PMID- 37514600
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230731
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 14
DP  - 2023 Jul 11
TI  - Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning.
LID - 10.3390/s23146305 [doi]
LID - 6305
AB  - 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 System (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 
      accuracy of 89.753% during detection and increasing privacy issues in a 
      decentralized IoT infrastructure with a near-real-time detection and mitigation 
      system.
FAU - de Caldas Filho, Francisco Lopes
AU  - de Caldas Filho FL
AUID- ORCID: 0000-0001-5419-2712
AD  - Electrical Engineering Department (ENE), Technology College, University of 
      Brasilia (UnB), Brasilia 70910-900, Brazil.
FAU - Soares, Samuel Carlos Meneses
AU  - Soares SCM
AUID- ORCID: 0009-0006-5055-2075
AD  - Electrical Engineering Department (ENE), Technology College, University of 
      Brasilia (UnB), Brasilia 70910-900, Brazil.
FAU - Oroski, Elder
AU  - Oroski E
AUID- ORCID: 0000-0003-3169-7245
AD  - Electrical Engineering Department (DAELT), Federal University of 
      Technology-Parana (UTFPR), Curitiba 80230-901, Brazil.
FAU - de Oliveira Albuquerque, Robson
AU  - de Oliveira Albuquerque R
AUID- ORCID: 0000-0002-6717-3374
AD  - Electrical Engineering Department (ENE), Technology College, University of 
      Brasilia (UnB), Brasilia 70910-900, Brazil.
FAU - da Mata, Rafael Zerbini Alves
AU  - da Mata RZA
AUID- ORCID: 0000-0002-5246-8858
AD  - Electrical Engineering Department (ENE), Technology College, University of 
      Brasilia (UnB), Brasilia 70910-900, Brazil.
FAU - de Mendonca, Fabio Lucio Lopes
AU  - de Mendonca FLL
AUID- ORCID: 0000-0001-7100-7304
AD  - Electrical Engineering Department (ENE), Technology College, University of 
      Brasilia (UnB), Brasilia 70910-900, Brazil.
FAU - de Sousa Junior, Rafael Timoteo
AU  - de Sousa Junior RT
AUID- ORCID: 0000-0003-1101-3029
AD  - Electrical Engineering Department (ENE), Technology College, University of 
      Brasilia (UnB), Brasilia 70910-900, Brazil.
LA  - eng
PT  - Journal Article
DEP - 20230711
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10384678
OTO - NOTNLM
OT  - DDoS
OT  - HIDPS
OT  - NIDS
OT  - deep learning
OT  - federated learning
OT  - fog computing
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/29 11:53
MHDA- 2023/07/29 11:54
PMCR- 2023/07/11
CRDT- 2023/07/29 01:42
PHST- 2023/05/29 00:00 [received]
PHST- 2023/06/23 00:00 [revised]
PHST- 2023/07/05 00:00 [accepted]
PHST- 2023/07/29 11:54 [medline]
PHST- 2023/07/29 11:53 [pubmed]
PHST- 2023/07/29 01:42 [entrez]
PHST- 2023/07/11 00:00 [pmc-release]
AID - s23146305 [pii]
AID - sensors-23-06305 [pii]
AID - 10.3390/s23146305 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Jul 11;23(14):6305. doi: 10.3390/s23146305.