{"id":63107,"date":"2025-04-14T11:58:40","date_gmt":"2025-04-14T11:58:40","guid":{"rendered":""},"modified":"2025-04-14T11:58:40","modified_gmt":"2025-04-14T11:58:40","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63107","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-63107\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 FedMade: \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u062f\u0631\u0627\u0644 \u0642\u0648\u06cc \u0628\u0631\u0627\u06cc \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc IoT \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0631\u0648\u0634 \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u067e\u0648\u06cc\u0627"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nFedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 FedMade: \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u062f\u0631\u0627\u0644 \u0642\u0648\u06cc \u0628\u0631\u0627\u06cc \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc IoT \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0631\u0648\u0634 \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u067e\u0648\u06cc\u0627<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nShihua Sun, Pragya Sharma, Kenechukwu Nwodo, Angelos Stavrou, Haining Wang<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
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\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
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\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n20<\/td>\n<\/tr>\n
\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nCryptography and Security,Networking and Internet Architecture,\u0631\u0645\u0632\u0646\u06af\u0627\u0631\u06cc \u0648 \u0627\u0645\u0646\u06cc\u062a , \u0634\u0628\u06a9\u0647 \u0633\u0627\u0632\u06cc \u0648 \u0645\u0639\u0645\u0627\u0631\u06cc \u0627\u06cc\u0646\u062a\u0631\u0646\u062a ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 13 August, 2024; originally announced August 2024. , Comments: To appear in the Information Security Conference (ISC) 2024<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 13 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u0628\u0631\u0627\u06cc \u062d\u0636\u0648\u0631 \u062f\u0631 \u06a9\u0646\u0641\u0631\u0627\u0646\u0633 \u0627\u0645\u0646\u06cc\u062a \u0627\u0637\u0644\u0627\u0639\u0627\u062a (ISC) 2024<\/td>\n<\/tr>\n
\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc <\/td>\n\n INSPIRE HEP<\/a>
\n
\n NASA ADS<\/a>
\n
\n Google Scholar<\/a>
\n
\n Semantic Scholar<\/a>
\n
\n arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
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\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

The rapid proliferation of Internet of Things (IoT) devices across multiple sectors has escalated serious network security concerns. This has prompted ongoing research in Machine Learning (ML)-based Intrusion Detection Systems (IDSs) for cyber-attack classification. Traditional ML models require data transmission from IoT devices to a centralized server for traffic analysis, raising severe privacy concerns. To address this issue, researchers have studied Federated Learning (FL)-based IDSs that train models across IoT devices while keeping their data localized. However, the heterogeneity of data, stemming from distinct vulnerabilities of devices and complexity of attack vectors, poses a significant challenge to the effectiveness of FL models. While current research focuses on adapting various ML models within the FL framework, they fail to effectively address the issue of attack class imbalance among devices, which significantly degrades the classification accuracy of minority attacks. To overcome this challenge, we introduce FedMADE, a novel dynamic aggregation method, which clusters devices by their traffic patterns and aggregates local models based on their contributions towards overall performance. We evaluate FedMADE against other FL algorithms designed for non-IID data and observe up to 71.07% improvement in minority attack classification accuracy. We further show that FedMADE is robust to poisoning attacks and incurs only a 4.7% (5.03 seconds) latency overhead in each communication round compared to FedAvg, without increasing the computational load of IoT devices.<\/p>\n

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\u0645\u062d\u0642\u0642\u0627\u0646 IDS \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0641\u062f\u0631\u0627\u0644 (FL) \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0648\u0645\u06cc \u0633\u0627\u0632\u06cc \u0645\u06cc \u06a9\u0646\u0646\u062f \u060c \u062f\u0631 \u062f\u0633\u062a\u06af\u0627\u0647 \u0647\u0627\u06cc IoT \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u0646\u062f.\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 \u060c \u0646\u0627\u0647\u0645\u06af\u0648\u0646\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u060c \u0646\u0627\u0634\u06cc \u0627\u0632 \u0622\u0633\u06cc\u0628 \u067e\u0630\u06cc\u0631\u06cc \u0647\u0627\u06cc \u0645\u062a\u0645\u0627\u06cc\u0632 \u062f\u0633\u062a\u06af\u0627\u0647 \u0647\u0627 \u0648 \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc 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\u0628\u0631\u0637\u0631\u0641 \u06a9\u0646\u0646\u062f \u060c \u06a9\u0647 \u0628\u0647 \u0637\u0648\u0631 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062f\u0642\u062a \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062d\u0645\u0644\u0627\u062a \u0627\u0642\u0644\u06cc\u062a \u0631\u0627 \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f.\u0628\u0631\u0627\u06cc \u063a\u0644\u0628\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u0686\u0627\u0644\u0634 \u060c \u0645\u0627 FedMade \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u060c \u06cc\u06a9 \u0631\u0648\u0634 \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u067e\u0648\u06cc\u0627 \u062c\u062f\u06cc\u062f \u060c \u06a9\u0647 \u062e\u0648\u0634\u0647 \u0647\u0627 \u062f\u0633\u062a\u06af\u0627\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0627 \u0627\u0644\u06af\u0648\u0647\u0627\u06cc \u062a\u0631\u0627\u0641\u06cc\u06a9\u06cc \u0622\u0646\u0647\u0627 \u0642\u0631\u0627\u0631 \u0645\u06cc \u062f\u0647\u0646\u062f \u0648 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0645\u062d\u0644\u06cc \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0633\u0647\u0645 \u0622\u0646\u0647\u0627 \u062f\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f \u06a9\u0644\u06cc \u062c\u0645\u0639 \u0645\u06cc \u06a9\u0646\u0646\u062f.\u0645\u0627 FedMade \u0631\u0627 \u062f\u0631 \u0628\u0631\u0627\u0628\u0631 \u0633\u0627\u06cc\u0631 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc FL \u06a9\u0647 \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u063a\u06cc\u0631 IID \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u0627\u0646\u062f \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u062a\u0627 71.07 \u066a \u0628\u0647\u0628\u0648\u062f \u062f\u0631 \u062f\u0642\u062a \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062d\u0645\u0644\u0647 \u0627\u0642\u0644\u06cc\u062a \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 Fedmade \u0646\u0633\u0628\u062a \u0628\u0647 \u062d\u0645\u0644\u0627\u062a \u0645\u0633\u0645\u0648\u0645\u06cc\u062a \u0642\u0648\u06cc \u0627\u0633\u062a \u0648 \u062a\u0646\u0647\u0627 \u062f\u0631 \u0647\u0631 \u062f\u0648\u0631 \u0627\u0631\u062a\u0628\u0627\u0637\u06cc \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 FEDAVG \u060c \u0628\u062f\u0648\u0646 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0628\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u062f\u0633\u062a\u06af\u0627\u0647 \u0647\u0627\u06cc IoT \u060c \u062f\u0631 \u0647\u0631 \u062f\u0648\u0631 \u0627\u0631\u062a\u0628\u0627\u0637\u06cc 4.7 \u066a (5.03 \u062b\u0627\u0646\u06cc\u0647) \u062f\u0631 \u0647\u0631 \u062f\u0648\u0631 \u0627\u0631\u062a\u0628\u0627\u0637\u06cc \u0645\u062a\u062d\u0645\u0644 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

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