Banca de QUALIFICAÇÃO: FRANCISCO LOPES DE CALDAS FILHO

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : FRANCISCO LOPES DE CALDAS FILHO
DATE: 21/12/2023
TIME: 17:00
LOCAL: Remota
TITLE:

IPS system with distributed processing using Federated Learning

KEY WORDS:

Internet of Things (IoT), cybersecurity, local area networks, Network Intrusion Detection Systems (NIDS)

PAGES: 91
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Telecomunicações
SUMMARY:

The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection Systems (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a Fog Computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with Federated Learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a Federated training and detection procedure through Deep Learning to minimize the impact of a Single point of failure (SPOF) and reduce the workload of each device, thus achieving an accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with near real-time detection and mitigation system.

COMMITTEE MEMBERS:
Externo ao Programa - 2556078 - GEORGES DANIEL AMVAME NZE - nullExterno à Instituição - GERALDO PEREIRA ROCHA FILHO - UESB
Interno - 2201912 - RAFAEL TIMOTEO DE SOUSA JUNIOR
Externo à Instituição - ROBSON DE OLIVEIRA ALBUQUERQUE - CEPESC
Interno - 1415757 - VINICIUS PEREIRA GONCALVES