| Title | Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks |
|---|---|
| ID_Doc | 41680 |
| Authors | Salim, MM; Singh, SK; Park, JH |
| Title | Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks |
| Year | 2021 |
| Published | |
| Abstract | Smart Cities contains millions of IoT sensors supporting critical applications such as Smart Transport, Buildings, Intelligent Vehicles, and Logistics. A central administrator appointed by the government manages and maintains the security of each node. Smart City relies upon millions of sensors that are heterogeneous and do not support standard security architecture. Different manufacturers have weak protection protocols for their products and do not update their firmware upon newly identified operating systems' vulnerabilities. Adversaries using brute force methods exploit the lack of inbuilt security systems on IoT devices to grow their bot network. Smart cities require a standard framework combining soft computing and Deep Learning (DL) for device fleet management and complete control of sensor operating systems for absolute security. This paper presents a real-world application for IoT fleet management security using a lightweight container-based botnet detection (C-BotDet) framework. Using a three-phase approach, the framework using Artificial Intelligence detects compromised IoT devices sending malicious traffic on the network. Balena Cloud revokes API keys and prevents a compromised device from infecting other devices to form a more giant botnet. VPN (Virtual Private Network) prevents inter-device communication and routes all malicious traffic through an external server. The framework quickly updates the standard Linux-based operating system IoT device fleet without relying on different manufacturers to update their system security individually. The simulation and analysis of the C-BotDet framework are presented in a practical working environment to demonstrate its implementation feasibility. (C) 2021 Published by Elsevier B.V. |
Similar Articles
| ID | Score | Article |
|---|---|---|
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 | |
| 44260
|
Sriram, S; Vinayakumar, R; Alazab, M; Soman, KP Network Flow based IoT Botnet Attack Detection using Deep Learning(2020) | |
| 41266
|
Salim, MM; Park, JH Deep Learning Based IoT Re-authentication for Botnet Detection and Prevention(2020) | |
| 42094
|
Rangelov, D; Lämmel, P; Brunzel, L; Borgert, S; Darius, P; Tcholtchev, N; Boerger, M Towards an Integrated Methodology and Toolchain for Machine Learning-Based Intrusion Detection in Urban IoT Networks and Platforms(2023)Future Internet, 15, 3 | |
| 40378
|
Alshahrani, MM; Prati, A A Secure and Intelligent Software-Defined Networking Framework for Future Smart Cities to Prevent DDoS Attack(2023)Applied Sciences-Basel, 13, 17 | |
| 38272
|
Saba, T; Khan, AR; Sadad, T; Hong, SP Securing the IoT System of Smart City against Cyber Threats Using Deep Learning(2022) | |
| 38565
|
Muniswamy, A; Rathi, R A Detailed Review on Enhancing the Security in Internet of Things-Based Smart City Environment Using Machine Learning Algorithms(2024) | |
| 36314
|
Rashid, MM; Kamruzzaman, J; Hassan, MM; Imam, T; Wibowo, S; Gordon, S; Fortino, G Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications(2022) | |
| 43375
|
Chen, DL; Wawrzynski, P; Lv, ZH Cyber security in smart cities: A review of deep learning-based applications and case studies(2021) | |
| 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 |