<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Othmane, Friha</style></author><author><style face="normal" font="default" size="100%">Ferrag, Mohamed-Amine</style></author><author><style face="normal" font="default" size="100%">Mohamed Benbouzid</style></author><author><style face="normal" font="default" size="100%">Berghout, Tarek</style></author><author><style face="normal" font="default" size="100%">Burak, Kantarci</style></author><author><style face="normal" font="default" size="100%">Kim-Kwang, Raymond-Choo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT</style></title><secondary-title><style face="normal" font="default" size="100%">Computers &amp; Security</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/abs/pii/S016740482300007X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">127</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Advanced technologies, such as the&amp;nbsp;Internet of Things&amp;nbsp;(IoT) and&amp;nbsp;Artificial Intelligence&amp;nbsp;(AI), underpin many of the innovations in&amp;nbsp;Industry 4.0. However, the&amp;nbsp;interconnectivity&amp;nbsp;and open nature of such systems in smart industrial facilities can also be targeted and abused by&amp;nbsp;malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP)&amp;nbsp;Federated Learning&amp;nbsp;(FL)-based&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/intrusion-detection-system&quot; title=&quot;Learn more about IDS from ScienceDirect's AI-generated Topic Pages&quot;&gt;I&lt;/a&gt;D&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/intrusion-detection-system&quot; title=&quot;Learn more about IDS from ScienceDirect's AI-generated Topic Pages&quot;&gt;S&lt;/a&gt;&amp;nbsp;(2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three&amp;nbsp;building blocks, namely: a&amp;nbsp;key exchange protocol&amp;nbsp;(for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of&amp;nbsp;cyber attacks&amp;nbsp;in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based&amp;nbsp;IDS&amp;nbsp;solutions.
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