A Deep Learning Approach for Efficient Anomaly Detection in WSNs
DOI:
https://doi.org/10.15837/ijccc.2023.1.4756Keywords:
Anomaly Detection, Autoencoder Neural Network, Data Aggregation, False Positive, Unsupervised Algorithms, Wireless Sensor NetworksAbstract
Data reliability in Wireless Sensor Networks (WSNs) has a substantial influence on their smooth functioning and resource limitations. In a WSN, the data aggregated from clustered sensor nodes are forwarded to the base station for analysis. Anomaly Detection (AD) focuses on detecting outlier data to ensure consistency during data aggregation. As WSNs have critical resource limitations concerning energy consumption and sensor node lifetime, AD is supposed to provide data integrity with minimum energy consumption, which has been an active research problem. Hence, researchers are striving for methods to improve the accuracy of data handled with a concern on the constraints of WSNs. This paper introduces a Feed-forward Autoencoder Neural Network (FANN) model to detect abnormal instances with improved accuracy and reduced energy consumption. The proposed model also acts as a False Positive Reducer intending to reduce false alarms. It has been compared with the other dominant unsupervised algorithms over robustness and other significant metrics with real-time datasets. Relatively, our proposed model yields an improved accuracy with fewer false alarms thereby supporting a sustainable WSN.
References
Miao Xie; Song Han; Biming Tian; Sazia Parvin (2011). Anomaly Detection in Wireless Sensor Networks: A survey, Journal of Network and computer Applications, vol. 34, no.4, pp. 1302-1325, 2011.
https://doi.org/10.1016/j.jnca.2011.03.004
Deqing Wang; Ru Xu; Xiaoyi Hu; Wei Su (2016). Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks, International Journal of Distributed Sensor Networks, pp. 1-14, 2016.
https://doi.org/10.1155/2016/8197606
Barakkath Nisha U; Uma Maheswari N; Venkatesh R; Yasir Abdullah R (2015). Improving Data Accuracy Using Proactive Correlated Fuzzy System in Wireless Sensor Networks, KSII Transactions On Internet And Information Systems, vol.9, pp.1976-7277, 2015.
https://doi.org/10.3837/tiis.2015.09.013
Robert Mitchell; Ing-Ray Chen (2014). A survey of intrusion detection in wireless network applications, Computer Communications, vol. 2 no. 42, pp. 1-23, 2014.
https://doi.org/10.1016/j.comcom.2014.01.012
Bo Sun; Xuemei Shan; Kui Wu; Yang Xiao (2013). Anomaly Detection Based Secure In-Network Aggregation for Wireless Sensor Networks, IEEE Systems Journal, vol. 7, no. 1, pp. 13-25, 2013.
https://doi.org/10.1109/JSYST.2012.2223531
Sapna Singh; Daya Shankar Singh; Shobhit Kumar (2014). Modified Mean Square Algorithm with reduced cost of training and Simulation time for Character Recognition in Backpropagation Neural Network, Advances in Intelligent Systems and Computing, Springer International Publishing, 2014.
https://doi.org/10.1007/978-3-319-02931-3_17
XinqianLiu; Jiadong Ren; Haitao He; Qian Wang; Shengting Sun (2020). A Novel Network Anomaly Detection Method based on Data Balancing and Recursive Feature Addition, KSII Transactions on Internet and Information Systems, Vol. 14, No. 7, July 31, 2020.
https://doi.org/10.3837/tiis.2020.07.020
Sankardas Roy; Mauro Conti; Sanjeev Setia; Sushil Jajodia (2012). Secure data aggregation in wireless sensor networks, IEEE Information Forensics and Security, vol. 7, no. 3, pp. 1040-1052, 2012.
https://doi.org/10.1109/TIFS.2012.2189568
Mohammed Abu Alsheikh; Shaowei Lin; Dusit Niyato; Hwee Pink Tan (2014). Machine learning in Wireless Sensor Network: Algorithm, Strategies & Application, IEEE Communication surveys & tutorials, vol. 16, 2014.
https://doi.org/10.1109/COMST.2014.2320099
Francesco Gullo; Giovanni Ponti; Andrea Tagarelli; Sergio Greco (2017). An information-theoretic approach to hierarchical clustering of uncertain data, Information Science, 402, 199-215, 2017.
https://doi.org/10.1016/j.ins.2017.03.030
G. Yuan; B. Li; Y. Yao; S. Zhang (2017). A deep learning-enabled subspace spectral ensemble clustering approach for web anomaly detection, International Joint Conference on Neural Networks (IJCNN), pp. 3896-3903, 2017.
https://doi.org/10.1109/IJCNN.2017.7966347
Guo Pu; Wang L (2021). A hybrid unsupervised clustering-based anomaly detection method, Tsinghua science, and technology, ISSN 1007-0214 pp 146-153, vol. 26, No 2, 2021.
https://doi.org/10.26599/TST.2019.9010051
Chen. Y; Li. S (2019). A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks, Wireless Personal Communication, 105, 1235-1256, 2019.
https://doi.org/10.1007/s11277-019-06143-1
Ruff. L; Vandermeulen. R; Goernitz. N; Deecke. L; Siddiqui. S.A; Binder. A; Müller. E; Kloft. M (2018) Deep One-class Classification, Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research, 80:4393-4402,2018.
Nurfazrina Mohd Zamry; Anazida Zainal; Murad A. Rassam (2018). Unsupervised anomaly detection for unlabelled Wireless Sensor Networks Data, International Journal Advance Soft Computing Applications, Vol. 10, No. 2, 2018.
Xuehui Wang; Yong Zhang; Hao Liu; Yang Wang; Lichun Wang; Baocai Yin (2018). An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow, Journal of Advanced Transportation, vol. 2018, 12 pages, 2018.
https://doi.org/10.1155/2018/7191549
Aaron Tuor; Samuel Kaplan; Brian Hutchinson; Nicole Nichols; Sean Robinson (2017). Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams, Proceedings of AI for Cyber Security Workshop at AAAI, 2017.
Marwan Ali Albahar; Muhammad Binsawad (2020). Deep Autoencoders and Feedforward Networks based on a New Regularization for Anomaly detection, Security and Communication Networks, Hindawi, 2020.
https://doi.org/10.1155/2020/7086367
Raghavendra Chalapathy; Sanjay Chawla (2019). Deep Learning for Anomaly Detection - A Survey, arXiv.org, 2019.
https://doi.org/10.1145/3394486.3406704
T. Luo; S. G. Nagarajan (2018). Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT, IEEE International Conference on Communications (ICC), pp. 1-6, 2018.
https://doi.org/10.1109/ICC.2018.8422402
Yan Qiao; Xinhong Cui (2020). Fast outlier detection for high-dimensional data of WSN, International Journal of Distributed Sensor Networks, vol. 16(10), 2020.
https://doi.org/10.1177/1550147720963835
Raghavendra Chalapathy; Aditya Krishna Menon; Sanjay Chawla (2019). Anomaly Detection using ONE-CLASS NEURAL NETWORKS, arXiv.org, 2019.
Markus Goldstein; Seiichi Uchida (2016). A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data, Journal.pone.0152173, PPLOS ONE, 2016.
https://doi.org/10.1371/journal.pone.0152173
DaochenZha; Kwei-Herng Lai; Mingyang Wan; Xia Hu (2020). Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning, arxiv.org, 2020.
F. de La Bourdonnaye; C. Teulière; T. Chateau; J. Triesch (2018). Learning of binocular fixations using anomaly detection with deep reinforcement learning, International Joint Conference on Neural Networks (IJCNN), pp. 760-767, 2018.
https://doi.org/10.1109/IJCNN.2017.7965928
Hoc Thai Nguyen; Nguyen Huu Thai (2019). Temporal & Spatial outlier detection in wireless sensor networks, Wiley ETRI Journal, 41(4):437-451, 2019.
https://doi.org/10.4218/etrij.2018-0261
Sahar Kamala; Rabie A. Ramadanb; Fawzy EL-Refai (2016). Smart Outlier Detection of WSN, Facta universitatis - series: Electronics and Energetics, vol. 29, Issue 3, pp. 383-393, 2016.
https://doi.org/10.2298/FUEE1603383K
Jakovljevic; Mihajlo; Elbasani; Ermal; Kim; Jeong-Dong (2021). LLAD: Life-Log Anomaly Detection Based on Recurrent Neural Network LSTM, Journal of Healthcare Engineering, Hindawi, pp. 2040-2295, 2021.
https://doi.org/10.1155/2021/8829403
S. Garg; K. Kaur; N. Kumar; G. Kaddoum; A. Y. Zomaya; R. Ranjan (2019). A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks, IEEE Transactions on Network and Service Management, 16, 3, 924-935, 2019.
https://doi.org/10.1109/TNSM.2019.2927886
Chander. B; Kumaravelan (2021). Outlier Detection in Wireless Sensor Networks with Denoising Auto-Encoder, Advances in Intelligent Systems and Computing, 1382, 2021.
https://doi.org/10.1007/978-3-030-76736-5_35
Chongxuan Li; Jun Zhu; Bo Zhang (2016). Learning to generate with memory, In International Conference on Machine Learning (ICML), pp. 1177-1186, 2016.
Xavier Glorot; YoshuaBengio (2010). Understanding the difficulty of training deep feedforward neural networks, International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
Szandała T (2021). Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks, In: Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903, 2021.
https://doi.org/10.1007/978-981-15-5495-7_11
Nwankpa. C. E; Ijomah. W; Gachagan. A; Marshall. S (2021). Activation functions: comparison of trends in practice and research for deep learning, 2nd International Conference on Computational Sciences and Technology, pp. 124-133, 2021.
Hendrycks. D; Gimpel. K (2016). Gaussian Error Linear Units (GELUs), Machine Learning (cs.LG), arXiv e-prints, 2016.
Gokcesu; Kaan; Hakan Gokcesu (2021). Generalized Huber loss for robust learning and its efficient minimization for a robust statistics, arXiv preprint arXiv:2108.12627, 2021.
Vallez. N; Velasco-Mata. A; Deniz. O (2021). Deep autoencoder for false positive reduction in handgun detection, Neural Computing & Applications, 33, 5885-5895, 2021.
https://doi.org/10.1007/s00521-020-05365-w
Bezerra. F; Wainer.J (2012). A Dynamic Threshold Algorithm for Anomaly Detection in Logs of Process Aware Systems,Journal Information and Data Management, vol. 3, p.316-331, 2012.
https://www.wunderground.com/history/monthly/in/new-delhi/VIDP
Barakkath Nisha Usman; Uma Maheswari Natarajan; Venkatesh Ramalingam; Yasir Abdullah Rabi (2016). Fuzzy based Flat Anomaly Diagnosis and Relief Measures in Distributed Wireless Sensor Network, International Journal of Fuzzy Systems, vol 19, 2016.
https://doi.org/10.1007/s40815-016-0253-2
John. T; Ogbiti; Ukwuoma Henry; Danjuma Salome; Ibrahim Mohammed (2016). Energy Consumption in Wireless Sensor Network, The International Institute for Science, Technology, and Education (IISTE), 2016.
Mohajer. A; Mazoochi. M; Niasar. F.A; Ghadikolayi. A.A; Nabipour. M (2013). Network Coding- Based QoS and Security for Dynamic Interference-Limited Networks, Communications in Computer and Information Science, 370, 2013.
https://doi.org/10.1007/978-3-642-38865-1_29
Leandro A. Villas; Azzedine Boukerche; Daniel L. Guidoni; Horacio A.B.F. de Oliveira; Regina Borges de Araujo; Antonio A.F. Loureiro (2013). An energy-aware Spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks, Computer Communications, 36, 9, 2013.
https://doi.org/10.1016/j.comcom.2012.04.007
A. Mohajer; F. Sorouri; A. Mirzaei; A. Ziaeddini; K. J. Rad, M. Bavaghar (2022). Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks, IEEE Systems Journal, pp. 1-12, 2022.
https://doi.org/10.1109/JSYST.2022.3154162
Mohamed Elshrkawey; Samiha M. Elsherif; M. ElsayedWahed (2018). An Enhancement Approach for Reducing the Energy Consumption in Wireless Sensor Networks, Journal of King Saud University - Computer and Information Sciences, vol. 30, 259-267, 2018.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Arul Jothi S
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
