Main Reference PaperIntrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection, 2018 [Contiki-Cooja Simulator].
  • Granjal, Jorge, João Silva, and Nuno Lourenço.

Author Name(s):
  • Granjal, Jorge, João Silva, and Nuno Lourenço.

  • 10.3390/s18082445

DOI:
  • 10.3390/s18082445

  • Internet of Things (IoT)

Research Area:
  • Internet of Things (IoT)

  • we propose an IDS framework for the detection and prevention of attacks in the context of Internet-integrated CoAP communication environments and, in the context of this framework, we implement and experimentally evaluate the effectiveness of anomaly-based intrusion detection, with the goal of detecting Denial of Service (DoS) attacks and attacks against the 6LoWPAN and CoAP communication protocols.

  • From the results obtained in our experimental evaluation we observe that the proposed approach may viably protect devices against the considered attacks.

  • We are able to achieve an accuracy of 93% considering the multi-class problem, thus when the pattern of specific intrusions is known

  • Considering the binary class problem, which allows us to recognize compromised devices, and though a lower accuracy of 92% is observed, a recall and an F_Measure of 98% were achieved.

  • As far as our knowledge goes, ours is the first proposal targeting the usage of anomaly detection and prevention approaches to deal with application-layer and DoS attacks in 6LoWPAN and CoAP communication environments

Abstract:
  • we propose an IDS framework for the detection and prevention of attacks in the context of Internet-integrated CoAP communication environments and, in the context of this framework, we implement and experimentally evaluate the effectiveness of anomaly-based intrusion detection, with the goal of detecting Denial of Service (DoS) attacks and attacks against the 6LoWPAN and CoAP communication protocols.

  • From the results obtained in our experimental evaluation we observe that the proposed approach may viably protect devices against the considered attacks.

  • We are able to achieve an accuracy of 93% considering the multi-class problem, thus when the pattern of specific intrusions is known

  • Considering the binary class problem, which allows us to recognize compromised devices, and though a lower accuracy of 92% is observed, a recall and an F_Measure of 98% were achieved.

  • As far as our knowledge goes, ours is the first proposal targeting the usage of anomaly detection and prevention approaches to deal with application-layer and DoS attacks in 6LoWPAN and CoAP communication environments

  • Operating System : Ubuntu 12.04 LTS 64bit

  • Simulator: Cooja, Instant Contiki-3.0 and Vmware Player 12.5.6

  • Language: C

Software Tools & Technologies
  • Operating System : Ubuntu 12.04 LTS 64bit

  • Simulator: Cooja, Instant Contiki-3.0 and Vmware Player 12.5.6

  • Language: C

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

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  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

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  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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