with the dynamics of infectious diseases.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of the Epidemic Model under Pulse Charging in Wireless Rechargeable Sensor Networks

Liu

Huang

et al. 2021

Entropy

View full text Add to dashboard Cite

With the development of wireless sensor networks (WSNs), energy constraints and network security have become the main problems. This paper discusses the dynamic of the Susceptible, Infected, Low-energy, Susceptible model under pulse charging (SILS-P) in wireless rechargeable sensor networks. After the construction of the model, the local stability and global stability of the malware-free T-period solution of the model are analyzed, and the threshold R0 is obtained. Then, using the comparison theorem and Floquet theorem, we obtain the relationship between R0 and the stability. In order to make the conclusion more intuitive, we use simulation to reveal the impact of parameters on R0. In addition, the paper discusses the continuous charging model, and reveals its dynamic by simulation. Finally, the paper compares three charging strategies: pulse charging, continuous charging and non-charging and obtains the relationship between their threshold values and system parameters.

show abstract

Lightweight models for detection of denial‐of‐service attack in wireless sensor networks

Cited by 8 publications

References 39 publications

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management

An Efficient Intrusion Detection and Prevention System for DDOS Attack in WSN Using SS-LSACNN and TCSLR

Modelling and Analysis of the Epidemic Model under Pulse Charging in Wireless Rechargeable Sensor Networks

Contact Info

Product

Resources

About