Literature DB >> 35062536

Preventing MQTT Vulnerabilities Using IoT-Enabled Intrusion Detection System.

Muhammad Husnain1, Khizar Hayat1, Enrico Cambiaso2, Ubaid U Fayyaz1, Maurizio Mongelli2, Habiba Akram1, Syed Ghazanfar Abbas1, Ghalib A Shah1.   

Abstract

The advancement in the domain of IoT accelerated the development of new communication technologies such as the Message Queuing Telemetry Transport (MQTT) protocol. Although MQTT servers/brokers are considered the main component of all MQTT-based IoT applications, their openness makes them vulnerable to potential cyber-attacks such as DoS, DDoS, or buffer overflow. As a result of this, an efficient intrusion detection system for MQTT-based applications is still a missing piece of the IoT security context. Unfortunately, existing IDSs do not provide IoT communication protocol support such as MQTT or CoAP to validate crafted or malformed packets for protecting the protocol implementation vulnerabilities of IoT devices. In this paper, we have designed and developed an MQTT parsing engine that can be integrated with network-based IDS as an initial layer for extensive checking against IoT protocol vulnerabilities and improper usage through a rigorous validation of packet fields during the packet-parsing stage. In addition, we evaluate the performance of the proposed solution across different reported vulnerabilities. The experimental results demonstrate the effectiveness of the proposed solution for detecting and preventing the exploitation of vulnerabilities on IoT protocols.

Entities:  

Keywords:  Internet of Things; IoT vulnerabilities; MQTT protocol; intrusion detection system; network attacks; network firewall

Year:  2022        PMID: 35062536      PMCID: PMC8779830          DOI: 10.3390/s22020567

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  A Framework for Malicious Traffic Detection in IoT Healthcare Environment.

Authors:  Faisal Hussain; Syed Ghazanfar Abbas; Ghalib A Shah; Ivan Miguel Pires; Ubaid U Fayyaz; Farrukh Shahzad; Nuno M Garcia; Eftim Zdravevski
Journal:  Sensors (Basel)       Date:  2021-04-26       Impact factor: 3.576

  1 in total
  1 in total

Review 1.  Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets.

Authors:  Eric Gyamfi; Anca Jurcut
Journal:  Sensors (Basel)       Date:  2022-05-14       Impact factor: 3.847

  1 in total

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