Smart City Gnosys

Smart city article details

Title	Smart Attacks Learning Machine Advisor System For Protecting Smart Cities From Smart Threats
ID_Doc	49172
Authors	Ali H.; Elzeki O.M.; Elmougy S.
Year	2022
Published	Applied Sciences (Switzerland), 12, 13
DOI	http://dx.doi.org/10.3390/app12136473
Abstract	The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	Extreme Learning Machine (ELM); Internet of Things (IoT); Intrusion Detection System (IDS); NSL-KDD; smart city; Software-Defined Networking (SDN)

Similar Articles

Id	Similarity	Authors	Title	Published
4457	0.906	Alshahrani M.M.	A Secure And Intelligent Software-Defined Networking Framework For Future Smart Cities To Prevent Ddos Attack	Applied Sciences (Switzerland), 13, 17 (2023)
35103	0.896	Sharma H.; Gupta S.	Leveraging Machine Learning And Sdn-Fog Infrastructure To Mitigate Flood Attacks	2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings (2021)
52188	0.895	Husnain A.; Nguyen C.; Le N.T.	Software-Defined Iot With Machine Learning-Based Enhanced Security	Proceedings - 2023 28th Asia Pacific Conference on Communications, APCC 2023 (2023)
55927	0.889	Gupta A.D.; Kishore K.; Prasad A.B.; Kannadasan B.; Narayana M.S.; Singh P.	The Intelligent Smart City Deployment Via Artificial Intelligence Software Networking	2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022 (2022)
52158	0.888	Wang S.; Gomez K.M.; Sithamparanathan K.; Zanna P.	Software Defined Network Security Framework For Iot Based Smart Home And City Applications	2019, 13th International Conference on Signal Processing and Communication Systems, ICSPCS 2019 - Proceedings (2019)
3259	0.888	Sri vidhya G.; Nagarajan R.	A Novel Bidirectional Lstm Model For Network Intrusion Detection In Sdn-Iot Network	Computing, 106, 8 (2024)
47749	0.883	Elsayed R.A.; Hamada R.A.; Abdalla M.I.; Elsaid S.A.	Securing Iot And Sdn Systems Using Deep-Learning Based Automatic Intrusion Detection	Ain Shams Engineering Journal, 14, 10 (2023)
29254	0.878	Alani M.M.	Honeytwin: Securing Smart Cities With Machine Learning-Enabled Sdn Edge And Cloud-Based Honeypots	Journal of Parallel and Distributed Computing, 188 (2024)
47485	0.873	Tawfik A.T.; Abdullah S.H.; Nori A.S.; Rakha M.A.	Sdn-Enabled Intrusion Detection System Using Machine Learning And Neural Network Schemes	Smart Applications of Artificial Intelligence and Big Data (2025)
47782	0.872	Ahmed Y.; Beyioku K.; Yousefi M.	Securing Smart Cities Through Machine Learning: A Honeypot-Driven Approach To Attack Detection In Internet Of Things Ecosystems	IET Smart Cities, 6, 3 (2024)