Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks
Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable ro...
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| Vydané v: | Ad hoc networks Ročník 163; s. 103581 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.10.2024
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| ISSN: | 1570-8705 |
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| Abstract | Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable routing attributes, aiming to establish an artificial intelligence-based attack detection system for WSNs. Our methodology leverages real-time analysis of the features of simulated WSN routing data. By integrating Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM), we develop a robust classification framework. This framework effectively identifies outliers, pinpoints malicious network behaviors from their origins, and categorizes them as either trusted or untrusted network activities. In addition, dimensionality reduction techniques are used to improve interpretability, reduce computation and processing time, extract uncorrelated features from network data, and optimize performances. The main advantage of our approach is to establish an efficient stochastic machine learning method capable of analyzing and filtering WSN traffic to prevent suspicious and unsafe data, reduce the large dissimilarity in the collected routing features, and rapidly detect attacks before they occur. In this work, we exploit a well-tuned data set that provides a lot of routing information without losing any data. The experimental results show that the proposed stochastic attack detection system can effectively identify and categorize anomalies in wireless sensor networks with high accuracy. The classification rates of the system were found to be around 83.65%, 84.94% and 94.55%, which is significantly better than the existing classification approaches. Furthermore, the proposed system showed a positive prediction value of 11.84% higher than the existing approaches. |
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| AbstractList | Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable routing attributes, aiming to establish an artificial intelligence-based attack detection system for WSNs. Our methodology leverages real-time analysis of the features of simulated WSN routing data. By integrating Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM), we develop a robust classification framework. This framework effectively identifies outliers, pinpoints malicious network behaviors from their origins, and categorizes them as either trusted or untrusted network activities. In addition, dimensionality reduction techniques are used to improve interpretability, reduce computation and processing time, extract uncorrelated features from network data, and optimize performances. The main advantage of our approach is to establish an efficient stochastic machine learning method capable of analyzing and filtering WSN traffic to prevent suspicious and unsafe data, reduce the large dissimilarity in the collected routing features, and rapidly detect attacks before they occur. In this work, we exploit a well-tuned data set that provides a lot of routing information without losing any data. The experimental results show that the proposed stochastic attack detection system can effectively identify and categorize anomalies in wireless sensor networks with high accuracy. The classification rates of the system were found to be around 83.65%, 84.94% and 94.55%, which is significantly better than the existing classification approaches. Furthermore, the proposed system showed a positive prediction value of 11.84% higher than the existing approaches. |
| ArticleNumber | 103581 |
| Author | Affane M., Anselme R. Satori, Hassan |
| Author_xml | – sequence: 1 givenname: Anselme R. orcidid: 0000-0001-8606-0108 surname: Affane M. fullname: Affane M., Anselme R. email: anselme.affane@outlook.com – sequence: 2 givenname: Hassan surname: Satori fullname: Satori, Hassan email: hassan.satori@usmba.ac.ma |
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| Keywords | Dimensionality reduction Secure routing algorithm Machine learning Hidden markov models Gaussian mixture models Stochastic attacks detection system Artificial intelligence Wireless sensor networks (WSNs) |
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| References | Bettencourt, Hagberg, Larkey (b28) 2007 Kalkha, Satori, Satori (b39) 2019; 148 Affane, Satori, Sanhaji, Boutazart, Satori (b8) 2023; 35 Titouna, Naït-Abdesselam, Khokhar (b23) 2019; 161 Amish, Vaghela (b41) 2016; 79 Dargie, Poellabauer (b1) 2010 Tripathi, Gaur, Laxmi (b40) 2013; 19 Rassam, Zainal, Maarof (b14) 2015; 18 Emperuman, Chandrasekaran (b37) 2020; 20 Calafate, Manzoni, Cano, Malumbres (b44) 2009; 53 Ghadban, Honeine, Francis, Mourad-Chehade, Farah (b13) 2014 Dias, Bellalta, Oechsner (b17) 2016; 49 Chen, Tan, Gong, Zhang (b36) 2012; 2012 Zhang, Meratnia, Havinga (b25) 2010; 12 Zamry, Zainal, Rassam, Alkhammash, Ghaleb, Saeed (b18) 2021; 21 Bo Sheng, Qun Li, Weizhen Mao, Wen Jin, Outlier detection in sensor networks, in: Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2007, pp. 219–228. Wan, Xiong, Hu, Wang, Shang (b10) 2019; 2019 Park, Cho, Kim (b22) 2018 Zhang, Meratnia, Havinga (b30) 2013; 11 Boutazart, Satori, M., Hamidi, Satori (b33) 2023; 14 Jellali, Atallah, Cherif (b34) 2019 Sundar, Chitradevi, Geetharamani (b20) 2012; 47 Rathore, Park (b19) 2018; 72 Nawaz, Ali, Laghari (b5) 2021; 28 Almomani, Al-Kasasbeh, Al-Akhras (b12) 2016; 2016 Laghari, Jumani, Laghari, Nawaz (b4) 2023; 3 Xie, Han, Tian, Parvin (b15) 2011; 34 Harb, Makhoul, Laiymani, Jaber, Tawil (b9) 2014 Anastasi, Conti, Di Francesco, Passarella (b16) 2009; 7 Lung, Zhou (b7) 2010; 8 Nadeem, Hussain, Owais, Salam, Iqbal, Ahsan (b2) 2015; 83 Meesookho, Narayanan, Raghavendra (b43) 2002 Morell, Correa, Barceló, Vicario (b11) 2016; 15 Coelho, Pinho, Boaventura-Cunha (b21) 2019 Moundounga, Satori, Satori (b6) 2020 Chandola, Banerjee, Kumar (b26) 2009; 41 Farah Sanhaji, H. Satori, K. Satori, Clustering Based on Neural Networks in Wireless Sensor Networks, in: Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems, 2017, pp. 1–6. Salmi, Oughdir (b42) 2023; 10 Winkler, Tuchs, Hughes, Barclay (b3) 2008 McDonald, Sanchez, Madria, Ercal (b24) 2015; 23 Zheng, Yang, Wu (b32) 2018 Sanhaji, Satori, Satori (b35) 2019 Karlof, Wagner (b38) 2003; 1 Zhang, Meratnia, Havinga (b31) 2009 10.1016/j.adhoc.2024.103581_b29 Chandola (10.1016/j.adhoc.2024.103581_b26) 2009; 41 Boutazart (10.1016/j.adhoc.2024.103581_b33) 2023; 14 Wan (10.1016/j.adhoc.2024.103581_b10) 2019; 2019 Amish (10.1016/j.adhoc.2024.103581_b41) 2016; 79 Titouna (10.1016/j.adhoc.2024.103581_b23) 2019; 161 10.1016/j.adhoc.2024.103581_b27 Meesookho (10.1016/j.adhoc.2024.103581_b43) 2002 Nadeem (10.1016/j.adhoc.2024.103581_b2) 2015; 83 Almomani (10.1016/j.adhoc.2024.103581_b12) 2016; 2016 McDonald (10.1016/j.adhoc.2024.103581_b24) 2015; 23 Karlof (10.1016/j.adhoc.2024.103581_b38) 2003; 1 Dargie (10.1016/j.adhoc.2024.103581_b1) 2010 Affane (10.1016/j.adhoc.2024.103581_b8) 2023; 35 Sanhaji (10.1016/j.adhoc.2024.103581_b35) 2019 Rassam (10.1016/j.adhoc.2024.103581_b14) 2015; 18 Jellali (10.1016/j.adhoc.2024.103581_b34) 2019 Sundar (10.1016/j.adhoc.2024.103581_b20) 2012; 47 Coelho (10.1016/j.adhoc.2024.103581_b21) 2019 Zhang (10.1016/j.adhoc.2024.103581_b31) 2009 Xie (10.1016/j.adhoc.2024.103581_b15) 2011; 34 Dias (10.1016/j.adhoc.2024.103581_b17) 2016; 49 Lung (10.1016/j.adhoc.2024.103581_b7) 2010; 8 Bettencourt (10.1016/j.adhoc.2024.103581_b28) 2007 Anastasi (10.1016/j.adhoc.2024.103581_b16) 2009; 7 Laghari (10.1016/j.adhoc.2024.103581_b4) 2023; 3 Tripathi (10.1016/j.adhoc.2024.103581_b40) 2013; 19 Chen (10.1016/j.adhoc.2024.103581_b36) 2012; 2012 Park (10.1016/j.adhoc.2024.103581_b22) 2018 Calafate (10.1016/j.adhoc.2024.103581_b44) 2009; 53 Salmi (10.1016/j.adhoc.2024.103581_b42) 2023; 10 Zhang (10.1016/j.adhoc.2024.103581_b25) 2010; 12 Zhang (10.1016/j.adhoc.2024.103581_b30) 2013; 11 Ghadban (10.1016/j.adhoc.2024.103581_b13) 2014 Emperuman (10.1016/j.adhoc.2024.103581_b37) 2020; 20 Moundounga (10.1016/j.adhoc.2024.103581_b6) 2020 Harb (10.1016/j.adhoc.2024.103581_b9) 2014 Rathore (10.1016/j.adhoc.2024.103581_b19) 2018; 72 Nawaz (10.1016/j.adhoc.2024.103581_b5) 2021; 28 Zamry (10.1016/j.adhoc.2024.103581_b18) 2021; 21 Morell (10.1016/j.adhoc.2024.103581_b11) 2016; 15 Kalkha (10.1016/j.adhoc.2024.103581_b39) 2019; 148 Winkler (10.1016/j.adhoc.2024.103581_b3) 2008 Zheng (10.1016/j.adhoc.2024.103581_b32) 2018 |
| References_xml | – start-page: 1 year: 2019 end-page: 5 ident: b35 article-title: Cluster head selection based on neural networks in wireless sensor networks publication-title: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems – volume: 7 start-page: 537 year: 2009 end-page: 568 ident: b16 article-title: Energy conservation in wireless sensor networks: A survey publication-title: Ad Hoc Netw. – volume: 83 start-page: 363 year: 2015 end-page: 380 ident: b2 article-title: Application specific study, analysis and classification of body area wireless sensor network applications publication-title: Comput. Netw. – volume: 47 year: 2012 ident: b20 article-title: Classification of cardiotocogram data using neural network based machine learning technique publication-title: Int. J. Comput. Appl. – volume: 10 start-page: 1 year: 2023 end-page: 25 ident: b42 article-title: Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network publication-title: J. Big Data – volume: 28 start-page: 1349 year: 2021 end-page: 1369 ident: b5 article-title: UAV communication networks issues: a review publication-title: Arch. Comput. Methods Eng. – volume: 79 start-page: 700 year: 2016 end-page: 707 ident: b41 article-title: Detection and prevention of wormhole attack in wireless sensor network using AOMDV protocol publication-title: Procedia Comput. Sci. – volume: 2012 year: 2012 ident: b36 article-title: PCA-guided routing algorithm for wireless sensor networks publication-title: J. Comput. Netw. Commun. – start-page: 689 year: 2018 end-page: 692 ident: b22 article-title: An effective classification for DoS attacks in wireless sensor networks publication-title: 2018 Tenth International Conference on Ubiquitous and Future Networks – volume: 72 start-page: 79 year: 2018 end-page: 89 ident: b19 article-title: Semi-supervised learning based distributed attack detection framework for IoT publication-title: Appl. Soft Comput. – volume: 49 start-page: 1 year: 2016 end-page: 35 ident: b17 article-title: A survey about prediction-based data reduction in wireless sensor networks publication-title: ACM Comput. Surv. – volume: 8 start-page: 328 year: 2010 end-page: 344 ident: b7 article-title: Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach publication-title: Ad Hoc Netw. – volume: 12 start-page: 159 year: 2010 end-page: 170 ident: b25 article-title: Outlier detection techniques for wireless sensor networks: A survey publication-title: IEEE Commun. Surveys Tutor. – volume: 11 start-page: 1062 year: 2013 end-page: 1074 ident: b30 article-title: Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine publication-title: Ad Hoc Netw. – reference: Farah Sanhaji, H. Satori, K. Satori, Clustering Based on Neural Networks in Wireless Sensor Networks, in: Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems, 2017, pp. 1–6. – start-page: 233 year: 2014 end-page: 236 ident: b13 article-title: Strategies for principal component analysis in wireless sensor networks publication-title: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop – reference: Bo Sheng, Qun Li, Weizhen Mao, Wen Jin, Outlier detection in sensor networks, in: Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2007, pp. 219–228. – start-page: 37 year: 2018 end-page: 49 ident: b32 article-title: An improved distributed PCA-based outlier detection in wireless sensor network publication-title: International Conference on Cloud Computing and Security – volume: 34 start-page: 1302 year: 2011 end-page: 1325 ident: b15 article-title: Anomaly detection in wireless sensor networks: A survey publication-title: J. Netw. Comput. Appl. – volume: 20 start-page: 745 year: 2020 ident: b37 article-title: Hybrid continuous density hmm-based ensemble neural networks for sensor fault detection and classification in wireless sensor network publication-title: Sensors – volume: 148 start-page: 552 year: 2019 end-page: 561 ident: b39 article-title: Preventing black hole attack in wireless sensor network using HMM publication-title: Proc.comput. Sci. – volume: 3 year: 2023 ident: b4 article-title: Unmanned aerial vehicles: A review. publication-title: Cogn. Robot. – start-page: 370 year: 2002 end-page: 374 ident: b43 article-title: Collaborative classification applications in sensor networks publication-title: Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002 – year: 2010 ident: b1 article-title: Fundamentals of wireless sensor networks: theory and practice – volume: 41 start-page: 1 year: 2009 end-page: 58 ident: b26 article-title: Anomaly detection: A survey publication-title: ACM Comput. Surveys (CSUR) – start-page: 174 year: 2019 end-page: 179 ident: b34 article-title: Principal component analysis based clustering approach for WSN with locally uniformly correlated data publication-title: 2019 15th International Wireless Communications & Mobile Computing Conference – volume: 2016 year: 2016 ident: b12 article-title: WSN-DS: A dataset for intrusion detection systems in wireless sensor networks publication-title: J. Sensors – start-page: 1 year: 2020 end-page: 7 ident: b6 article-title: An overview of routing techniques in WSNs publication-title: 2020 Fourth International Conference on Intelligent Computing in Data Sciences – volume: 2019 start-page: 1 year: 2019 end-page: 11 ident: b10 article-title: Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks publication-title: EURASIP J. Wireless Commun. Networking – volume: 23 start-page: 163 year: 2015 end-page: 182 ident: b24 article-title: A survey of methods for finding outliers in wireless sensor networks publication-title: J. Netw. Syst. Manag. – volume: 14 year: 2023 ident: b33 article-title: COVID-19 dataset clustering based on K-means and EM algorithms publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 18 start-page: 85 year: 2015 end-page: 101 ident: b14 article-title: Principal component analysis–based data reduction model for wireless sensor networks publication-title: Int. J. Ad Hoc Ubiquitous Comput. – volume: 53 start-page: 2586 year: 2009 end-page: 2600 ident: b44 article-title: Markovian-based traffic modeling for mobile ad hoc networks publication-title: Comput. Netw. – year: 2019 ident: b21 article-title: Hidden Markov Models: Theory and Implementation using MATLAB® – start-page: 990 year: 2009 end-page: 995 ident: b31 article-title: Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks publication-title: 2009 International Conference on Advanced Information Networking and Applications Workshops – start-page: 223 year: 2007 end-page: 239 ident: b28 article-title: Separating the wheat from the chaff: Practical anomaly detection schemes in ecological applications of distributed sensor networks publication-title: International Conference on Distributed Computing in Sensor Systems – volume: 161 start-page: 93 year: 2019 end-page: 101 ident: b23 article-title: DODS: A distributed outlier detection scheme for wireless sensor networks publication-title: Comput. Netw. – start-page: 434 year: 2014 end-page: 441 ident: b9 article-title: K-means based clustering approach for data aggregation in periodic sensor networks publication-title: 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications – volume: 15 start-page: 3908 year: 2016 end-page: 3919 ident: b11 article-title: Data aggregation and principal component analysis in WSNs publication-title: IEEE Trans. Wireless Commun. – start-page: 37 year: 2008 end-page: 45 ident: b3 article-title: Theoretical and practical aspects of military wireless sensor networks publication-title: J. Telecommun. Inf. Technol. – volume: 21 start-page: 8017 year: 2021 ident: b18 article-title: Lightweight anomaly detection scheme using incremental principal component analysis and support vector machine publication-title: Sensors – volume: 19 start-page: 1101 year: 2013 end-page: 1107 ident: b40 article-title: Comparing the impact of black hole and gray hole attack on LEACH in WSN publication-title: Procedia Comput. Sci. – volume: 1 start-page: 293 year: 2003 end-page: 315 ident: b38 article-title: Secure routing in wireless sensor networks: Attacks and countermeasures publication-title: Ad Hoc Netw. – volume: 35 start-page: 5381 year: 2023 end-page: 5393 ident: b8 article-title: Energy enhancement of routing protocol with hidden Markov model in wireless sensor networks publication-title: Neural Comput. Appl. – volume: 2016 year: 2016 ident: 10.1016/j.adhoc.2024.103581_b12 article-title: WSN-DS: A dataset for intrusion detection systems in wireless sensor networks publication-title: J. Sensors doi: 10.1155/2016/4731953 – volume: 14 issue: 3 year: 2023 ident: 10.1016/j.adhoc.2024.103581_b33 article-title: COVID-19 dataset clustering based on K-means and EM algorithms publication-title: Int. J. Adv. Comput. Sci. Appl. – volume: 10 start-page: 1 issue: 1 year: 2023 ident: 10.1016/j.adhoc.2024.103581_b42 article-title: Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network publication-title: J. Big Data doi: 10.1186/s40537-023-00692-w – volume: 148 start-page: 552 year: 2019 ident: 10.1016/j.adhoc.2024.103581_b39 article-title: Preventing black hole attack in wireless sensor network using HMM publication-title: Proc.comput. Sci. doi: 10.1016/j.procs.2019.01.028 – volume: 53 start-page: 2586 issue: 14 year: 2009 ident: 10.1016/j.adhoc.2024.103581_b44 article-title: Markovian-based traffic modeling for mobile ad hoc networks publication-title: Comput. Netw. doi: 10.1016/j.comnet.2009.05.007 – volume: 49 start-page: 1 issue: 3 year: 2016 ident: 10.1016/j.adhoc.2024.103581_b17 article-title: A survey about prediction-based data reduction in wireless sensor networks publication-title: ACM Comput. Surv. doi: 10.1145/2996356 – start-page: 233 year: 2014 ident: 10.1016/j.adhoc.2024.103581_b13 article-title: Strategies for principal component analysis in wireless sensor networks – start-page: 990 year: 2009 ident: 10.1016/j.adhoc.2024.103581_b31 article-title: Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks – volume: 8 start-page: 328 issue: 3 year: 2010 ident: 10.1016/j.adhoc.2024.103581_b7 article-title: Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2009.09.004 – ident: 10.1016/j.adhoc.2024.103581_b27 doi: 10.1145/3167486.3167505 – volume: 20 start-page: 745 issue: 3 year: 2020 ident: 10.1016/j.adhoc.2024.103581_b37 article-title: Hybrid continuous density hmm-based ensemble neural networks for sensor fault detection and classification in wireless sensor network publication-title: Sensors doi: 10.3390/s20030745 – start-page: 1 year: 2020 ident: 10.1016/j.adhoc.2024.103581_b6 article-title: An overview of routing techniques in WSNs – volume: 161 start-page: 93 year: 2019 ident: 10.1016/j.adhoc.2024.103581_b23 article-title: DODS: A distributed outlier detection scheme for wireless sensor networks publication-title: Comput. Netw. doi: 10.1016/j.comnet.2019.06.014 – ident: 10.1016/j.adhoc.2024.103581_b29 doi: 10.1145/1288107.1288137 – volume: 2012 year: 2012 ident: 10.1016/j.adhoc.2024.103581_b36 article-title: PCA-guided routing algorithm for wireless sensor networks publication-title: J. Comput. Netw. Commun. – start-page: 1 year: 2019 ident: 10.1016/j.adhoc.2024.103581_b35 article-title: Cluster head selection based on neural networks in wireless sensor networks – volume: 83 start-page: 363 year: 2015 ident: 10.1016/j.adhoc.2024.103581_b2 article-title: Application specific study, analysis and classification of body area wireless sensor network applications publication-title: Comput. Netw. doi: 10.1016/j.comnet.2015.03.002 – volume: 41 start-page: 1 issue: 3 year: 2009 ident: 10.1016/j.adhoc.2024.103581_b26 article-title: Anomaly detection: A survey publication-title: ACM Comput. Surveys (CSUR) doi: 10.1145/1541880.1541882 – volume: 3 year: 2023 ident: 10.1016/j.adhoc.2024.103581_b4 article-title: Unmanned aerial vehicles: A review. publication-title: Cogn. Robot. – volume: 47 issue: 14 year: 2012 ident: 10.1016/j.adhoc.2024.103581_b20 article-title: Classification of cardiotocogram data using neural network based machine learning technique publication-title: Int. J. Comput. Appl. – start-page: 174 year: 2019 ident: 10.1016/j.adhoc.2024.103581_b34 article-title: Principal component analysis based clustering approach for WSN with locally uniformly correlated data – volume: 34 start-page: 1302 issue: 4 year: 2011 ident: 10.1016/j.adhoc.2024.103581_b15 article-title: Anomaly detection in wireless sensor networks: A survey publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2011.03.004 – volume: 7 start-page: 537 issue: 3 year: 2009 ident: 10.1016/j.adhoc.2024.103581_b16 article-title: Energy conservation in wireless sensor networks: A survey publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2008.06.003 – volume: 72 start-page: 79 year: 2018 ident: 10.1016/j.adhoc.2024.103581_b19 article-title: Semi-supervised learning based distributed attack detection framework for IoT publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.05.049 – start-page: 37 year: 2008 ident: 10.1016/j.adhoc.2024.103581_b3 article-title: Theoretical and practical aspects of military wireless sensor networks publication-title: J. Telecommun. Inf. Technol. – volume: 21 start-page: 8017 issue: 23 year: 2021 ident: 10.1016/j.adhoc.2024.103581_b18 article-title: Lightweight anomaly detection scheme using incremental principal component analysis and support vector machine publication-title: Sensors doi: 10.3390/s21238017 – volume: 23 start-page: 163 issue: 1 year: 2015 ident: 10.1016/j.adhoc.2024.103581_b24 article-title: A survey of methods for finding outliers in wireless sensor networks publication-title: J. Netw. Syst. Manag. doi: 10.1007/s10922-013-9287-z – volume: 19 start-page: 1101 year: 2013 ident: 10.1016/j.adhoc.2024.103581_b40 article-title: Comparing the impact of black hole and gray hole attack on LEACH in WSN publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2013.06.155 – volume: 1 start-page: 293 issue: 2–3 year: 2003 ident: 10.1016/j.adhoc.2024.103581_b38 article-title: Secure routing in wireless sensor networks: Attacks and countermeasures publication-title: Ad Hoc Netw. doi: 10.1016/S1570-8705(03)00008-8 – volume: 11 start-page: 1062 issue: 3 year: 2013 ident: 10.1016/j.adhoc.2024.103581_b30 article-title: Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2012.11.001 – volume: 15 start-page: 3908 issue: 6 year: 2016 ident: 10.1016/j.adhoc.2024.103581_b11 article-title: Data aggregation and principal component analysis in WSNs publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2016.2531041 – start-page: 689 year: 2018 ident: 10.1016/j.adhoc.2024.103581_b22 article-title: An effective classification for DoS attacks in wireless sensor networks – volume: 28 start-page: 1349 year: 2021 ident: 10.1016/j.adhoc.2024.103581_b5 article-title: UAV communication networks issues: a review publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-020-09418-0 – year: 2019 ident: 10.1016/j.adhoc.2024.103581_b21 – volume: 12 start-page: 159 issue: 2 year: 2010 ident: 10.1016/j.adhoc.2024.103581_b25 article-title: Outlier detection techniques for wireless sensor networks: A survey publication-title: IEEE Commun. Surveys Tutor. doi: 10.1109/SURV.2010.021510.00088 – year: 2010 ident: 10.1016/j.adhoc.2024.103581_b1 – volume: 18 start-page: 85 issue: 1–2 year: 2015 ident: 10.1016/j.adhoc.2024.103581_b14 article-title: Principal component analysis–based data reduction model for wireless sensor networks publication-title: Int. J. Ad Hoc Ubiquitous Comput. doi: 10.1504/IJAHUC.2015.067756 – start-page: 223 year: 2007 ident: 10.1016/j.adhoc.2024.103581_b28 article-title: Separating the wheat from the chaff: Practical anomaly detection schemes in ecological applications of distributed sensor networks – volume: 35 start-page: 5381 issue: 7 year: 2023 ident: 10.1016/j.adhoc.2024.103581_b8 article-title: Energy enhancement of routing protocol with hidden Markov model in wireless sensor networks publication-title: Neural Comput. Appl. doi: 10.1007/s00521-022-07970-3 – start-page: 434 year: 2014 ident: 10.1016/j.adhoc.2024.103581_b9 article-title: K-means based clustering approach for data aggregation in periodic sensor networks – volume: 2019 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.adhoc.2024.103581_b10 article-title: Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks publication-title: EURASIP J. Wireless Commun. Networking doi: 10.1186/s13638-019-1374-8 – start-page: 37 year: 2018 ident: 10.1016/j.adhoc.2024.103581_b32 article-title: An improved distributed PCA-based outlier detection in wireless sensor network – volume: 79 start-page: 700 year: 2016 ident: 10.1016/j.adhoc.2024.103581_b41 article-title: Detection and prevention of wormhole attack in wireless sensor network using AOMDV protocol publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2016.03.092 – start-page: 370 year: 2002 ident: 10.1016/j.adhoc.2024.103581_b43 article-title: Collaborative classification applications in sensor networks |
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| SubjectTerms | Artificial intelligence Dimensionality reduction Gaussian mixture models Hidden markov models Machine learning Secure routing algorithm Stochastic attacks detection system Wireless sensor networks (WSNs) |
| Title | Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks |
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