A Comprehensive Study on Machine Learning Algorithms for Wireless Sensor Network Security

Wireless sensor network is one of the most hopeful technologies for its small-shape, low-price, and simply circulated behavior. It may be altered dynamically because of some exterior or interior causes. The consecutive methods have been bluntly programmed that anticipate the networks difficult to react dynamically. For overcoming this kind of situation, machine learning approaches can be exercised to respond correctly. Machine Learning is the procedure of learning from the expertness and actions without human help or re-program. There are many opportunities present in this field and we have highlighted some of them in this survey.

[1]  Jitender Grover,et al.  Security issues in Wireless Sensor Network — A review , 2016, 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO).

[2]  Hong-Tzer Yang,et al.  Load Identification in Neural Networks for a Non-intrusive Monitoring of Industrial Electrical Loads , 2007, CSCWD.

[3]  Mariano García Otero,et al.  Detection of wormhole attacks in wireless sensor networks using range-free localization , 2012, 2012 IEEE 17th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[4]  Tao Liu,et al.  Data-driven link quality prediction using link features , 2014, TOSN.

[5]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jean Hennebert,et al.  A Survey on Intrusive Load Monitoring for Appliance Recognition , 2014, 2014 22nd International Conference on Pattern Recognition.

[7]  Ran Wolff,et al.  Noname manuscript No. (will be inserted by the editor) In-Network Outlier Detection in Wireless Sensor Networks , 2022 .

[8]  Mohsen Guizani,et al.  Machine learning in the Internet of Things: Designed techniques for smart cities , 2019, Future Gener. Comput. Syst..

[9]  Kalaiarasi Sonai Muthu,et al.  Classification Algorithms in Human Activity Recognition using Smartphones , 2012 .

[10]  John Langford,et al.  Agnostic Active Learning Without Constraints , 2010, NIPS.

[11]  Yong Wang,et al.  Predicting link quality using supervised learning in wireless sensor networks , 2007, MOCO.

[12]  Wang Ke,et al.  Attribute-based clustering for information dissemination in wireless sensor networks , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[13]  Richard Demo Souza,et al.  A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks , 2017, IEEE Communications Surveys & Tutorials.

[14]  Cyrus Shahabi,et al.  The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks , 2007, TOSN.

[15]  Majid Alotaibi Security to wireless sensor networks against malicious attacks using Hamming residue method , 2019, EURASIP J. Wirel. Commun. Netw..

[16]  Yusheng Ji,et al.  An Area-Based Approach for Node Replica Detection in Wireless Sensor Networks , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[17]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[18]  Demis Hassabis,et al.  Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm , 2017, ArXiv.

[19]  Sanju Islam,et al.  A Secure Framework for IoT Smart Home by Resolving Session Hijacking , 2020 .

[20]  Qi Hao,et al.  Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey , 2018, IEEE Communications Surveys & Tutorials.

[21]  Robert H. Deng,et al.  Detecting node replication attacks in mobile sensor networks: theory and approaches , 2012, Secur. Commun. Networks.

[22]  Walid Saad,et al.  Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity , 2016, IEEE Access.

[23]  Davide Brunelli,et al.  Wireless Sensor Networks , 2012, Lecture Notes in Computer Science.

[24]  Xiaofan Li,et al.  A Survey on Deep Learning Techniques in Wireless Signal Recognition , 2019, Wirel. Commun. Mob. Comput..

[25]  Anastasios A. Economides,et al.  Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information , 2015, Expert Syst. Appl..

[26]  Y.A. Sekercioglu,et al.  Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[27]  Steve Hanneke,et al.  Theory of Disagreement-Based Active Learning , 2014, Found. Trends Mach. Learn..

[28]  Duc A. Tran,et al.  Localization In Wireless Sensor Networks Based on Support Vector Machines , 2008, IEEE Transactions on Parallel and Distributed Systems.

[29]  João B. Martins,et al.  An approach to localization scheme of wireless sensor networks based on artificial neural networks and Genetic Algorithms , 2012, 10th IEEE International NEWCAS Conference.

[30]  Qiuwei Yang,et al.  Survey of Security Technologies on Wireless Sensor Networks , 2015, J. Sensors.

[31]  Carey L. Williamson,et al.  Offline/realtime traffic classification using semi-supervised learning , 2007, Perform. Evaluation.

[32]  Mehmet Demirci,et al.  A Review of Machine Learning Solutions to Denial-of-Services Attacks in Wireless Sensor Networks , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[33]  Yifeng Zhu,et al.  Localization using neural networks in wireless sensor networks , 2008, MOBILWARE.

[34]  David Silver,et al.  Deep Reinforcement Learning with Double Q-Learning , 2015, AAAI.

[35]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[36]  Antonio Liotta,et al.  Ensembles of incremental learners to detect anomalies in ad hoc sensor networks , 2015, Ad Hoc Networks.

[37]  Erkki Mäkinen,et al.  A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks , 2009, IEEE Transactions on Neural Networks.

[38]  Mingxuan Sun,et al.  Intelligent wireless communications enabled by cognitive radio and machine learning , 2017, China Communications.

[39]  Timothy J. O'Shea,et al.  Semi-supervised radio signal identification , 2016, 2017 19th International Conference on Advanced Communication Technology (ICACT).

[40]  Carlos León,et al.  Giving neurons to sensors. QoS management in wireless sensors networks. , 2006, 2006 IEEE Conference on Emerging Technologies and Factory Automation.

[41]  Yi Min Zhou,et al.  A Trust-Aware and Location-Based Secure Routing Protocol for WSN , 2013, ICRA 2013.

[42]  Anis Koubaa,et al.  Radio Link Quality Estimation in Low-Power Wireless Networks , 2013, Springer Briefs in Electrical and Computer Engineering.

[43]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[44]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[45]  Ying-Chang Liang,et al.  Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[46]  Ingrid Moerman,et al.  A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer , 2020, Electronics.

[47]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[48]  Yingshu Li,et al.  Real time clustering of sensory data in wireless sensor networks , 2009, 2009 IEEE 28th International Performance Computing and Communications Conference.

[49]  Yong Guan,et al.  Lightweight Location Verification Algorithms for Wireless Sensor Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.

[50]  Nirvana Meratnia,et al.  Adaptive and Online One-Class Support Vector Machine-Based Outlier Detection Techniques for Wireless Sensor Networks , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[51]  Diana Bohm,et al.  Computer And Information Security Handbook , 2016 .