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Adaptive intrusion detection techniques for internet of things-based smart cities

Grant number: 21/10234-5
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): February 01, 2022
Effective date (End): December 18, 2022
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Acordo de Cooperação: CNPq - INCTs
Principal Investigator:Fabio Kon
Grantee:Mosab Hamdan Adam Mohamed Alhassan
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:14/50937-1 - INCT 2014: on the Internet of the Future, AP.TEM

Abstract

Given the large-scale expansion of the Internet of Things (IoT) for sustainable resource management in smart cities, a proper Intrusion Detection System (IDS) architecture is vital to protect future network infrastructure from invaders. With therise of connected devices, the most extensively used centralized (cloud-based) IDS suffers from excessive latency and network overhead, resulting in sluggish detection of unauthorized users and unresponsiveness to attacks. Moreover, by considering the complexity and dynamics of IoT's computing concepts, the major issues that will possibly arise in the use of IoT technology are the energy consumption and memory consumption of the resource-constraint IoT-based smart city is a challenge to the IoT devices. This is because the current security architecture is obsolete to handle complexity and dynamicity involving security threats. The solution that will be proposed will be conscious of the implication of IoT-based smart city applications that are committed to providing solutions that will meet the needs of end-user in terms ofen suring better security at the IoT nodes. This project intends to address the following objectives: to design and develop an efficient, lightweight anomaly detection for the IoT-based smart city objects forms minimize energy and memory consumption in processing and transmitting data while achieving high accuracy; to enhance the detection effectiveness of an IDS for the IoT-based smart cities to detect routing attacks; to improve the detection effectiveness and efficiency when applying the IDS in a distributed IoT network-based smart environment. The combined final output of the proposed schemes will be a potential solution for addressing routing attacks in IoT-based smart environment devices. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DA SILVA OLIVEIRA, GIOVANNI APARECIDO; SILVA LIMA, PRISCILA SERRA; KON, FABIO; TERADA, ROUTO; BATISTA, DANIEL MACEDO; HIRATA, ROBERTO; HAMDAN, MOSAB; MORAES, IM; CAMPISTA, MEM; GHAMRI-DOUDANE, Y; et al. A Stacked Ensemble Classifier for an Intrusion Detection System in the Edge of IoT and IIoT Networks. 2022 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM), v. N/A, p. 6-pg., . (14/50937-1, 21/10234-5, 15/24485-9)
HASSAN, MOHAMED KHALAFALLA; ARIFFIN, SHARIFAH HAFIZAH SYED; GHAZALI, N. EFFIYANA; HAMAD, MUTAZ; HAMDAN, MOSAB; HAMDI, MONIA; HAMAM, HABIB; KHAN, SULEMAN. Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts. SENSORS, v. 22, n. 9, p. 27-pg., . (21/10234-5)

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