More Web Proxy on the site http://driver.im/

research-article

Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things

Authors:

Abdel-Mehsen Ahmad,

Joaquin Garcia-AlfaroAuthors Info & Claims

ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security

Article No.: 79, Pages 1 - 10

https://doi.org/10.1145/3407023.3407048

Published: 25 August 2020 Publication History

Abstract

The insecure growth of Internet-of-Things (IoT) can threaten its promising benefits to our daily life activities. Weak designs, low computational capabilities, and faulty protocol implementations are just a few examples that explain why IoT devices are nowadays highly prone to cyber-attacks. In this survey paper, we review approaches addressing this problem. We focus on machine learning-based solutions as a representative trend in the related literature. We survey and classify Machine Learning (ML)-based techniques that are suitable for the construction of Intrusion Detection Systems (IDS) for IoT. We contribute with a detailed classification of each approach based on our own taxonomy. Open issues and research challenges are also discussed and provided.

References

[1]

Y. Yang, L. Wu, G. Yin, L. Li, and H. Zhao. A survey on security and privacy issues in internet-of-things. IEEE Internet of Things Journal, 4(5):1250--1258, 2017. ISSN 2372-2541.

[2]

Daniele Miorandi, Sabrina Sicari, Francesco De Pellegrini, and Imrich Chlamtac. Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7):1497--1516, 2012. ISSN 1570-8705.

Digital Library

[3]

Biljana L. Risteska Stojkoska and Kire V. Trivodaliev. A review of internet of things for smart home: Challenges and solutions. Journal of Cleaner Production, 140:1454-1464, 2017. ISSN 0959-6526.

[4]

J. Du and S. Chao. A study of information security for m2m of iot. In 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), volume 3, pages V3--576--V3--579, 2010.

[5]

J. Lee and H. Kim. Security and privacy challenges in the internet of things [security and privacy matters]. IEEE Consumer Electronics Magazine, 6(3):134--136, 2017. ISSN 2162-2256.

[6]

M. Plachkinova and C. Maurer. Teaching case security breach at target. Journal of Information Systems Education, 29(1):11--20, 2018. ISSN 10553096.

[7]

Sergio Castillo-Perez and Joaquin Garcia-Alfaro. Spyware-based Menaces Against Web Applications. In International Conference on Intelligent Networking and Collaborative Systems (INCOS'09), pages 409--412. IEEE, November 2009. URL http://dx.doi.org/10.1109/INCOS.2009.31.

Digital Library

[8]

Nizar Kheir, Gregory Blanc, Hervé Debar, Joaquin Garcia-Alfaro, and Dingqi Yang. Automated classification of C & C connections through malware URL clustering. In IFIP International Information Security Conference, pages 252--266. Springer, 2015. URL http://dx.doi.org/10.1007/978-3-319-18467-8_17.

[9]

Qi Jing, Athanasios V. Vasilakos, Jiafu Wan, Jingwei Lu, and Dechao Qiu. Security of the internet of things: perspectives and challenges. Wireless Networks, 20(8): 2481--2501, 2014. ISSN 1572-8196.

Digital Library

[10]

H. Ning, H. Liu, and L. T. Yang. Cyberentity security in the internet of things. Computer, 46(4):46--53, 2013. ISSN 1558-0814.

Digital Library

[11]

S. Sicari, A. Rizzardi, L.A. Grieco, and A. Coen-Porisini. Security, privacy and trust in internet of things: The road ahead. Computer Networks, 76:146--164, 2015. ISSN 1389-1286.

Digital Library

[12]

Jun Zhou, Zhenfu Cao, Xiaolei Dong, and Athanasios V. Vasilakos. Security and privacy for cloud-based iot: Challenges. Comm. Mag., 55(1):26âĂŞ33, 2017. ISSN 0163-6804.

Digital Library

[13]

Sukhvir Notra, Muhammad Siddiqi, Hassan Habibi Gharakheili, Vijay Sivaraman, and Roksana Boreli. An experimental study of security and privacy risks with emerging household appliances. 2014 IEEE Conference on Communications and Network Security, pages 79--84, 2014.

[14]

Bruno Bogaz ZarpelÃčo, Rodrigo Sanches Miani, ClÃąudio Toshio Kawakani, and Sean Carlisto de Alvarenga. A survey of intrusion detection in internet of things. Journal of Network and Computer Applications, 84:25--37, 2017. ISSN 1084-8045.

Digital Library

[15]

Mohiuddin Ahmed, Abdun Naser Mahmood, and Jiankun Hu. A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60:19--31, 2016. ISSN 1084-8045.

Digital Library

[16]

Kelton A.P. Costa, Luis A.M. Pereira, Rodrigo Y.M. Nakamura, Clayton R. Pereira, JoÃčo P. Papa, and Alexandre Xavier FalcÃčo. A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks. Information Sciences, 294:95--108, 2015. ISSN 0020-0255.

Digital Library

[17]

Liang Xiao, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang, and Di Wu. Iot security techniques based on machine learning. CoRR, abs/1801.06275, 2018. URL http://arxiv.org/abs/1801.06275.

[18]

Qais Saif Qassim, Norziana Jamil, Maslina Daud, Ahmed Patel, and Norhamadi JaâĂ&Zacute;affar. A survey of intrusion detection and prevention systems. Information Management & Computer Security, 18(4):277--290, 2010. ISSN 0968-5227.

[19]

John R. Vacca. Computer and Information Security Handbook. Morgan Kaufmann, 2013. ISBN978-0-12-394397-2.

[20]

Robert Mitchell and Ing-Ray Chen. A survey of intrusion detection techniques for cyber-physical systems. ACM Computing Surveys (CSUR), 46(4):55:1--55:29, 2014. ISSN 0360-0300.

Digital Library

[21]

Hung-Jen Liao, Chun-Hung Richard Lin, Ying-Chih Lin, and Kuang-Yuan Tung. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1):16--24, 2013. ISSN 1084-8045.

Digital Library

[22]

J. P. Amaral, L. M. Oliveira, J. J. P. C. Rodrigues, G. Han, and L. Shu. Policy and network-based intrusion detection system for ipv6-enabled wireless sensor networks. In 2014 IEEE International Conference on Communications (ICC), pages 1796--1801, 2014.

[23]

I. Butun, S. D. Morgera, and R. Sankar. A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys Tutorials, 16(1):266--282, 2014. ISSN 2373-745X.

[24]

Hezam Akram Abdul-Ghani, Dimitri Konstantas, and Mohammed Mahyoub. A comprehensive iot attacks survey based on a building-blocked reference model. International Journal of Advanced Computer Science and Applications, 9(3), 2018.

[25]

Tom M. Mitchell. Machine Learning. McGraw Hill, 1997. ISBN 97800704280720070428077.

Digital Library

[26]

Taiwo Oladipupo Ayodele. New Advances in Machine Learning. InTech, 2010. ISBN 978-953-307-034-6.

[27]

P. Langley and H.A. Simon. Applications of machine learning and rule induction. Communications of the ACM, 38(11):54--64, 1995.

Digital Library

[28]

N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki. Network intrusion detection for iot security based on learning techniques. IEEE Communications Surveys Tutorials, 21(3):2671--2701, 2019. ISSN 2373-745X.

[29]

C. Kolias, G. Kambourakis, A. Stavrou, and S. Gritzalis. Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset. IEEE Communications Surveys Tutorials, 18(1):184--208, 2016. ISSN 2373-745X.

Digital Library

[30]

D. Praveen Kumar, Tarachand Amgoth, and Chandra Sekhara Rao Annavarapu. Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49:1--25, 2019. ISSN 1566-2535.

Digital Library

[31]

Vilhelm Verendel. Quantified security is a weak hypothesis: A critical survey of results and assumptions. In Proceedings of the 2009 Workshop on New Security Paradigms Workshop, pages 37--50, 2009. ISBN 978-1-60558-845-2.

Digital Library

[32]

Behrouz Pourghebleh and Nima Jafari Navimipour. Towards efficient data collection mechanisms in the vehicular ad hoc networks. International Journal of Communication Systems, 32(5):e3893, 2019.

[33]

Bahram Hajimirzaei and Nima Jafari Navimipour. Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express, 5(1):56--59, 2019. ISSN 2405-9595.

[34]

Yalda Ebadi and Nima Jafari Navimipour. An energy-aware method for data replication in the cloud environments using a tabu search and particle swarm optimization algorithm. Concurrency and Computation: Practice and Experience, 31(1):e4757, 2019.

[35]

Somayye Hajiheidari, Karzan Wakil, Maryam Badri, and Nima Jafari Navimipour. Intrusion detection systems in the internet of things: A comprehensive investigation. Computer Networks, 160:165--191, 2019. ISSN 1389-1286.

Digital Library

[36]

Kelton A.P. da Costa, JoÃčo P. Papa, Celso O. Lisboa, Roberto Munoz, and Victor Hugo C. de Albuquerque. Internet of things: A survey on machine learning-based intrusion detection approaches. Computer Networks, 151:147--157, 2019. ISSN 1389-1286.

Digital Library

[37]

Bruno Bogaz ZarpelÃčo, Rodrigo Sanches Miani, ClÃąudio Toshio Kawakani, and Sean Carlisto de Alvarenga. A survey of intrusion detection in internet of things. Journal of Network and Computer Applications, 84:25--37, 2017. ISSN 1084-8045.

Digital Library

[38]

E. Benkhelifa, T. Welsh, and W. Hamouda. A critical review of practices and challenges in intrusion detection systems for iot: Toward universal and resilient systems. IEEE Communications Surveys Tutorials, 20(4):3496--3509, 2018. ISSN 2373-745X.

Digital Library

[39]

A. Tabassum, A. Erbad, and M. Guizani. A survey on recent approaches in intrusion detection system in iots. In 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC), pages 1190--1197, 2019.

[40]

A. A. Gendreau and M. Moorman. Survey of intrusion detection systems towards an end to end secure internet of things. In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), pages 84--90, 2016.

[41]

Leonel Santos, Carlos RabadÃčo, and Ramiro GonÃğalves. Intrusion detection systems in internet of things: A literature review. In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pages 1--7, 2018.

[42]

E. Hodo, X. Bellekens, A. Hamilton, P. Dubouilh, E. Iorkyase, C. Tachtatzis, and R. Atkinson. Threat analysis of iot networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC), pages 1--6, 2016.

[43]

M. Nobakht, V. Sivaraman, and R. Boreli. A host-based intrusion detection and mitigation framework for smart home iot using openflow. In 2016 11th International Conference on Availability, Reliability and Security (ARES), pages 147--156, 2016.

[44]

J. Roux, E. Alata, G. Auriol, V. Nicomette, and M. Kaaniche. Toward an intrusion detection approach for iot based on radio communications profiling. In 2017 13th European Dependable Computing Conference (EDCC), pages 147--150, 2017.

[45]

H. Sedjelmaci, S. M. Senouci, and M. Al-Bahri. A lightweight anomaly detection technique for low-resource iot devices: A game-theoretic methodology. In 2016 IEEE International Conference on Communications (ICC), pages 1--6, 2016.

[46]

S. Malhotra, V. Bali, and K. K. Paliwal. Genetic programming and k-nearest neighbour classifier based intrusion detection model. In 2017 7th International Conference on Cloud Computing, Data Science Engineering - Confluence, pages 42--46,2017.

[47]

M. Surendar and A. Umamakeswari. Indres: An intrusion detection and response system for internet of things with 6lowpan. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pages 1903--1908, 2016.

[48]

P. Li and Y. Zhang. A novel intrusion detection method for internet of things. In 2019 Chinese Control And Decision Conference (CCDC), pages 4761--4765, 2019.

[49]

A. Yahyaoui, T. Abdellatif, and R. Attia. Hierarchical anomaly based intrusion detection and localization in iot. In 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC), pages 108--113, 2019.

[50]

E. Anthi, L. Williams, M. SÅĆowiÅĎska, G. Theodorakopoulos, and P. Burnap. A supervised intrusion detection system for smart home iot devices. IEEE Internet of Things Journal, 6(5):9042--9053, 2019. ISSN 2372-2541.

[51]

A. Mansour, M. Azab, M. R. M. Rizk, and M. Abdelazim. Biologically-inspired sdn-based intrusion detection and prevention mechanism for heterogeneous iot networks. In 2018 IEEE 9th Annual Information Technology,Electronics and Mobile Communication Conference (IEMCON), pages 1120--1125, 2018.

[52]

S. Hanif, T. Ilyas, and M. Zeeshan. Intrusion detection in iot using artificial neural networks on unsw-15 dataset. In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT IoT and AI (HONET-ICT), pages 152--156, 2019.

[53]

B. Roy and H. Cheung. A deep learning approach for intrusion detection in internet of things using bi-directional long short-term memory recurrent neural network. In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), pages 1--6, 2018.

[54]

E. Aydogan, S. Yilmaz, S. Sen, I. Butun, S. ForsstrÃűm, and M. Gidlund. A central intrusion detection system for rpl-based industrial internet of things. In 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS), pages 1--5, 2019.

[55]

Dharmini Shreenivas, Shahid Raza, and Thiemo Voigt. Intrusion detection in the rpl-connected 6lowpan networks. In Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security, page 31âĂŞ38. Association for Computing Machinery, 2017. ISBN 9781450349697.

Digital Library

[56]

R. Doshi, N. Apthorpe, and N. Feamster. Machine learning ddos detection for consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops (SPW), pages 29--35, 2018.

[57]

Liqun Liu, Bing Xu, Xiaoping Zhang, and Xianjun Wu. An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP Journal on Wireless Communications and Networking, 2018(1):113, 2018. ISSN 1687-1499.

[58]

M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani. A detailed analysis of the kdd cup 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pages 1--6, 2009.

[59]

C. Cervantes, D. Poplade, M. Nogueira, and A. Santos. Detection of sinkhole attacks for supporting secure routing on 6lowpan for internet of things. In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pages 606--611, 2015.

[60]

N. Moustafa and J. Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 Military Communications and Information Systems Conference (MilCIS), pages 1--6, 2015.

[61]

Canadian Institute of Cybersecurity. Nsl-kdd dataset, 2009. URL https://www.unb.ca/cic/datasets/nsl.html.

[62]

Shahid Raza, Linus Wallgren, and Thiemo Voigt. Svelte: Real-time intrusion detection in the internet of things. Ad Hoc Networks, 11(8):2661--2674, 2013. ISSN1570-8705.

Digital Library

[63]

A. Meddeb. Internet of things standards: who stands out from the crowd? IEEE Communications Magazine, 54(7):40--47, 2016. ISSN 1558-1896.

Digital Library

[64]

Huiping Guo, Yingjiu Li, and Sushil Jajodia. Chaining watermarks for detecting malicious modifications to streaming data. Information Sciences, 177(1):281--298, 2007. ISSN 0020-0255. URL http://www.sciencedirect.com/science/article/pii/S0020025506000855.

[65]

Wei Zhang, Yonghe Liu, Sajal K. Das, and Pradip De. Secure data aggregation in wireless sensor networks: A watermark based authentication supportive approach. Pervasive and Mobile Computing, 4(5):658--680, 2008. ISSN 1574-1192. URL http://www.sciencedirect.com/science/article/pii/S157411920800059X.

Digital Library

[66]

Sean Weerakkody, Omur Ozel, Yilin Mo, and Bruno Sinopoli. Resilient control in cyber-physical systems: Countering uncertainty, constraints, and adversarial behavior. Foundations and TrendsÂő in Systems and Control, 7(1-2):1--252, 2019. ISSN 2325-6818. URL http://dx.doi.org/10.1561/2600000018.

[67]

Khizar Hameed, Abid Khan, Mansoor Ahmed, Alavalapati Goutham Reddy, and M. Mazhar Rathore. Towards a formally verified zero watermarking scheme for data integrity in the internet of things based-wireless sensor networks. Future Generation Computer Systems, 82:274--289, 2018. ISSN 0167-739X.

[68]

Zhang Guoyin, Kou Liang, Zhang Liguo, Liu Chao, Da Qingan, and Sun Jianguo. A new digital watermarking method for data integrity protection in the perception layer of iot. Security and Communication Networks, 2017, 2017. ISSN 1939-0114.

[69]

Arwa Alromih, Mznah Al-Rodhaan, and Yuan Tian. A randomized watermarking technique for detecting malicious data injection attacks in heterogeneous wireless sensor networks for internet of things applications. Sensors (Basel, Switzerland), 18(12):4346, Dec 2018. ISSN 1424-8220. URL https:// .ncbi.nlm.nih.gov/30544877.

[70]

Wei Li Neal N. Xiong Baowei Wang, Weiwen Kong. A dual-chaining watermark scheme for data integrity protection in internet of things. Computers, Materials & Continua, 58(3):679--695, 2019. ISSN 1546-2226. URL http://www.techscience.com/cmc/v58n3/23040.

[71]

Xi Shi and Di Xiao. A reversible watermarking authentication scheme for wireless sensor networks. Inf. Sci., 240:173âĂŞ183, August 2013. ISSN 0020-0255. URL https://doi.org/10.1016/j.ins.2013.03.031.

Digital Library

[72]

Qun Ding, Baowei Wang, Xingming Sun, Jinwei Wang, and Jian Shen. A reversible watermarking scheme based on difference expansion for wireless sensor networks. International Journal of Grid Distribution Computing, 8(2):143--154, 2015. URLhttp://dx.doi.org/10.14257/ijgdc.2015.8.2.14.

[73]

Xingming Sun, Jianwei Su, Baowei Wang, and Qi Liu. Digital watermarking method for data integrity protection in wireless sensor networks. International Journal of Security and Its Applications, 7(4):407--416, 2013.

Cited By

Dwivedi ATiwari VWaoo A(2024)IMPACT OF MACHINE LEARNING-BASED ROUTING PROTOCOLS FOR EFFICIENT DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS (WSNS)ShodhKosh: Journal of Visual and Performing Arts10.29121/shodhkosh.v5.i1.2024.18745:1Online publication date: 31-Jan-2024
https://doi.org/10.29121/shodhkosh.v5.i1.2024.1874
Alfriehat NAnbar MKaruppayah SRihan SAlabsi BMomani A(2024)Detecting Version Number Attacks in Low Power and Lossy Networks for Internet of Things Routing: Review and TaxonomyIEEE Access10.1109/ACCESS.2024.336863312(31136-31158)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368633
Zohourian ADadkhah SMolyneaux HNeto EGhorbani A(2024)IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networksComputers & Security10.1016/j.cose.2024.104034146(104034)Online publication date: Nov-2024
https://doi.org/10.1016/j.cose.2024.104034
Show More Cited By

Recommendations

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats
Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. An ...
Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing
Abstract
The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research ...
RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures
Abstract
Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security

August 2020

1073 pages

ISBN:9781450388337

DOI:10.1145/3407023

Program Chairs:
Melanie Volkamer
Karlsruhe Institute of Technologie (KIT), Competence Center for Applied Security Technology (KASTEL)
,
Christian Wressnegger
Karlsruhe Institute of Technologie (KIT), Competence Center for Applied Security Technology (KASTEL)

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

European Commission
Agency for Management of University and Research Grants (AGAUR)
Ministry of Science and Innovation

Conference

ARES 2020

ARES 2020: The 15th International Conference on Availability, Reliability and Security

August 25 - 28, 2020

Virtual Event, Ireland

Acceptance Rates

Overall Acceptance Rate 228 of 451 submissions, 51%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
318
Total Downloads

Downloads (Last 12 months)32
Downloads (Last 6 weeks)2

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Dwivedi ATiwari VWaoo A(2024)IMPACT OF MACHINE LEARNING-BASED ROUTING PROTOCOLS FOR EFFICIENT DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS (WSNS)ShodhKosh: Journal of Visual and Performing Arts10.29121/shodhkosh.v5.i1.2024.18745:1Online publication date: 31-Jan-2024
https://doi.org/10.29121/shodhkosh.v5.i1.2024.1874
Alfriehat NAnbar MKaruppayah SRihan SAlabsi BMomani A(2024)Detecting Version Number Attacks in Low Power and Lossy Networks for Internet of Things Routing: Review and TaxonomyIEEE Access10.1109/ACCESS.2024.336863312(31136-31158)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3368633
Zohourian ADadkhah SMolyneaux HNeto EGhorbani A(2024)IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networksComputers & Security10.1016/j.cose.2024.104034146(104034)Online publication date: Nov-2024
https://doi.org/10.1016/j.cose.2024.104034
Alfriehat NAnbar MAladaileh MHasbullah IShurbaji TKaruppayah SAlmomani A(2024)RPL-based attack detection approaches in IoT networks: review and taxonomyArtificial Intelligence Review10.1007/s10462-024-10907-y57:9Online publication date: 12-Aug-2024
https://doi.org/10.1007/s10462-024-10907-y
Ismail SDawoud DReza H(2023)Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A ReviewFuture Internet10.3390/fi1506020015:6(200)Online publication date: 30-May-2023
https://doi.org/10.3390/fi15060200
Zaim HYolaçan EYavanoğlu U(2023)Intelligent Attack Detection in ROS-based Systems2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386583(5946-5950)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386583
Al-Amiedy TAnbar MBelaton BKabla AHasbullah IAlashhab Z(2022)A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of ThingsSensors10.3390/s2209340022:9(3400)Online publication date: 29-Apr-2022
https://doi.org/10.3390/s22093400
Lavaur LPahl MBusnel YAutrel F(2022)The Evolution of Federated Learning-Based Intrusion Detection and Mitigation: A SurveyIEEE Transactions on Network and Service Management10.1109/TNSM.2022.317751219:3(2309-2332)Online publication date: Sep-2022
https://doi.org/10.1109/TNSM.2022.3177512
Nandhini PKuppuswami SHarish MGomanishwaran SBharani S(2022)A Comparison on Feature Selection Methods using Machine Learning Algorithms for improving the Performance Parameters of RPL-BASED IoT Attacks Classification2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA54612.2022.9985557(981-986)Online publication date: 21-Sep-2022
https://doi.org/10.1109/ICIRCA54612.2022.9985557
Heine FKleiner CKlostermeyer PAhlers VLaue TWellermann N(2022)Detecting Attacks in Network Traffic Using Normality Models: The Cellwise EstimatorFoundations and Practice of Security10.1007/978-3-031-08147-7_18(265-282)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-031-08147-7_18
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents