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malicious_url_detection [2025/04/23 21:27]
193.70.81.103 old revision restored (2023/09/27 13:28)
malicious_url_detection [2025/05/02 06:03] (current)
195.191.219.133 old revision restored (2023/12/11 04:34)
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 ====== Introduction ====== ====== Introduction ======
-  * Member : 蔡昀達, 廖其忻+  * Member : 蔡昀達, 廖其忻, 尹聖翔
   * Meeting :    * Meeting : 
  
Line 74: Line 77:
  
 - (黑名單太少,參考用)https://otx.alienvault.com/ - (黑名單太少,參考用)https://otx.alienvault.com/
 +
  
 ======  Meeting ====== ======  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 ===== ===== 09/16 =====
malicious_url_detection.1745414849.txt.gz · Last modified: 2025/04/23 21:27 by 193.70.81.103