Smart Environments IoT Device Classification Using Network Traffic Characteristics
Keywords:
Traffic modeling, traffic volume, Machine learning, IoT, characteristics of network, device visibility, classificationAbstract
The growing number of Internet of Things (IoT) gadgets in smart settings has made it harder to manage and protect these systems that are all linked together. Some important parts of managing IoT devices are classification and recognition. Devices can be put into groups based on how they work and behave. Using network traffic characteristics, this study suggests a way to group Internet of Things (IoT) objects in smart environments. Our method looks at the trends and characteristics of network data that IoT devices send in order to correctly identify and group these devices. This will make management easier and security better. We show testing results that show our proposed method can effectively sort different IoT devices into groups based on their network traffic signatures. We go over the trade-offs that must be made between performance, speed, and cost while implementing the categorization system in real time. Without requiring any specialised equipment or protocols, our study provides the door for operators of smart environments to monitor the existence, operation, and cyber-security of their IoT assets.
Downloads
References
3GPP, ”Service Requirements for Machine-Type Communications,” TS 22.368 V10.1.0, June 2010.
Y. Morioka, ”LTE for Mobile Consumer Devices”, ETSI Workshop on Machine to Machine Standardization, 2011.
3GPP TR 37.868 v0.8.1 (2011-08),”Study on RAN Improvements for Machine-type communications (Release 10)”.
IEEE802.16p, Machine to Machine (M2M) System Requirements Document (SRD), Aug 2012.
T. Taleb and A. Kunz,”Machine Type Communications in 3GPP Networks: Potential, Challenges, and Solutions,” IEEE Communication Magazine, March 2012.
S. Krco, J. Vuckovic, and S. Jokic, ”ecoBus Mobile Environment Monitoring”, Book Chapter in Towards a Service-Based Internet, 2010.
Ravindra Changala “A Survey1 on Clustering Techniques to Improve Energy Efficient Routing in Wireless Sensor Networks” in International Journal of Applied Engineering Research ,10(58), pp.-1-5,2015.
Ravindra Changala, “Secured Activity Based Authentication System” in " in Journal of innovations in computer science and engineering (JICSE), Volume 6, Issue 1,Pages 1-4, September 2016.ISSN: 2455-3506.
Ravindra Changala, “Object Tracking in Wireless Sensor networks using Data mining Techniques”, in IOSR Journal of Electrical and Electronics Engineering, 2015.
K. Moskvitch, “Securing IoT: In your Smart Home and your Connected Enterprise,” Engineering Technology, vol. 12, April 2017.
N. Dhanjani, Abusing the Internet of Things: Blackouts, Freakouts, and Stakeouts. O’Reilly Media, 2015.
E. Fernandes et al., “Security Analysis of Emerging Smart Home Applications,” in 2016 IEEE Symposium on Security and Privacy (SP). IEEE, may 2016.
T. guardian. (2016) Why the internet of things is the new magic ingredient for cyber criminals.
T. Yu et al., “Handling a Trillion (Unfixable) Flaws on a Billion Devices: Rethinking Network Security for the Internet-of-Things,” in Proc. ACM HotNets, Nov 2015.
Sivanathan et al., “Low-Cost Flow-Based Security Solutions for Smart-Home IoT Devices,” in Proc. IEEE ANTS, Nov 2016.
Ravindra Changala, Framework for Virtualized Network Functions (VNFs) in Cloud of Things Based on Network Traffic Services, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169 Volume 11, Issue 11s, August 2023.
Ravindra Changala, Block Chain and Machine Learning Models to Evaluate Faults in the Smart Manufacturing System, International Journal of Scientific Research in Science and Technology, Volume 10, Issue 5, ISSN: 2395-6011, Page Number 247-255, September-October-2023.
Ravindra Changala, MapReduce Framework to Improve the Efficiency of Large Scale Item Sets in IoT Using Parallel Mining of Representative Patterns in Big Data, International Journal of Scientific Research in Science and Technology, ISSN: 2395-6011, Volume 9, Issue 6, Page Number: 151-161, November 2022.
R. Ferdous et al., “On the Use of SVMs to Detect Anomalies in a Stream of SIP Messages,” in Proc. IEEE ICMLA, Boca Raton, Florida, USA, Dec 2012.
M. Z. Shafiq et al., “A First Look at Cellular Machine-to-Machine Traffic: Large Scale Measurement and Characterization,” in Proc. ACM Sigmetrics, England, Jun 2012.
N. Nikaein et al., “Simple Traffic Modeling Framework for Machine Type Communication,” in Proc. ISWCS, Germany, Aug 2013.
M. Jadoul. The IoT: The Network Can Make It or Break It.
Ravindra Changala, A Dominant Feature Selection Method for Deep Learning Based Traffic Classification Using a Genetic Algorithm, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, ISSN : 2456-3307, Volume 8, Issue 6, November-December-2022, Page Number : 173-181.
D. Bonfiglio et al., “Revealing Skype Traffic: When Randomness Plays with You,” SIGCOMM Comput. Commun. Rev., vol. 37, no. 4, pp. 37–48, Aug. 2007.
Andrea et al., “Internet of Things: Security vulnerabilities and challenges,” in 2015 IEEE Symposium on Computers and Communication (ISCC), July 2015.
Ravindra Changala, A Novel Approach for Network Traffic and Attacks Analysis Using Big Data in Cloud Environment, International Journal of Innovative Research in Computer and Communication Engineering: 2320-9798, Volume 10, Issue 11, November 2022.
M. Iliofotou et al., “Exploiting Dynamicity in Graph-based Traffic Analysis: Techniques and Applications,” in Proc. ACM CoNEXT, Rome, Italy, Dec 2009.
P. Svoboda, M. Laner, J. Fabini, M. Rupp, F. Ricciato, ”Packet Delay Measurements in Reactive IP Networks”, IEEE Instrumentation & Measurement Magazine, 2012.
[29] Ravindra Changala, “Diminution of Deployment Issues in Secure Multicast System with Group Key Management” published in International Journal of Computer Application (IJCA), Impact Factor 2.52, ISSN No: 2250-1797, Volume 2, Issue 3, June 2012.
M. Z. r Shafiq, L. Ji, A. X. Liu, J. Pang, J. Wang, ”A First Look at Cellular Machine-to-Machine Traffic Large Scale Measurement and Characterization”, SIGMETRICS 2012.
Moore and D. Zuev, “Internet Traffic Classification Using Bayesian Analysis Techniques,” SIGMETRICS Perform. Eval. Rev., vol. 33, no. 1, pp. 50–60, Jun. 2005.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
