TY  - JOUR
AU  - Rani, Sunbal
AU  - Rahim, Saqib Shahid
PY  - 2025
DA  - 2025/08/18
TI  - Secure Aware Outlier Detection in Underwater Wireless Sensor Networks Using Deep Learning
JO  - ICCK Transactions on Information Security and Cryptography
T2  - ICCK Transactions on Information Security and Cryptography
JF  - ICCK Transactions on Information Security and Cryptography
VL  - 1
IS  - 1
SP  - 13
EP  - 25
DO  - 10.62762/TISC.2025.610386
UR  - https://www.icck.org/article/abs/TISC.2025.610386
KW  - underwater sensor networks (UWSN)
KW  - anomaly detection (AD)
KW  - outlier detection (OD)
KW  - deep learning (DL)
KW  - long short-term memory (LSTM)
AB  - The emerging field of underwater sensor networks (UWSN) has a vital potential shaping the modern and future landscape that assists in measuring water quality, pollution tracking, and identification of underwater habitats. The challenging conditions in the UWSN environments raise data and security concerns in terms of outliers related to the complicated communication system, poor visibility, and limited resources. The data quality and network efficiency may be affected due to these unwanted conditions, giving rise to certain malicious activities in the network. This study aims to enhance the outlier identification process in terms of security and quality perspectives using the Long Short-Term Memory (LSTM) framework. The focus is to identify the temporal patterns and differentiate between various outliers in critical UWSN conditions. Results reveal that the proposed framework achieved high accuracy up to 95% and surpassed the other traditional machine learning models. It is worth mentioning that underwater sensor data have a complicated pattern that can be more appropriately handled using deep learning models, including LSTM, in comparison to traditional machine learning models.
SN  - 3070-2429
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Rani2025Secure,
  author = {Sunbal Rani and Saqib Shahid Rahim},
  title = {Secure Aware Outlier Detection in Underwater Wireless Sensor Networks Using Deep Learning},
  journal = {ICCK Transactions on Information Security and Cryptography},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {13-25},
  doi = {10.62762/TISC.2025.610386},
  url = {https://www.icck.org/article/abs/TISC.2025.610386},
  abstract = {The emerging field of underwater sensor networks (UWSN) has a vital potential shaping the modern and future landscape that assists in measuring water quality, pollution tracking, and identification of underwater habitats. The challenging conditions in the UWSN environments raise data and security concerns in terms of outliers related to the complicated communication system, poor visibility, and limited resources. The data quality and network efficiency may be affected due to these unwanted conditions, giving rise to certain malicious activities in the network. This study aims to enhance the outlier identification process in terms of security and quality perspectives using the Long Short-Term Memory (LSTM) framework. The focus is to identify the temporal patterns and differentiate between various outliers in critical UWSN conditions. Results reveal that the proposed framework achieved high accuracy up to 95\% and surpassed the other traditional machine learning models. It is worth mentioning that underwater sensor data have a complicated pattern that can be more appropriately handled using deep learning models, including LSTM, in comparison to traditional machine learning models.},
  keywords = {underwater sensor networks (UWSN), anomaly detection (AD), outlier detection (OD), deep learning (DL), long short-term memory (LSTM)},
  issn = {3070-2429},
  publisher = {Institute of Central Computation and Knowledge}
}