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    # 碩論 (2021/4/14) 第11次 > [碩論 (2021/1/6) 第1次](https://hackmd.io/lQUIFZCdSHOLwzvwI9WsOw?both) > [碩論 (2021/1/20) 第2次](https://hackmd.io/IbJDXXacSaetaHRpinuqpA?both) > [碩論 (2021/1/27) 第3次](https://hackmd.io/mzur4HIZRF66sjkMear1rQ) > [碩論 (2021/2/3) 第4次](https://hackmd.io/HBvT3a8tS9CxdUcCcQVkqg) > [碩論 (2021/2/24) 第5次](https://hackmd.io/MvviEMO_TyG1Xqd5sQTSPw) > [碩論 (2021/3/3) 第6次](https://hackmd.io/X4KKgcnnQMieS-dvl1lYyA) > [碩論 (2021/3/17) 第7次](https://hackmd.io/r2EZTug-Q3OHScgs0eMi5w) > [碩論 (2021/3/25) 第8次](https://hackmd.io/uxTGxw7LQTGkd2CU1gUIAQ) > [碩論 (2021/3/31) 第9次](https://hackmd.io/1fXLcGDBSC2txhP51G7nAQ) > [碩論 (2021/4/7) 第10次](https://hackmd.io/ipFw-4TxRd2xTbNQ6-DfMA) ###### tags: `碩論地獄` :::info [TOC] ::: ## Yen aka 不喝飲料人 ### 雲環境中基於虛擬機自我檢查偵測惡意行為之研究 > 英文題目:Using Virtual Machine Introspection to Detect Malicious Behavior for Cloud Environment 1. (Yin) 2. (古) 3. (Yen) S C : Proposed ... 開頭 4. (Cen) 5. (槍熊) 6. (Riz) 7. (晁誌) 8. (祐綸) 9. (韋德) 10. (晨偉) 11. (宗琪) 12. (議員) 13. (日日) * time * Intro - Introduction : * Motivation & Goals : * Related Work : * System : * Experiment : * Conclusions : * Total : * 老師 --- ## Gu aka 想喝但不能喝飲料人 ### 車載網路於異構邊緣雲中基於身分隱私之服務下載機制 > 英文題目:A Service Download Mechanism Based on Identity Privacy for VANET in Heterogeneous Edge-Cloud Environment 1. (Yin) 2. (古) 3. (Yen) 4. (Cen) 5. (槍熊) 6. (Riz) 7. (晁誌) 8. (祐綸) 9. (韋德) 10. (晨偉) 11. (宗琪) 12. (議員) 13. (日日) * time * Intro - Introduction : * Motivation & Goals : * Related Work : * System : * Experiment : * Conclusions : * Total : * 老師 * --- ## Yin aka 不喝飲料人 ### 基於深度學習之惡意舒服加密封包偵測 > 英文題目:A Deep Learning Approach for Malicious and Encrypted Packet Detection 1. (Yin) 2. (古) 3. (Yen) 4. (Cen) 5. (槍熊) 6. (Riz) 7. (晁誌) 8. (祐綸) 9. (韋德) 10. (晨偉) 11. (宗琪) 12. (議員) 13. (日日) * time * Intro - Introduction : * Motivation & Goals : * Related Work : * System : * Experiment : * Conclusions : * Total : * 老師 * 你的"新奈瑞爾"要有劇情,要有劇本~ * 每個flow chart都要有個 圖,e.g. 劇本:筱茵的一天 --- ## Cen aka 不喝飲料人 ### 5G網路中針對分散式舒服切片移動攻擊之防禦 > 英文題目: TODO 1. (Yin) 為什麼要用RL? 2. (古) 3. (Yen) 4. (Cen) 5. (槍熊) 5G環境部分的架設是如何建立的? 6. (Riz) 7. (晁誌) ue 使用ism切換使用的切片,是可以指定還是就只是說切換而已。攻擊的資料集怎麼來ㄉ 8. (祐綸) Defence的作為? 9. (韋德) 10. (晨偉) 11. (宗琪) 比較表,inter network 是指用 controller 偵測嗎?SDN switch 不是只有第一次遇到未知封包的時候會去詢問 controller 要怎麼處理,應該很難第一次遇到封包就偵測是不是惡意吧 12. (議員) 13. (日日) * time * Intro - Introduction : 6m55s * Motivation & Goals : 1m49s * Related Work : 3m58s * System : 2m31s * Experiment : * Conclusions : * Total : 15m15s * 老師 * system,這邊還是不夠 * 小茵這個問題很好,事實上,妳應該要有一個表格來說明所有的(機器學習?)好處壞處,為什麼要使用這個(Reinforecement Learning) * 第一頁 下一頁 下一頁 這算你的第一頁(4)對不對,應該不會只有圖吧?下一頁 你的起承轉合? 下一頁 這邊你有講 pdu session 你後面有講到 pdu session嗎? 有需要講到這麼細嗎? 每一個的起承轉合很重要,就是要留意這個地方,就這樣趕快加油。 * 你的5gc有結果嗎?替代方案? k8s? 好 --- ## Gun Bear (槍熊)aka 不喝飲料人 ![](https://i.imgur.com/isxDXMY.jpg) ### 基於SDN網路之Slow HTTP服務阻斷攻擊偵測 > 英文題目: Slow HTTP DoS Attack Detection in SDN Network 1. (Yin) comparison table第二個第三個是3小?是不是代表三個x? 2. (古) MTD為什麼不適合用在你的環境 3. (Yen) 4. (Cen) 5. (槍熊) 6. (Riz) 7. (晁誌) 要怎麼樣去找限制連線數量的閥值 8. (祐綸) 為什麼要用sdn architecutre? 9. (韋德) 限制同一來源的連線會對正常流量的使用造成影響嗎 10. (晨偉) 限制同一來源的連線,是不是還是會受到分散式的攻擊? 11. (宗琪) 比較表中有三個 X,是代表不要用到會比較好嗎?少了這三個東西,但你的方法還是可以有差不多的效果嗎? 12. (議員) 13. (日日) 無情缺席 太神啦 一知道要報告就缺席 * time * Intro - Introduction : 1m2s * Motivation & Goals : 59s * Related Work : 3m26a * System : 1m1s * Experiment : * Conclusions : * Total : 6m32s * 老師 * 還是要提醒大家一下哦,為什麼老師要我們要propose三個topic,就是要你換題目的時候可以很smooth的無縫切換,所以那時候你就可以很快的無縫切換,所以這邊大家重新來幫他看看有什麼問題。 * 一般來說你的proposed都要是"O",集合他們的優點,很少看過有"X"的,會讓人搞不清楚那個含義,你打X就是不需要,為什你要survey他? * 總之,會混肴,不太好。 * MTD還是可以用吧 * 用threshold的方式是比較簡易的,所以大家講的問題你都要考慮。 * 你用SDN的什麼特性,爲什麼要用SDN?~~(因為sdn很性感舒服)~~ * 統計一定要SDN才能做嗎?我覺得不見得。如果你要用SDN就要有SDN的特性跟特色。然後SDN用在DDOS也蠻多的,你survey的夠多嗎?我覺得這些才是你需要survey的。 * 因為你是用SDN,那我可不可以不要集中式控管?我可不可以放到k8s上?edge端會不會離user更近,這樣攔截更快? * Summary: * 你用了SDN的什麼特性? * 跟用了SDN可以離user更近嗎? * 你的立足點沒有很足的話,你後面要改的東西會很多? * 你要面對問題,知道ㄇ?你新提問題的時候一定會有很多的問題。 * 禮拜五我要開12小時的會,沒空~~~~~~ * 你可以考慮p4, k8s, edge computing。你不搭這些至少MTD吧? --- ## Riz ### In-Network traffic classification using programmable data plane 1. (Yin) 2. (古) 3. (Yen) 4. (Cen) 5. (槍熊) 6. (Riz) 7. (晁誌) 8. (祐綸) what do u want to classify for? 9. (韋德) 10. (晨偉) 11. (宗琪) p19, why do you mask IP src and dst? You could remove them when you learn a model or classify packets. If you remove data that you don't need, it may reduce training time. 12. (議員) 13. (日日) * time * Intro - Introduction : 3m39s * Background : 5m29s * Related Work : 5m3s * Motivation & Goals & comparison table: 2m4s * Workflow - System : 5m38s * Experiment Plan : 1m4s * Conclusions : * Total : 23m * 老師 * P15,is too late to show your motivation & goal --- --- ## Template 1. (Yin) 2. (古) 3. (Yen) 4. (Cen) 5. (槍熊) 6. (Riz) 7. (晁誌) 8. (祐綸) 9. (韋德) 10. (晨偉) 11. (宗琪) 12. (議員) 13. (日日) * time * Intro - Introduction : * Motivation & Goals : * Related Work : * System : * Experiment : * Conclusions : * Total : * 老師 ## template * time * Intro - Introduction : * Background : * Related Work : * Motivation & Goals & comparison table: * Workflow - System : * Experiment Plan : * Conclusions : * Total :

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