Smart City Gnosys

Smart city article details

Title	Guarding The Future: Anomaly Detection In Iot-Enabled Smart Cities
ID_Doc	28563
Authors	Ansari L.
Year	2024
Published	Lecture Notes in Networks and Systems, 1110 LNNS
DOI	http://dx.doi.org/10.1007/978-981-97-6678-9_1
Abstract	The Internet of Things (IoT) and its applications have developed into smarter, more linked systems that are used in every element of smart cities. The machine learning (ML) method is helpful to the further improvement in the varied intelligence and potential of the application as the amount of composed data increases. Researchers have been interested in smart transportation applications, which have been approached using both ML and IoT methodologies. This research suggested an innovative, effective detection system that depends on machine learning techniques to identify IoT attacks and stop harmful activity. Additionally, the UNSW-NB15 and CICIDS2017 datasets are utilized in this work. Initially, the data is pre-processed using combinations of several preprocessing and normalization processes. Then the features are extracted from the pre-processed data using an improved principal component analysis algorithm and genetic algorithm, and the attacks are classified using a random forest classifier. This study evaluates a variety of ML techniques for binary classification issues. The results show that for both datasets in the suggested model, the proposed random field technique is superior to conventional ML algorithms for attack detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Author Keywords	Anomaly detection; Cloud computing; Genetic algorithm (GA); Internet of Things (IoT); Machine learning; PCA; Random forest; Security; Smart city

Similar Articles

Id	Similarity	Authors	Title	Published
36913	0.936	Girubagari N.; Ravi T.N.	Methods Of Anomaly Detection For The Prevention And Detection Of Cyber Attacks	International Journal of Intelligent Engineering Informatics, 11, 4 (2024)
9646	0.928	Maniriho P.; Niyigaba E.; Bizimana Z.; Twiringiyimana V.; Mahoro L.J.; Ahmad T.	Anomaly-Based Intrusion Detection Approach For Iot Networks Using Machine Learning	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (2020)
37192	0.924	Mahor V.; Bijrothiya S.; Mishra R.; Rawat R.	Ml Techniques For Attack And Anomaly Detection In Internet Of Things Networks	Autonomous Vehicles, 1 (2022)
9197	0.919	Janani Pandeeswari G.; Jeyanthi S.	Analysis Of Intrusion Detection Using Machine Learning Techniques	2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022 (2022)
19240	0.915	Rangani H.; Chandrashekar K.	Detection And Prevention Of Cyber Threats In Smart Cities Using Machine Learning And Intrusion Detection Systems	2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024 - Proceedings (2024)
33346	0.914	Berhili M.; Chaieb O.; Benabdellah M.	Intrusion Detection Systems In Iot Based On Machine Learning: A State Of The Art	Procedia Computer Science, 251 (2024)
1448	0.908	Muniswamy A.; Rathi R.	A Detailed Review On Enhancing The Security In Internet Of Things-Based Smart City Environment Using Machine Learning Algorithms	IEEE Access, 12 (2024)
814	0.907	Basheer L.; Ranjana P.	A Comparative Study Of Various Intrusion Detections In Smart Cities Using Machine Learning	2022 International Conference on IoT and Blockchain Technology, ICIBT 2022 (2022)
37199	0.906	Al-Ambusaidi M.; Yinjun Z.; Muhammad Y.; Yahya A.	Ml-Ids: An Efficient Ml-Enabled Intrusion Detection System For Securing Iot Networks And Applications	Soft Computing, 28, 2 (2024)
6153	0.906	Alrashdi I.; Alqazzaz A.; Aloufi E.; Alharthi R.; Zohdy M.; Ming H.	Ad-Iot: Anomaly Detection Of Iot Cyberattacks In Smart City Using Machine Learning	2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019 (2019)