IDLP: an efficient intrusion detection and location-aware prevention mechanism for network coding-enabled mobile small cells

Reza Parsamehr, Georgios Mantas, Jonathan Rodriguez, Jose-Fernan Martinez-Ortega

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Mobile small cell technology is considered as a 5G enabling technology for delivering ubiquitous 5G services in a cost-effective and energy efficient manner. Moreover, Network Coding (NC) technology can be foreseen as a promising solution for the wireless network of mobile small cells to increase its throughput and improve its performance. However, NC-enabled mobile small cells are vulnerable to pollution attacks due to the inherent vulnerabilities of NC. Although there are several works on pollution attack detection, the attackers may continue to pollute packets in the next transmission of coded packets of the same generation from the source node to the destination nodes. Therefore, in this paper, we present an intrusion detection and location-aware prevention (IDLP) mechanism which does not only detect the polluted packets and drop them but also identify the attacker's exact location so as to block them and prevent packet pollution in the next transmissions. In the proposed IDLP mechanism, the detection and locating schemes are based on a null space-based homomorphic MAC scheme. However, the proposed IDLP mechanism is efficient because, in its initial phase (i.e., Phase 1), it is not needed to be applied to all mobile devices in order to protect the NC-enabled mobile small cells from the depletion of their resources. The proposed efficient IDLP mechanism has been implemented in Kodo, and its performance has been evaluated and compared with our previous IDPS scheme proposed in [1], in terms of computational complexity, communicational overhead, and successfully decoding probability as well
Original languageEnglish
Article number9019671
Pages (from-to)43863 - 43875
Number of pages13
JournalIEEE Access
Issue number00
Publication statusPublished - 12 Mar 2020


  • Network coding
  • pollution attacks
  • intrusion detection
  • location-aware prevention
  • efficiency
  • 5G


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