Data Injection Attacks in Randomized Gossiping

Gentz, R., Xiaoxiao, S., Wai, H-T., Scaglione, A., Leshem, A.


IEEE Transactions on Signal and Information Processing over Networks, vol. 2, no. 4, pp. 523-538, Dec. 2016.


The subject of this paper is the detection and mitigation of data injection attacks in randomized average consensus gossip algorithms. It is broadly known that the main advantages of randomized average consensus gossip are its fault tolerance and distributed nature. Unfortunately, the flat architecture of the algorithm also increases the attack surface for a data injection attack. Even though we cast our problem in the context of sensor network security, the attack scenario is identical to existing models for opinion dynamics (the so-called DeGroot model) with stubborn agents steering the opinions of the group toward a final state that is not the average of the network initial states. We specifically propose two novel strategies for detecting and locating attackers and study their detection and localization performance numerically and analytically. Our detection and localization methods are completely decentralized and, therefore, nodes can directly act on their conclusions and stop receiving information from nodes identified as attackers. As we show by simulation, the network can often recover in this fashion, leveraging the resilience of randomized gossiping to reduced network connectivity.

Related Videos

Related Technologies

Related Impact Areas

Copyright Notice

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

  1. The following copyright notice applies to all of the above items that appear in IEEE publications: "Personal use of this material is permitted. However, permission to reprint/publish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE."

  2. The following copyright notice applies to all of the above items that appear in ACM publications: "© ACM, effective the year of publication shown in the bibliographic information. This file is the authorís version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the journal or proceedings indicated in the bibliographic data for each item."

  3. The following copyright notice applies to all of the above items that appear in IFAC publications: "Document is being reproduced under permission of the Copyright Holder. Use or reproduction of the Document is for informational or personal use only."