| Title | A Machine Learning Approach for Intrusion Detection in Smart Cities |
|---|---|
| ID_Doc | 41657 |
| Authors | Elsaeidy, A; Munasinghe, KS; Sharma, D; Jamalipour, A |
| Title | A Machine Learning Approach for Intrusion Detection in Smart Cities |
| Year | 2019 |
| Published | |
| Abstract | Over the recent years smart cities have been emerged as promising paradigm for a transition toward providing effective and real time smart services. Despite the great potential it brings to citizens' life, security and privacy issues still need to be addressed. Due to technology advances, large amount of data is produced, where machine learning methods are applied to learn meaningful patterns. In this paper a machine learning-based framework is proposed for detecting distributed Denial of Service (DDoS) attacks in smart cities. The proposed framework applies restricted Boltzmann machines to learn high-level features from raw data and on top of these learned features, a feed forward neural network model is trained for attack detection. The performance of the proposed framework is verified using a smart city dataset collected from a smart water plant. The results show the effectiveness of the proposed framework in detecting DDoS attacks. |
Similar Articles
| ID | Score | Article |
|---|---|---|
43160
|
Elsaeidy, A; Munasinghe, KS; Sharma, D; Jamalipour, A Intrusion detection in smart cities using Restricted Boltzmann Machines(2019) | |
| 42628
|
Ali, H; Elzeki, OM; Elmougy, S Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats(2022)Applied Sciences-Basel, 12, 13 | |
| 36837
|
Elsaeidy, AA; Jamalipour, A; Munasinghe, KS A Hybrid Deep Learning Approach for Replay and DDoS Attack Detection in a Smart City(2021) | |
| 45810
|
Rashid, MM; Kamruzzaman, J; Hassan, MM; Imam, T; Gordon, S Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques(2020)International Journal Of Environmental Research And Public Health, 17, 24 | |
| 37333
|
Almasri, MM; Alajlan, AM A novel-cascaded ANFIS-based deep reinforcement learning for the detection of attack in cloud IoT-based smart city applications(2023)Concurrency And Computation-Practice & Experience, 35.0, 22 | |
| 39259
|
Amaizu, GC; Lee, JM; Kim, DS Machine Learning Based Security for Smart Cities(2022) | |
| 45350
|
Elsaeidy, A; Elgendi, I; Munasinghe, KS; Sharma, D; Jamalipour, A A Smart City Cyber Security Platform for Narrowband Networks(2017) | |
| 38272
|
Saba, T; Khan, AR; Sadad, T; Hong, SP Securing the IoT System of Smart City against Cyber Threats Using Deep Learning(2022) | |
| 41832
|
Gupta, SK; Tripathi, M; Grover, J Hybrid optimization and deep learning based intrusion detection system(2022) |