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--- malicious_url_detection [2025/05/07 17:58]
173.212.247.30 old revision restored (2023/04/07 05:26)
+++ malicious_url_detection [2025/05/10 05:16] (current)
146.70.116.177 old revision restored (2025/05/09 23:42)
@@ Line 1: / Line 1: @@
+Cr
 ====== Introduction ======
-  * Member : 蔡昀達, 廖其忻
+  * Member : 蔡昀達, 廖其忻, 尹聖翔
   * Meeting :
@@ Line 74: / Line 76: @@
 - (黑名單太少，參考用)https://otx.alienvault.com/
 ======  Meeting ======
+===== 10/15 =====
-===== 09/23 progress =====
+{{:1015.pptx|}}
-  - tfidf results
-    - accuracy : 98.3%
-  - model explain mechanism
-    - implemented : highlight  trigger pattern
-    - results1 (tree interpreter):
-    {{:dada.1.png}}
-    - results2 (lime interpreter):
-    {{:dada.2.png}}
-^Year^Venue^Title^Link^Assign^
-|2016|ACM|LIME:Why should i trust you?: Explaining the predictions of any classifier|[[https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf|PDF]]| |
-|2014|KAIS|Shapley sampling values:Explaining prediction models and individual predictions with feature contributions|[[|PDF]]| |
-|2017|arxiv|DeepLIFT:Learning important features through propagating activation differences|[[|PDF]]| |
-|2016|IEEE SP|QII:Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems|[[|PDF]]| |
-|2015|PLoS ONE|Layer-wise relevance propagation:On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation|[[|PDF]]| |
-|2010|PLoS ONE|Shapley regression values:Analysis of regression in game theory approach|[[|PDF]]| |
+===== 10/08 =====
+{{:1008.pptx|}}
+===== 10/01 =====
+{{:1001.pptx|}}
+===== 09/24 =====
+{{:0924.pptx|}}
 ===== 09/16 =====

mslab

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