Smart City Gnosys

Smart city article details

Title	Intrusion Detection In Iot Using Artificial Neural Networks On Unsw-15 Dataset
ID_Doc	33333
Authors	Hanif S.; Ilyas T.; Zeeshan M.
Year	2019
Published	HONET-ICT 2019 - IEEE 16th International Conference on Smart Cities: Improving Quality of Life using ICT, IoT and AI
DOI	http://dx.doi.org/10.1109/HONET.2019.8908122
Abstract	IoT devices are susceptible to numerous cyber-Attacks due to its low power, low computational requirements and controlled environment that make it hard to implement authentication and cryptography in IoT devices. In this work we propose artificial neural network based threat detection for IoT to solve the authentication issues. We use supervised learning algorithm to detect the attacks and furthermore controller discards the commands after classifying it as threat. Proposed ANN consist of input, hidden and output layers. Input layer passes the data as signal to hidden layer where these signals are computed with the assigned weights and activation functions are used to transform an input to an output signal. Proposed technique is able to detect attacks effectively and timely decisions are taken to tackle the attacks. Proposed ANN approach achieves an average precision of 84% and less than %8 of average false positive rate in repeated 10-fold cross-validation. This reveals the robustness, precision and accuracy of proposed approach in large and heterogeneous dataset. Approach proposed in this work has the potential to considerably improve the utilization of intrusion detection systems. © 2019 IEEE.
Author Keywords	ANN; Intrusion Detection; IOT; Machine Learning; Network Security

Similar Articles

Id	Similarity	Authors	Title	Published
16900	0.894	Rashid M.M.; Kamruzzaman J.; Imam T.; Kaisar S.; Alam M.J.	Cyber Attacks Detection From Smart City Applications Using Artificial Neural Network	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 (2020)
32413	0.888	Chauhan S.; Dalwadi H.; Mahida N.; Gupta R.; Tanwar S.	Intelligent Intrusion Detection System Framework For Smart Infrastructure Using Deep Learning	IET Conference Proceedings, 2024, 37 (2024)
33346	0.888	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)
33508	0.879	Saini K.S.; Chaudhary S.	Investigation On Attack Detection In Iot Networks: A Study And Analysis Of The Existing Machine Learning And Deep Learning Techniques	3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025)
2052	0.875	Elsayed R.; Hamada R.; Hammoudeh M.; Abdalla M.; Elsaid S.A.	A Hierarchical Deep Learning-Based Intrusion Detection Architecture For Clustered Internet Of Things	Journal of Sensor and Actuator Networks, 12, 1 (2023)
957	0.875	Houichi M.; Jaidi F.; Bouhoula A.	A Comprehensive Study Of Intrusion Detection Within Internet Of Things-Based Smart Cities: Synthesis, Analysis And A Novel Approach	2023 International Wireless Communications and Mobile Computing, IWCMC 2023 (2023)
6991	0.874	Aljohani R.; Bushnag A.; Alessa A.	Ai-Based Intrusion Detection For A Secure Internet Of Things (Iot)	Journal of Network and Systems Management, 32, 3 (2024)
33032	0.873	Dawoud A.; Sianaki O.A.; Shahristani S.; Raun C.	Internet Of Things Intrusion Detection: A Deep Learning Approach	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020)
9648	0.87	Alsoufi M.A.; Razak S.; Siraj M.M.; Nafea I.; Ghaleb F.A.; Saeed F.; Nasser M.	Anomaly-Based Intrusion Detection Systems In Iot Using Deep Learning: A Systematic Literature Review	Applied Sciences (Switzerland), 11, 18 (2021)
7830	0.869	Kumar S.M.; Velluri R.; Dayananda P.; Nagaraj S.; Srikantaswamy M.; Chandrappa K.Y.	An Efficient Detection And Prediction Of Intrusion In Smart Grids Using Artificial Neural Networks	Lecture Notes in Networks and Systems, 922 LNNS (2024)