{"id":43899,"date":"2015-09-16T16:03:12","date_gmt":"2015-09-16T02:03:12","guid":{"rendered":"http:\/\/www.irantahgig.ir\/?p=43899"},"modified":"2015-09-16T16:03:12","modified_gmt":"2015-09-16T02:03:12","slug":"%d8%b4%d8%a8%da%a9%d9%87-%d9%87%d8%a7%db%8c-%d8%b9%d8%b5%d8%a8%db%8c-%d8%a2%d9%85%d9%88%d8%b2%d8%b4%db%8c-%d8%b1%d9%82%d8%a7%d8%a8%d8%aa%db%8c-%d9%be%db%8c%d8%b4%d8%b1%d9%81%d8%aa%d9%87-%d8%af%d8%b1","status":"publish","type":"post","link":"http:\/\/www.irantahgig.ir\/?p=43899","title":{"rendered":"\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0648 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u06a9\u0644\u0627\u0647\u0628\u0631\u062f\u0627\u0631\u06cc"},"content":{"rendered":"

Improved competitive learning neural networks for network intrusion and fraud detection<\/span><\/p>\n

\u062a\u0627\u0631\u06cc\u062e:<\/strong> \u06f2\u06f0\u06f1\u06f2<\/span><\/p>\n

\u067e\u0627\u06cc\u06af\u0627\u0647:\u00a0<\/strong> \u0627\u0644\u0632\u0648\u06cc\u0631<\/span><\/p>\n

\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0627\u0635\u0644 \u0645\u0642\u0627\u0644\u0647<\/a><\/span><\/p>\n

\"OK\"<\/span><\/p>\n

\t

\n\n\t\t\n\t\t
\n\t\t\t200,000 \u0631\u06cc\u0627\u0644 – \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f<\/span> <\/span><\/a>\u0646\u0647\u0627\u06cc\u06cc \u06a9\u0631\u062f\u0646 \u062e\u0631\u06cc\u062f<\/a>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t<\/svg>\n\t\t\t\t\t\t\u0645\u0648\u0631\u062f \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f \u0627\u0636\u0627\u0641\u0647 \u0634\u062f\t\t\t\t\t<\/span>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t

<\/span><\/p>\n

\u0646\u0627\u0645 \u0645\u062c\u0644\u0647:\u00a0<\/strong>Neurocomputing<\/span><\/span><\/p>\n

\u0642\u06cc\u0645\u062a:<\/strong> \u06f2\u06f0\u06f0,\u06f0\u06f0\u06f0 \u0631\u06cc\u0627\u0644<\/span><\/p>\n

\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc:<\/strong> \u06f1\u06f1<\/span><\/p>\n

\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0641\u0627\u0631\u0633\u06cc:<\/strong> \u06f3\u06f3<\/span><\/p>\n

\u06a9\u062f: <\/strong>\u06f5\u06f5\u06f0\u06f3\u06f6<\/span><\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0641\u0627\u0631\u0633\u06cc<\/span><\/h3>\n

\u062f\u0631 \u0627\u06cc\u0646 \u067e\u0698\u0648\u0647\u0634\u060c \u062f\u0648 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062e\u0648\u0634\u0647\u200c\u0628\u0646\u062f\u06cc \u062c\u062f\u06cc\u062f \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645. \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 (ICLN) \u0648 \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0646\u0638\u0627\u0631\u062a\u06cc (SICLN) \u06a9\u0647 \u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 \u062a\u0634\u062e\u06cc\u0635 \u06a9\u0644\u0627\u0647\u0628\u0631\u062f\u0627\u0631\u06cc \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0645\u06cc\u200c\u0628\u0627\u0634\u0646\u062f. \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 (ICLN) \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062e\u0648\u0634\u0647\u200c\u0628\u0646\u062f\u06cc \u063a\u06cc\u0631\u0646\u0638\u0627\u0631\u062a\u06cc \u0645\u06cc\u200c\u0628\u0627\u0634\u062f\u060c \u06a9\u0647 \u0642\u0648\u0627\u0646\u06cc\u0646 \u062c\u062f\u06cc\u062f\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f (SCLN) \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc\u200c\u06a9\u0646\u062f. \u0646\u0648\u0631\u0648\u0646\u200c\u0647\u0627\u06cc \u0634\u0628\u06a9\u0647 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 (ICLN)\u00a0 \u0628\u0631\u0627\u06cc \u0627\u0631\u0627\u0626\u0647 \u0645\u0631\u06a9\u0632 \u062f\u0627\u062f\u0647 \u062a\u0648\u0633\u0637 \u0642\u0648\u0627\u0646\u06cc\u0646 \u0628\u0631\u0648\u0632 \u0634\u062f\u0647 \u062a\u0646\u0628\u06cc\u0647 \u0648 \u067e\u0627\u062f\u0627\u0634 \u062c\u062f\u06cc\u062f \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647\u200c\u0627\u0646\u062f. \u0627\u06cc\u0646 \u0642\u0648\u0627\u0646\u06cc\u0646 \u0628\u0631\u0648\u0632 \u0634\u062f\u0647\u060c \u0628\u06cc\u200c\u062b\u0628\u0627\u062a\u06cc \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f(SCLN) \u0631\u0627 \u0627\u0632 \u0628\u06cc\u0646 \u0645\u06cc\u200c\u0628\u0631\u0646\u062f. \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0646\u0638\u0627\u0631\u062a\u06cc (SICLN) \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0646\u0633\u062e\u0647 \u0628\u0627\u0632\u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 (ICLN) \u0645\u06cc\u200c\u0628\u0627\u0634\u062f . \u062f\u0631 SICLN (\u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0646\u0638\u0627\u0631\u062a\u06cc (SICLN) \u060c \u0642\u0648\u0627\u0646\u06cc\u0646 \u0628\u0631\u0648\u0632\u0631\u0633\u0627\u0646\u06cc\u200c\u0634\u062f\u0647 \u0646\u0638\u0627\u0631\u062a\u06cc \u0627\u0632 \u062f\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0647\u062f\u0627\u06cc\u062a \u0645\u0631\u0627\u062d\u0644 \u0622\u0645\u0648\u0632\u0634 \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u0631\u0633\u06cc \u0628\u0647 \u0646\u062a\u0627\u06cc\u062c \u062e\u0648\u0634\u0647\u200c\u0628\u0646\u062f\u06cc \u0628\u0647\u062a\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u06a9\u0646\u062f. \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0646\u0638\u0627\u0631\u062a \u0634\u062f\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062f\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u0634\u062f\u0647 \u0648 \u062f\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u0646\u0634\u062f\u0647 \u0627\u0639\u0645\u0627\u0644 \u0634\u062f\u0647 \u0648 \u062f\u0631 \u0633\u0637\u062d \u0628\u0627\u0644\u0627\u06cc\u06cc \u062f\u0631 \u0628\u0631\u0627\u0628\u0631 \u0627\u062a\u06cc\u06a9\u062a\u200c\u0647\u0627\u06cc \u0645\u0641\u0642\u0648\u062f\u06cc \u0648 \u062a\u0627\u062e\u06cc\u0631\u06cc \u0645\u0642\u0627\u0648\u0645 \u0645\u06cc\u200c\u0628\u0627\u0634\u062f. \u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646\u060c \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0646\u0638\u0627\u0631\u062a\u06cc (SICLN) \u062f\u0627\u0631\u0627\u06cc \u0642\u0627\u0628\u0644\u06cc\u062a \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u0628\u0648\u062f\u0647\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u06a9\u0627\u0645\u0644\u0627 \u0645\u0633\u062a\u0642\u0644 \u0627\u0632 \u062a\u0639\u062f\u0627\u062f \u0627\u0648\u0644\u06cc\u0647 \u062e\u0648\u0634\u0647\u200c\u0647\u0627 \u0645\u06cc\u200c\u0628\u0627\u0634\u062f.\u0628\u0631\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u0638\u0631\u060c \u0628\u0647 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0639\u0645\u0644\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062a\u062d\u0642\u06cc\u0642 \u0648 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062d\u0642\u06cc\u0642\u06cc \u062f\u0631 \u062a\u0634\u062e\u06cc\u0635 \u06a9\u0644\u0627\u0647\u0628\u0631\u062f\u0627\u0631\u06cc \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u0634\u0628\u06a9\u0647 \u067e\u0631\u062f\u0627\u062e\u062a\u06cc\u0645. \u0646\u062a\u0627\u06cc\u062c \u0627\u062b\u0628\u0627\u062a \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u0647\u0631 \u062f\u0648 \u0645\u0648\u0631\u062f ICLN \u0648 SICLN \u0628\u0647 \u0628\u0627\u06cc\u06af\u0627\u0646\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0627\u0644\u0627 \u0645\u06cc\u200c\u067e\u0631\u062f\u0627\u0632\u0646\u062f\u060c \u0648 SICLN \u062f\u0631 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627\u06cc \u062e\u0648\u0634\u0647\u200c\u0628\u0646\u062f\u06cc \u063a\u06cc\u0631\u0646\u0638\u0627\u0631\u062a\u06cc \u0633\u0646\u062a\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0647\u062a\u0631\u06cc \u062f\u0627\u0631\u062f.<\/span><\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/span><\/h3>\n

In this research, we propose two new clustering algorithms, the improved competitive learning network (ICLN) and the supervised improved competitive learning network (SICLN), for fraud detection and network intrusion detection. The ICLN is an unsupervised clustering algorithm, which applies new rules to the standard competitive learning neural network (SCLN). The network neurons in the ICLN are trained to represent the center of the data by a new reward-punishment update rule. This new update rule overcomes the instability of the SCLN. The SICLN is a supervised version of the ICLN. In the SICLN, the new supervised update rule uses the data labels to guide the training process to achieve a better clustering result. The SICLN can be applied to both labeled and unlabeled data and is highly tolerant to missing or delay labels. Furthermore, the SICLN is capable to reconstruct itself, thus is completely independent from the initial number of clusters.To assess the proposed algorithms, we have performed experimental comparisons on both research data and real-world data in fraud detection and network intrusion detection. The results demonstrate that both the ICLN and the SICLN achieve high performance, and the SICLN outperforms traditional unsupervised clustering algorithms<\/span><\/p>\n

\u0645\u0634\u062e\u0635\u0627\u062a \u0627\u0633\u062a\u0646\u0627\u062f\u06cc<\/strong><\/span><\/h3>\n

Lei, J. Z., & Ghorbani, A. A. (2012). Improved competitive learning neural networks for network intrusion and fraud detection. Neurocomputing, 75(1), 135-145<\/span><\/p>\n

\u062f\u0627\u0646\u0644\u0648\u062f \u0627\u0635\u0644 \u0645\u0642\u0627\u0644\u0647<\/span><\/h3>\n

\"10002icon\"<\/a>\u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0645\u0642\u0627\u0644\u0647 \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0648 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u06a9\u0644\u0627\u0647\u0628\u0631\u062f\u0627\u0631\u06cc<\/span><\/h1>\n

\u0645\u0642\u0627\u0644\u0647 “\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u062a\u0634\u062e\u06cc\u0635 \u0646\u0641\u0648\u0630 \u062f\u0631 \u0634\u0628\u06a9\u0647 \u0648 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u06a9\u0644\u0627\u0647\u0628\u0631\u062f\u0627\u0631\u06cc” \u062f\u0631 \u0633\u0627\u0644 \u06f2\u06f0\u06f1\u06f2 \u062f\u0631 \u0645\u062c\u0644\u0647\u00a0Neurocomputing<\/span> \u0686\u0627\u067e \u0634\u062f\u0647 \u0648 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0627\u0637\u0644\u0627\u0639\u0627\u062a\u06cc \u0627\u0644\u0632\u0648\u06cc\u0631 \u0646\u0645\u0627\u06cc\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a. \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0628\u0631\u0631\u0633\u06cc\u00a0\u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062e\u0648\u0634\u0647\u200c\u0628\u0646\u062f\u06cc\u060c\u00a0\u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 (ICLN)\u060c \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0646\u0638\u0627\u0631\u062a\u06cc (SICLN) \u0648\u00a0\u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0642\u0627\u0628\u062a\u06cc \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f (SCLN) \u067e\u0631\u062f\u0627\u062e\u062a\u0647 \u0627\u0633\u062a. \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0631\u0627\u0633\u0627\u0633 \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u067e\u0627\u06cc\u06af\u0627\u0647 \u0627\u0637\u0644\u0627\u0639\u0627\u062a\u06cc \u06af\u0648\u06af\u0644 \u0627\u0633\u06a9\u0648\u0644\u0627\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06f1\u06f8 \u0628\u0627\u0631 \u0645\u0648\u0631\u062f \u0627\u0633\u062a\u0646\u0627\u062f \u0642\u0631\u0627\u0631 \u06af\u0631\u0641\u062a\u0647 \u0627\u0633\u062a.<\/span><\/p>\n

\t\n\n\t\t\n\t\t

\n\t\t\t200,000 \u0631\u06cc\u0627\u0644 – \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f<\/span> <\/span><\/a>\u0646\u0647\u0627\u06cc\u06cc \u06a9\u0631\u062f\u0646 \u062e\u0631\u06cc\u062f<\/a>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t<\/svg>\n\t\t\t\t\t\t\u0645\u0648\u0631\u062f \u0628\u0647 \u0633\u0628\u062f \u062e\u0631\u06cc\u062f \u0627\u0636\u0627\u0641\u0647 \u0634\u062f\t\t\t\t\t<\/span>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t

Improved competitive learning neural networks for network intrusion and fraud detection \u062a\u0627\u0631\u06cc\u062e: \u06f2\u06f0\u06f1\u06f2 \u067e\u0627\u06cc\u06af\u0627\u0647:\u00a0 \u0627\u0644\u0632\u0648\u06cc\u0631 \u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0627\u0635\u0644 \u0645\u0642\u0627\u0644\u0647<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[84,127,316,515],"tags":[],"_links":{"self":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts\/43899"}],"collection":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=43899"}],"version-history":[{"count":1,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts\/43899\/revisions"}],"predecessor-version":[{"id":43909,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=\/wp\/v2\/posts\/43899\/revisions\/43909"}],"wp:attachment":[{"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=43899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=43899"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.irantahgig.ir\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=43899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}