Detecting Security Threats in Wireless Sensor Networks using Hybrid Network of CNNs and Long Short-Term Memory
Keywords:
Wireless Sensor Network, Deep Learning, LSTM, CNN, Security IssuesAbstract
Wireless Sensor Networks (WSNs) are pervasive in various domains due to their capability to monitor and collect data from the environment. However, the open and distributed nature of WSNs makes them susceptible to security threats and attacks. Detecting and mitigating these attacks is vital to confirming the veracity and reliability of the collected data. In this study, we propose a novel CNN-LSTM hybrid network that makes use of the geographical and temporal information found in sensor data to identify attacks in WSNs. The proposed hybrid network combines the advantages of long short-term memory (LSTM) networks and convolutional neural networks (CNNs). CNNs are used to automatically extract key features from the sensor input and learn spatial representations. In order to identify the temporal dependencies and long-term patterns present in the consecutive sensor readings, the output of the final CNN layer is then fed into the LSTM layers. We conducted experiments utilizing a real-world WSN dataset encompassing a variety of typical actions and various types of attacks to assess the efficacy of our technique. The dataset was carefully curated and labeled to ensure its representativeness and diversity. Our CNN-LSTM hybrid network was evaluated against a number of baseline models, such as standalone CNN and LSTM networks and conventional machine learning techniques frequently employed for attack detection in WSNs. The experimental findings show that, in terms of accuracy, precision, recall, and F1-score, our proposed CNN-LSTM hybrid network outperforms the baseline models. The hybrid network’s ability to capture both spatial and temporal information allows it to better discern subtle attack patterns that might be missed by the standalone CNN or LSTM models. Furthermore, the model exhibits robustness and generalization, effectively detecting various attack scenarios while maintaining low false positive rates.
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Mehmood, A.; Lv, Z.; Lloret, J.; Umar, M.M. ELDC: An Artificial Neural Network Based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs. IEEE Trans. Emerg. Top. Comput. 2020, 8, 106–114.
N. A. Prasad and C. D. Guruprakash, "An ephemeral investigation on energy proficiency mechanisms in WSN," 2017 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Tumkur, 2017, pp. 180-185.
P. N and C. D. Guruprakash, "A Relay Node Scheme for Energy Redeemable and Network Lifespan Enhancement," 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Mangalore, India, 2018, pp. 266-274.
Deng, F.; Yue, X.; Fan, X.; Guan, S.; Xu, Y.; Chen, J. Multisource Energy Harvesting System for a Wireless Sensor Network Node in the Field Environment. IEEE Internet Things J. 2019, 6, 918–927.
Achyutha Prasad, N., Guruprakash, C.D., 2019. A relay node scheme of energy redeemable and network lifespan enhancement for wireless sensor networks and its analysis with standard channel models. International Journal of Innovative Technology and Exploring Engineering 8, 605–612.
Otoum, S.; Kantarci, B.; Mouftah, H.T. On the Feasibility of Deep Learning in Sensor Network Intrusion Detection. IEEE Netw. Lett. 2019, 1, 68–71.
Rekha VS, Siddaraju., “An Ephemeral Analysis on Network Lifetime Improvement Techniques for Wireless Sensor Networks”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, issue 9, 2278-3075, pp. 810–814, 2019.
R. V S and Siddaraju, "Defective Motes Uncovering and Retrieval for Optimized Network," 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 303-313, doi: 10.1109/ICCMC53470.2022.9754109.
Achyutha Prasad, N., Guruprakash, C.D., 2019. A relay mote wheeze for energy saving and network longevity enhancement in WSN. International Journal of Recent Technology and Engineering 8, 8220–8227. doi:10.35940/ijrte.C6707.098319.
Enami, Neda, et al. ”Neural network based energy efficiency in wireless sensor networks: A survey.” International Journal of Computer Science & Engineering Survey 1.1 (2010): 39-53.
Almomani, I., Al-Kasasbeh, B. and Al-Akhras, M., 2016. WSN- DS:A dataset for intrusion detection systems in wireless sensor net- works.Journal of Sensors, 2016.
N. G and G. C. D, "Unsupervised Machine Learning Based Group Head Selection and Data Collection Technique," 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1183-1190, doi: 10.1109/ICCMC53470.2022.9753995.
SUDAR, K. Muthamil, NAGARAJ, P., DEEPALAKSHMI, P., et al. Analysis of Intruder Detection in Big Data Analytics. In : 2021 In- ternational Conference on Computer Communication and Informatics (ICCCI). IEEE, 2021. p. 1-5.
P. K. Pareek, A. P. N, C. Srinivas and J. B. N, "Prediction of Rainfall in Karnataka Region using optimised MVC-LSTM Model," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-8, doi: 10.1109/ICICACS57338.2023.10100324.
Rekha, V.S., Siddaraju (2023). Goodness Ratio and Throughput Improvement Using Multi-criteria LEACH Method in Group Sensing Device Network. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_50.
Hu Z, Wang Z, Wang J, Zeng T. Routing Protocol for Wireless Sensor Network Based on Automatic Cluster Optimization. International Journal of Online and Biomedical Engineering (iJOE). 2018 Dec23;14(12):150- 63.
Achyutha Prasad, N., Guruprakash, C.D., 2019. A two hop relay battery aware mote scheme for energy redeemable and network lifespan improvement in WSN. International Journal of Engineering and Advanced Technology 9, 4785–4791. doi:10.35940/ijeat.A2204.109119.
G. T and U. N. L, "Routing and Security in Wireless Ad-Hoc Networks: State of the Art and Recent Advances," 2023 IEEE Renewable Energy and Sustainable E-Mobility Conference (RESEM), Bhopal, India, 2023, pp. 1-8, doi: 10.1109/RESEM57584.2023.10236306.
S. Chaudhury, N. Achyutha Prasad, S. Chakrabarti, C. A. Kumar and M. A. Elashiri, "The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), 2022, pp. 610-615, doi: 10.1109/ICIEM54221.2022.9853128.
P. B.D, A. Prasad N, Dhanraj and M. T N, "Adaptive Voting Mechanism with Artificial Butterfly Algorithm based Feature Selection for IDS in MANET," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-7, doi: 10.1109/ICICACS57338.2023.10099861.
Nirmala, G., Guruprakash, C.D. (2023). An Overview of Data Aggregation Techniques with Special Sensing Intelligent Device Selection Approaches. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_58.
Achyutha Prasad N., Chaitra H.V., Manjula G., Mohammad Shabaz, Ana Beatriz Martinez-Valencia, Vikhyath K.B., Shrawani Verma, José Luis Arias-Gonzáles, “Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks”, Physical Communication, Volume 58, 2023, 102038, ISSN 1874-4907.
Hebbale, S., Marndi, A., Manjunatha Kumar, B. H., Mohan, B. R. ., Achyutha, P. N., & Pareek, P. K. (2022). A survey on automated medical image classification using deep learning. International Journal of Health Sciences, 6(S1), 7850–7865. https://doi.org/10.53730/ijhs.v6nS1.6791
Murthy, R. K., Dhanraj, S., Manjunath, T. N., Prasad, A. N., Pareek, P. K., & Kumar, H. N. (2022). A human activity recognition using CNN and long term short term memory. International Journal of Health Sciences, 6(S6), 10797–10809. https://doi.org/10.53730/ijhs.v6nS6.12919
Mohan, B. R. ., M, D. ., Bhuria, V. ., Gadde, S. S. ., M, K. ., & N, A. P. . (2023). Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 178–185. https://doi.org/10.17762/ijritcc.v11i3.6333
Prasad N. Achyutha, Sushovan Chaudhury, Subhas Chandra Bose, Rajnish Kler, Jyoti Surve, Karthikeyan Kaliyaperumal, "User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis", Mathematical Problems in Engineering, vol. 2022, Article ID 4644855, 9 pages, 2022. https://doi.org/10.1155/2022/4644855
G, M. ., Deshmukh, P. ., N. L., U. K. ., Macedo, V. D. J. ., K B, V. ., N, A. P. ., & Tiwari, A. K. Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 118–126. https://doi.org/10.17762/ijritcc.v11i3s.6172
Jipeng, T., Neelagar, M. B., & Rekha, V. S. (2021). Design of an embedded control scheme for control of remote appliances. Journal of Advanced Research in Instrumentation and Control Engineering, 7(3 & 4), 5-8.
Hebbale, S., Marndi, A., Achyutha, P. N., Manjula, G., Mohan, B. R., & Jagadeesh, B. N. Automated medical image classification using deep learning. International Journal of Health Sciences, 6(S5), 1650–1667. https://doi.org/10.53730/ijhs.v6nS5.9153.
Murthy, R. K., Dhanraj, S., Manjunath, T. N., Achyutha, P. N., Prasad, A. N., & Gangambika, G. (2022). A survey on human activity recognition using CNN and LSTM. International Journal of Health Sciences, 6(S7), 3408–3417. https://doi.org/10.53730/ijhs.v6nS7.12479.
Kadakadiyavar, S., Prasad, A. N., Pareek, P. K., Vani, V., Rekha, V. S., & Nirmala, G. (2022). Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network. International Journal of Health Sciences, 6(S3), 4295–4306. https://doi.org/10.53730/ijhs.v6nS3.6851.
Prakash, N. C., Narasimhaiah, A. P., Nagaraj, J. B., Pareek, P. K., Sedam, R. V., & Govindhaiah, N. (2022). A survey on NLP based automatic extractive text summarization using spacy. International Journal of Health Sciences, 6(S8), 1514–1525. https://doi.org/10.53730/ijhs.v6nS8.10526.
Sagar, Y. S., and N. Achyutha Prasad. "Charm: a cost-efficient multi-cloud data hosting scheme with high availability." International Journal for Technological Research In Engineering 5.10 (2018).
Manjunatha Kumar, B. H., Achyutha, P. N., Kalashetty, J. N., Rekha, V. S., & Nirmala, G. (2022). Business analysis and modelling of flight delays using artificial intelligence. International Journal of Health Sciences, 6(S1), 7897–7908. https://doi.org/10.53730/ijhs.v6nS1.6735.
Aluka, M. ., Dixit, R. ., & Kumar, P. . (2023). Enhancing and Detecting the Lung Cancer using Deep Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 127–134. https://doi.org/10.17762/ijritcc.v11i3s.6173
Anthony Thompson, Ian Martin, Alejandro Perez, Luis Rodriguez, Diego Rodríguez. Utilizing Machine Learning for Educational Game Design. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/183
Kothandaraman, D., Praveena, N., Varadarajkumar, K., Madhav Rao, B., Dhabliya, D., Satla, S., & Abera, W. (2022). Intelligent forecasting of air quality and pollution prediction using machine learning. Adsorption Science and Technology, 2022 doi:10.1155/2022/5086622
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