1
Department of Information Technology, Karpagam College of Engineering, Coimbatore, India
(sherubha0106@gmail.com)
2
School of Computer Science Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India
(iqbalmecse@gmail.com)
3
Department of Bachelor of Computer Application, The American College, Madurai, India
(aileen.cse@gmail.com)
4
Department of Computer Science and Engineering, University college of Engineering, Ariyalur, India
(arivupra@gmail.com)
5
Department of Computer Science and Engineering, KRP Institute of Engineering and Technology, India
(nan.ecs1@gmail.com)
Abstract :
The adversarial machine learning approaches are modelled to provide a defence mechanism during the prediction of cloning and jamming attacks launched over the wireless communication process. The transmitter is supplied with a pre-trained classifier to analyze the status of the channel based on the sensing nature and determine the other transmission process. The learning method gathers all acknowledgements and fusion made between nodes and the channel's current state to build a learning model that can accurately identify the succeeding transmission constraint caused by network jamming. In this instance, compared to random jamming procedures, an inventive anti-clone detection strategy aims to minimize the number of clones and jamming found throughout the network model. The transmitter analyzes the power restrictions over the sensor networks using the learning-based fisher score (FS). Here, an adversarial network model (ANM-FS) is fused to diminish the computational time to collect the training dataset by examining the incoming samples. With this defence mechanism, the transmitter intends to predict the false prediction rate (FPR) and design a better model for providing a reliable classifier. Systematically, the transmitter identifies the floating of attacks over the network model and adopts the defending mechanism to mislead the injected clone, enhancing the throughput and reducing the prediction error.
Keywords :
Wireless Sensor Networks; clone; jamming; adversarial network model; fisher score; classifier
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| Style | # |
|---|---|
| MLA | P. Sherubha, Mohammed Iqbal, Aileen Chris, Arivazhagi, Nandhagopal Subramani. "Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks." Fusion: Practice and Applications, Vol. 15, No. 1, 2024 ,PP. 180-195 (Doi : https://doi.org/10.54216/FPA.150114) |
| APA | P. Sherubha, Mohammed Iqbal, Aileen Chris, Arivazhagi, Nandhagopal Subramani. (2024). Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks. Journal of Fusion: Practice and Applications, 15 ( 1 ), 180-195 (Doi : https://doi.org/10.54216/FPA.150114) |
| Chicago | P. Sherubha, Mohammed Iqbal, Aileen Chris, Arivazhagi, Nandhagopal Subramani. "Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks." Journal of Fusion: Practice and Applications, 15 no. 1 (2024): 180-195 (Doi : https://doi.org/10.54216/FPA.150114) |
| Harvard | P. Sherubha, Mohammed Iqbal, Aileen Chris, Arivazhagi, Nandhagopal Subramani. (2024). Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks. Journal of Fusion: Practice and Applications, 15 ( 1 ), 180-195 (Doi : https://doi.org/10.54216/FPA.150114) |
| Vancouver | P. Sherubha, Mohammed Iqbal, Aileen Chris, Arivazhagi, Nandhagopal Subramani. Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks. Journal of Fusion: Practice and Applications, (2024); 15 ( 1 ): 180-195 (Doi : https://doi.org/10.54216/FPA.150114) |
| IEEE | P. Sherubha, Mohammed Iqbal, Aileen Chris, Arivazhagi, Nandhagopal Subramani, Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks, Journal of Fusion: Practice and Applications, Vol. 15 , No. 1 , (2024) : 180-195 (Doi : https://doi.org/10.54216/FPA.150114) |