Malicious Traffic Classification in WSN using Deep Learning Approaches
- Title
- Malicious Traffic Classification in WSN using Deep Learning Approaches
- Creator
- Gupta C.L.P.; Rajassekharan D.; Sharma D.K.; Elangovan M.; Myilsamy V.; Upreti K.
- Description
- Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. 2023 IEEE.
- Source
- 2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 426-431.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- data routing; deep learning; LSTM; malicious transmission; neural network; Traffic data; transmission; WSN
- Coverage
- Gupta C.L.P., Bansal Institute of Engineering and Technology, Dept. of Computer Science and Engineering, Lucknow, India; Rajassekharan D., Peninsula College, Faculty of Computer Science, Peninsula, Malaysia; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Elangovan M., K. S. Rangasamy College of Technology, Department of Artificial Intelligence and Data Science, Tamil Nadu, Tiruchengode, 637215, India; Myilsamy V., V.S.B College of Engineering Technical Campus, Department of Electrical and Electronics Engineering, Tamil Nadu, Coimbatore, 642109, India; Upreti K., CHRIST(Deemed to Be University), Department of Computer Science, Delhi-NCR, Ghaziabad, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036996-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Gupta C.L.P.; Rajassekharan D.; Sharma D.K.; Elangovan M.; Myilsamy V.; Upreti K., “Malicious Traffic Classification in WSN using Deep Learning Approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19707.