{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T02:37:23Z","timestamp":1768963043125,"version":"3.49.0"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Cyber-Phys. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    Cross-domain communication systems are important in multiple areas such as smart factories and automatic driving. Derived from real-life scenarios, each domain has a local network and accesses the public network via a central server, which makes it easy to manage the local network and protect entities in the domain. Based on this, a cross-domain communication system follows the \u201c\n                    <jats:italic toggle=\"yes\">device-server-server-device<\/jats:italic>\n                    \u201d pattern. For each communication session, there are two steps: authentication and communication. The authentication phase helps two parties to build a secure communication channel. If the server is compromised, the authentication request can be sent to the attacker and then the attacker can impersonate the original receiver. As a result, it is necessary to effectively solve this server-compromise case. Further, anomaly detection is required to monitor the behaviors of entities in domains. Current deep learning solutions deploy autoencoder-based models to reconstruct input data and then detect anomalies. However, they feed raw input data into the models, which makes it hard to reconstruct evolving data streams.\n                  <\/jats:p>\n                  <jats:p>In this work, we focus on the detection of compromised entities for cross-domain communication systems. Specifically, we split the problem into two parts: the server-compromise case in the authentication phase and anomaly detection for the whole system. Toward the server-compromise case, we propose a blockchain-based \u201cdouble verification\u201d scheme to prevent the server from making decisions on its own. Specifically, nodes in the blockchain network evaluate submitted records using the public key infrastructure. Meanwhile, the distributed nature of blockchain allows us to deploy more machines as verifiers to monitor the behavior of servers. For anomaly detection, we propose to feed the degree of change of items instead of raw item values into deep learning models, which is more adaptive to evolving data streams. We propose to use the linear combination of historical data and recent data to compute the degree of change. Finally, we analyze the security properties of the proposed system and evaluate the proposed anomaly detection method using real datasets. We build a simple cross-domain communication system using the Fabric framework to simulate the \u201cdouble verification\u201d scheme and the proposed anomaly detection method achieves around 0.11 accuracy improvement on average.<\/jats:p>","DOI":"10.1145\/3748819","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T14:29:49Z","timestamp":1752762589000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of Compromised Entities in Cross-Domain Communication Systems"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6875-5691","authenticated-orcid":false,"given":"Shiqing","family":"Li","sequence":"first","affiliation":[{"name":"Illinois Advanced Research Center at Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4436-9200","authenticated-orcid":false,"given":"Utku","family":"Tefek","sequence":"additional","affiliation":[{"name":"Illinois Advanced Research Center at Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3290-2514","authenticated-orcid":false,"given":"Ertem","family":"Esiner","sequence":"additional","affiliation":[{"name":"Illinois Advanced Research Center at Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6040-6865","authenticated-orcid":false,"given":"Zbigniew","family":"Kalbarczyk","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3016-0270","authenticated-orcid":false,"given":"Deming","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2021.563060"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3190508.3190538"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450023"},{"key":"e_1_3_3_5_2","first-page":"22","article-title":"Ethereum white paper","volume":"1","author":"Buterin Vitalik","year":"2013","unstructured":"Vitalik Buterin. 2013. 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