Adaptive Intrusion Detection Techniques for Internet of Things-Based Smart Cities

• Grant number: 21/10234-5
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: February 01, 2022
• End date: December 18, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: 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

Abstract

Given the large-scale expansion of the Internet of Things (IoT) for sustainableresource 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) IDSsuffers from excessive latency and network overhead, resulting in sluggish detectionof unauthorized users and unresponsiveness to attacks. Moreover, by consideringthe complexity and dynamics of IoT's computing concepts, the major issues that willpossibly arise in the use of IoT technology are the energy consumption and memoryconsumption of the resource-constraint IoT-based smart city is a challenge to the IoTdevices. This is because the current security architecture is obsolete to handlecomplexity and dynamicity involving security threats. The solution that will beproposed will be conscious of the implication of IoT-based smart city applications thatare committed to providing solutions that will meet the needs of end-user in terms ofensuring better security at the IoT nodes. This project intends to address thefollowing objectives: To design and develop an efficient, lightweight anomalydetection for the IoT-based smart city objects forms minimize energy and memoryconsumption in processing and transmitting data while achieving high accuracy. Toenhance the detection effectiveness of an IDS for the IoT-based smart cities to detectrouting attacks. To improve the detection effectiveness and efficiency when applyingthe IDS in a distributed IoT network-based smart environment. The combined finaloutput of the proposed schemes will be a potential solution for addressing routingattacks in IoT-based smart environment devices.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

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., 2022-01-01. (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., 2022-05-01. (21/10234-5)