北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】

首頁-成功案例-algorithm
Assignment 1 for Semester 2, 2021
時間: 2021-09-01 09:40:14

INSTRUCTIONS: ? This assignment is a total of 100 marks worth 15% of your overall grade for this course. ? Please submit your assignment in the Assignment section on Wattle using the Turnitin submission link. When uploading to Wattle you must submit the following, combined into a single ’PDF’ document: 1. Your assignment/report in a pdf document. 2. All your R codes you have used for the assignment added as an Appendix to the end of the report. Failure to upload the R code will result in a penalty. ? Assignment solutions should be typed. Your assignment may include some carefully edited R output (e.g. graphs, tables) showing the results of your data analysis and a discussion of these results, as well as some carefully selected code. Please be selective about what you present and only include as much R output as necessary to justify your solution. It is important to be be concise in your discussion of the results. Clearly label each part of your report with the part of the question that it refers to. ? Unless otherwise advised, use a significance level of 5%. ? Marks may be deducted if these instructions are not strictly adhered to, and marks will certainly be deducted if the total report is of an unreasonable length, i.e. more than 10 pages including graphs and tables. You must include an appendix that is in addition to the above page limits which include all the R code. Although, the appendix will not be marked but if the R codes are not provided then marks will be deducted. The R codes are required should there be any question the markers have about the work you have submitted. ? You may ask me (Abhinav Mehta) questions about this assignment up to 24 hours before the submission time. This will allow me enough time to respond to your questions. The tutors will not entertain any questions about the assignment other than troubleshooting R codes. ? Late submissions will attract a penalty of 5% of your mark for each day of delay. No assignments will be accepted 10 days beyond the due date. ? Extensions will usually be granted on medical or compassionate grounds on production of appropriate evidence, but must have my permission by no later than 24 hours before the submission date. If you are granted an extension and submit your assignment after the extended deadline then the late submission penalty will still apply. Assignment 1 - Sem 2, 2021 Page 1 of 3 Question 1 [40 Marks] Data on eruptions of Old Faithful Geyser, in October 1980 was collected and stored in a .csv file ‘oldfaithful’. Variables are the duration in seconds of the current eruption, and the interval time in minutes to the next eruption. Data was not collected between approximately midnight and 6 AM. It is suspected that Duration is associated with the Interval (a) [5 marks] Conduct an exploratory data analysis to assess whether the two variables are associated. Is there a statistically significant correlation between the variables? Use the cor.test() function to conduct a suitable hypothesis test. Clearly specify the hypotheses you are testing and present and interpret the results. (b) [20 marks] Fit a simple linear regression (SLR) model with Interval as the response variable and Duration as the predictor. Construct a plot of the residuals against the fitted values, a normal Q-Q plot of the residuals, a bar plot of the leverages for each observation and a bar plot of Cook’s distances for each observation. Use these plots (and other means) to comment on the model assumptions and on any unusual data points. (c) [10 marks] What are the estimated coefficients of the SLR model in part (b) and the standard errors associated with these coefficients? Interpret the values of these estimated coefficients and perform t-tests to test whether or not these coefficients differ significantly from zero. What do you conclude as a result of these t-tests? (d) [5 marks] If there is a eruption which lasted for 100 seconds then what will be the interval of time before the next eruption, as predicted by your model? Construct an appropriate interval estimate for the length of this interval. Assignment 1 - Sem 2, 2021 Page 2 of 3 Question 2 [60 Marks] The international bank UBS regularly produces a report (UBS, 2009) on prices and earnings in major cities throughout the world. Three of the measures they include are prices of 1kg of rice, a 1kg loaf of bread and the price of a Big Mac. An interesting feature of the prices they report is measured in the minutes of labor required for a ‘typical’ worker in that location to earn enough money to purchase the commodity. Using minutes of labor corrects at least in in part for currency fluctuations, prevailing wage rates, and local prices. The data file includes measurements for rice, bread and big mac from the 2003 and 2009 reports. The year 2003 was before the GFC and the year 2009 which is after the GFC and so would reflect changes in prices due to this recession. You may access the dataset from the ‘ALR4’ package and load the data from this package using the data(UBSprices) command. (a) [5 marks] Plot a scatterplot of Big Mac prices with year 2003 as the predictor variable. Add a line for y = x to this plot. How would you interpret points that are on this line, above the line and below the line? (b) [5 marks] Identify the cities which you consider to have the highest increase or decrease in the Big Mac price. You will find these R functions very useful for this step: identify() and row.names(). (c) [10 marks] Is a linear model the right one to fit the Big Mac price data? If not, what variable transformations would you consider appropriate? Give justification for your choice of transformation. (d) [20 marks] Fit a linear model with your chosen transformation using OLS estimation method. Write out the mathematical expression for the functional form of your model using both the transformed variables and the untransformed original variables. Interpret the effect of coefficients on the untransformed response variable in the untransformed version of the regression model. (e) [10 marks] Produce the ANOVA (Analysis of Variance) table for the SLR model and interpret the results of the F-test. What is the coefficient of determination for this model and how should you interpret this summary measure? (f) [10 marks] Assess the model fit for the assumptions of a Normal Error Regression Model. Are there any influential points in your regression model and if yes, identify these cities and the type of influence they have on the model fit.

說明:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?本作業總共100分,占本課程總成績的15%。


?請使用Turnitin在Wattle作業部分提交作業


提交鏈接。當上傳到Wattle時,您必須提交以下內容,包括


轉換為單個“PDF”文檔:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】1.您的作業/報告以pdf文檔形式提供。


2.您在作業中使用的所有R代碼作為附錄添加到


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】報告結束。未能上傳R代碼將導致罰款。


?應鍵入作業解決方案。你的作業可能包括一些細節


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】編輯的R輸出(如圖形、表格),顯示數據分析結果和


討論這些結果,以及一些精心挑選的代碼。請選擇


關于您所呈現的內容,并且僅包含必要的R輸出,以證明


你的解決方案。在討論結果時要簡明扼要,這一點很重要。清晰地


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在報告的每一部分都貼上它所指的問題部分的標簽。


?除非另有建議,否則使用5%的顯著性水平。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?如果未嚴格遵守這些說明,可能會扣減分數,以及


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】如果總報告的長度不合理,即超過


超過10頁,包括圖表。您必須包括一個附錄,該附錄在


除上述頁面限制外,還包括所有R代碼。盡管如此,附錄


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】不會被標記,但如果未提供R代碼,則將扣除標記。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】如果標記有任何關于標記的問題,則需要R代碼


您提交的工作。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?您可以在24小時內向我(Abhinav Mehta)詢問有關本任務的問題


在提交時間之前。這將使我有足夠的時間回答你的問題。導師不會回答任何關于作業的問題,除非


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】故障排除R代碼。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?逾期提交的材料每延遲一天將受到5%分數的處罰。不


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】作業將在到期日后10天接受。


?在生產時,通常基于醫療或同情理由批準延期


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】提供適當證據,但必須在24小時前獲得我的許可


提交日期。如果您獲準延期并在


延長期限后,逾期提交罰款仍將適用。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】作業1-Sem 2021第2頁共3頁第1頁


問題1[40分]


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】1980年10月老忠實間歇泉噴發的數據收集并儲存在


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】一個.csv文件“OldFaith”。變量是當前噴發的持續時間(以秒為單位),


下一次噴發的間隔時間(以分鐘為單位)。未在兩個月之間收集數據


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】大約午夜和早上6點。懷疑持續時間與


間隔


(a) [5分]進行探索性數據分析,以評估這兩個變量


是關聯的。變量之間是否存在統計顯著相關性?


使用cor.test()函數進行適當的假設檢驗。明確規定


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】您正在測試的假設,并呈現和解釋結果。


(b) [20分]擬合以區間為響應的簡單線性回歸(SLR)模型


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】變量和持續時間作為預測因子。構建一個殘差圖,并將其與


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】擬合值、殘差的正常Q-Q圖、杠桿的條形圖


每次觀察,以及每次觀察庫克距離的條形圖。使用


這些圖(和其他方式)用于對模型假設和任何


不尋常的數據點。


(c) [10分]第(b)部分中的SLR模型的估計系數是多少


與這些系數相關的標準誤差是多少?解讀這些價值觀


估計系數并進行t檢驗,以測試這些系數是否


與零顯著不同。通過這些t檢驗,你得出了什么結論?


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】(d) [5馬克]如果爆發持續了100秒,那么會發生什么


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】你的模型預測的下次噴發前的時間間隔?構造


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】此間隔長度的適當間隔估計。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】作業1-掃描電鏡2021年第2頁,共3頁


問題2[60分]


國際銀行瑞銀集團(UBS)定期編制一份關于價格和收益的報告(瑞銀集團,2009年)


全球主要城市的收入。其中包括三項措施:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】一公斤大米、一公斤面包和一個巨無霸的價格。有趣的故事


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】他們報告的價格特征是以一項工作所需的勞動分鐘數來衡量的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】該地區的“典型”工人掙錢購買商品。


使用勞動記錄至少部分地糾正了當前的貨幣波動


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】工資率和當地價格。數據文件包括大米、面包和大蛋糕的測量值


2003年和2009年報告中的mac。2003年是在GFC和


2009年是全球金融危機之后的一年,因此將反映出經濟衰退導致的價格變化。


您可以從“ALR4”包訪問數據集并加載t


Neural Dynamics on Complex Networks
時間: 2021-08-31 09:55:31

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】ABSTRACT Learning continuous-time dynamics on complex networks is crucial for understanding, predicting and controlling complex systems in science and engineering. However, this task is very challenging due to the combinatorial complexities in the structures of high dimensional systems, their elusive continuous-time nonlinear dynamics, and their structural-dynamic dependencies. To address these challenges, we propose to combine Ordinary Diferential Equation Systems (ODEs) and Graph Neural Networks (GNNs) to learn continuous-time dynamics on complex networks in a datadriven manner. We model diferential equation systems by GNNs. Instead of mapping through a discrete number of neural layers in the forward process, we integrate GNN layers over continuous time numerically, leading to capturing continuous-time dynamics on graphs. Our model can be interpreted as a Continuous-time GNN model or a Graph Neural ODEs model. Our model can be utilized for continuous-time network dynamics prediction, structured sequence prediction (a regularly-sampled case), and node semi-supervised classifcation tasks (a one-snapshot case) in a unifed framework. We validate our model by extensive experiments in the above three scenarios. The promising experimental results demonstrate our model’s capability of jointly capturing the structure and dynamics of complex systems in a unifed framework. CCS CONCEPTS ? Mathematics of computing → Graph algorithms; Ordinary diferential equations; ? Computing methodologies → Neural networks; Network science; KEYWORDS Continuous-time Graph Neural Networks; Graph Neural Ordinary Diferential Equations; Continuous-time GNNs; Graph Neural ODEs; Continuous-time Network Dynamics Prediction; Structured Sequence Prediction; Graph Semi-supervised Learning; Diferential Deep Learning on Graphs; ACM Reference Format: Chengxi Zang and Fei Wang. 2020. Neural Dynamics on Complex Networks. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’20), August 23–27, 2020, Virtual Event, CA, USA. ACM, New York, NY, USA, 11 pages. http://doi.org/10.1145/3394486.3403132 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. KDD ’20, August 23–27, 2020, Virtual Event, CA, USA ? 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-7998-4/20/08. . . $15.

摘要


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】學習復雜網絡上的連續時間動力學對于理解、預測和控制復雜系統至關重要


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在科學和工程方面。然而,這項任務非常具有挑戰性


由于高層建筑結構中的組合復雜性


維系統,它們難以捉摸的連續時間非線性動力學,以及它們的結構動力學相關性。解決


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】面對這些挑戰,我們建議將普通的差別


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】方程系統(ODE)和圖形神經網絡(GNNs)用于


以數據驅動的方式學習復雜網絡上的連續時間動力學。我們用GNNs對微分方程組進行建模。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】而不是通過一個離散的神經層映射


在正向過程中,我們在連續時間內集成GNN層


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】從數值上講,這將導致在計算機上捕獲連續時間動態


圖。我們的模型可以解釋為連續時間GNN


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】模型或圖形神經ODEs模型。我們的模型可用于


連續時間網絡動力學預測,結構化序列


預測(定期采樣的情況)和節點半監督


統一框架中的分類任務(一個快照案例)。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】我們通過上述三個領域的大量實驗驗證了我們的模型


情節。有希望的實驗結果證明了我們的結論


模型聯合捕捉結構和動力學的能力


在一個統一的框架中對復雜系統進行分析。


CCS概念


?計算數學→ 圖算法;普通的


微分方程計算方法→ 神經網絡;網絡科學;


關鍵詞


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】連續時間圖神經網絡;圖神經網絡


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】微分方程;連續時間GNNs;圖神經微分方程;


連續時間網絡動態預測;結構化序列預測;圖半監督學習;不同的


深入學習圖形;


ACM參考格式:


西藏城西和王菲。2020.復雜網絡上的神經動力學。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】第26屆ACM SIGKDD知識發現會議記錄


和數據挖掘(KDD'20),2020年8月23日至27日,虛擬事件,加利福尼亞州,美國,


紐約,紐約,美國,11頁。http://doi.org/10.1145/3394486.3403132


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】允許為個人或個人目的制作本作品全部或部分的數字或硬拷貝


教室使用是免費的,前提是不制作或分發副本


以獲取利潤或商業利益,且副本包含本通知和完整引文


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在第一頁。本作品組成部分的版權歸非版權所有者所有


必須尊重作者。允許信用提取。以其他方式復制,或


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】重新發布、在服務器上發布或重新分發到列表,需要事先獲得特定權限


和/或收費。請求來自的權限permissions@acm.org.


KDD'20,2020年8月23日至27日,虛擬活動,加利福尼亞州,美國


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?2020版權歸所有者/作者所有。授權給ACM的出版權。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】ACM ISBN 978-1-4503-7998-4/20/08$15


ELEC0009 – LSA Simulation Laboratory 2021
時間: 2021-08-29 08:48:12

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】You are expected to complete the following set of simulation tasks using Multisim. Your submission is an electronic lab-book as a .PDF file. Please note that you are NOT expected to write a report, but your submission is an electronic labbook populated by results, calculations, and very brief notes. This is an individual assignment, and you should work on your own to complete the tasks. Task A: This task is primarily on large signal analysis. While here we refer to total signal values (e.g. iC) for completeness, in each case you should consider a DC/large signal change. Then from this large signal analysis you should derive small signal parameters. 1. From the available Multisim library of transistors, select one BJT and one enhancement MOSFET. If you select an npn BJT you must select a PMOS transistor, and if you select a pnp BJT you must select an NMOS transistor. Indicating their key features, very briefly comment on your selections. 2. Plot iC vs. vCE for different vBE values on the same graph and estimate ro for the selected BJT based on the plots. 3. Evaluate β for the BJT using a simulation setup. 4. Plot iC vs. vBE and estimate gm and rπ for a given VBE for the selected BJT. 5. Plot iD vs. vDS for different vGS values on the same graph and estimate ro for the selected MOSFET based on the plots. 6. Plot iD vs. vGS and estimate gm for a given VGS for the selected MOSFET. Task B: In this task, you must use the transistors you have selected in Task A and observe the following: ? You MUST at least once design a BJT circuit and you MUST at least once design a MOSFET circuit. ? You should use an ideal sinusoidal input voltage signal with an amplitude of 40 mV (vsig). vsig must have a resistor in series with it (Rsig). In two of the following subtasks you should use Rsig = 100 kΩ and only in one of the subtasks you should use Rsig = 100 Ω. You should justify your choices briefly. ? You only have access to two DC supply voltages set at any value between - 6 V to + 6 V. ? You should consider a load resistance RL = 60 kΩ. ? You have access to any number of resistors and capacitors of any value. You should appropriately bias the transistors in the circuits and AC-couple input and output voltage signals. ? You should use source or emitter degeneration resistors in all three circuits. ? Your key priority is to maximise gain in each circuit. You should clearly demonstrate how you achieve this goal. ? Your other design decision is to ensure that lower -3dB corner frequency is no more than 60 Hz. If you cannot achieve this goal explain why. ? In each case clearly demonstrate your designs and, include calculations, brief notes on design choices you make and include appropriate simulation results and graphs to indicate successful implementation of your design. Evaluate the designs at low frequencies, midband and high frequencies. 1. Design and simulate either a common emitter or a common source amplifier circuit. 2. Design and simulate either a common base or a common gate amplifier circuit. 3. Design and simulate either a common collector or a common drain amplifier circuit.

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】您需要使用Multisim完成以下一組模擬任務。你的意見


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】是一本電子實驗書,格式為.PDF文件。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】請注意,您不需要編寫報告,但您提交的是一個電子實驗室,里面有結果、計算和非常簡短的注釋。


這是一項個人任務,你應該自己完成任務。


任務A:


這項任務主要是大信號分析。在這里,我們指的是總信號值(如iC)


完整性,在每種情況下,你應該考慮一個DC /大信號變化。那么從這個大的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】信號分析您應該導出小信號參數。


1.從可用的Multisim晶體管庫中,選擇一個BJT和一個增強


MOSFET。如果選擇npn BJT,則必須選擇PMOS晶體管,如果選擇pnp


BJT必須選擇NMOS晶體管。說明他們的主要特點,非常簡短的評論


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】根據您的選擇。


2.在同一圖表上繪制不同vBE值的iC與vCE,并估算所選BJT的ro


根據情節。


3.使用模擬裝置評估BJT的β。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】4.繪制iC與vBE的關系圖,并估算所選BJT給定vBE的gm和rπ。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】5.在同一圖表上繪制不同vGS值的iD與vDS,并估計所選變量的ro


基于圖的MOSFET。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】6.繪制iD與VG的關系圖,并估計所選MOSFET給定VG的gm。


任務B:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在本任務中,您必須使用任務A中選擇的晶體管,并遵守以下規定:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?您必須至少設計一次BJT電路,并且必須至少設計一次MOSFET


巡回賽。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?應使用振幅為40 mV(vsig)的理想正弦輸入電壓信號。


vsig必須有一個與之串聯的電阻器(Rsig)。在以下兩個子任務中,您應該


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】使用Rsig=100 kΩ,并且僅在其中一個子任務中使用Rsig=100Ω。你應該


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】簡要說明你的選擇。


?您只能使用設置在-6 V至+6 V之間任何值的兩個直流電源電壓。


你應該考慮負載電阻RL=60 KΩ。


?您可以使用任意數量、任意值的電阻器和電容器。你應該


適當偏置電路中的晶體管,交流耦合輸入和輸出電壓


信號。


?應在所有三個電路中使用源極或發射極退化電阻器。


?您的首要任務是最大化每個電路的增益。你應該清楚地展示你是如何做到的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】實現這一目標。


?您的另一個設計決策是確保低3dB轉角頻率不超過60


赫茲。如果你無法實現這個目標,請解釋原因。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?在每種情況下,清楚地展示您的設計,包括計算,簡要說明


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】您所做的設計選擇,包括適當的模擬結果和圖形,以表明


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】成功實現您的設計。評估低頻、中頻和高頻的設計。


1.設計并模擬共發射極或共源放大器電路。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】2.設計并模擬公共基極或公共門放大器電路。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】3.設計并模擬公共集電極或公共漏極放大器電路。


Deep-learning based numerical BSDE method for barrier options Bing Yu? , Xiaojing Xing? , Agus Sudjianto? April 15, 2019
時間: 2021-08-27 08:35:26

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation (BSDE). We can either solve the PDE to obtain option prices or solve its associated BSDE. Recently a deep learning technique has been applied to solve option prices using the BSDE approach. In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions. In this paper, we extend the deep-learning technique to solve a PDE with both terminal and boundary conditions. In particular, we will employ the technique to solve barrier options using Brownian motion bridges.

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】眾所周知,期權價格是某一偏微分方程的解


帶有終端條件(支付函數)的方程(PDE)。有


PDE解與a解之間的密切聯系


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】倒向隨機微分方程(BSDE)。我們可以解決


PDE獲取期權價格或解決其相關BSDE。最近


深度學習技術已被應用于解決期權價格使用


BSDE方法。在這種方法中,深度學習用于學習一些知識


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】確定性函數,用于求解帶終端的BSDE


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】條件在本文中,我們擴展了深度學習技術來解決


具有終端和邊界條件的偏微分方程。特別是,我們


將使用該技術使用布朗運動求解障礙選項


橋。

1 Introduction

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】A barrier option is a type of derivative where the payoff depends on whether

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】the underlying asset has breached a predetermined barrier price. For a simple

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】barrier case, an analytical pricing formula is available (see [1]). Because barrier

options have additional conditions built in, they tend to have cheaper premiums

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】than comparable options without barriers. Therefore, if a trader believes the

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】barrier is unlikely to be reached, they may prefer to buy a knock-out barrier

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】option for a lower premium. There are different methods to solve option prices,

ranging from an analytical solution, solving PDE numerically, and Monte Carlo

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】simulations. Recently, a different approach using machine learning has been

proposed.

Using machine learning to solve PDE was studied in [2]. In this work, a new

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】method was proposed for solving parabolic partial differential equations with

terminal conditions, which we will call the standard framework hereafter. In this

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?Corporate Model Risk, Wells Fargo, bing.yu@wellsfargo.com

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?Corporate Model Risk, Wells Fargo

?Corporate Model Risk, Wells Fargo

1

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】new method, the PDE is formulated as a stochastic control problem through

a Feymann-Kac formula. In this formulation, a connection between PDE for

option prices and BSDE is made. The option price is obtained by solving the

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】BSDE rather than solving PDE. The solution to the BSDE is represented by

two deterministic functions. One innovation (shown in [2]) is the use of a neural

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】network and deep-learning technique to learn these deterministic functions. The

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】mathematical foundation of this approach is based on a Kolmogorov-Arnold

representation theorem. This theorem states that any continuous function can

be approximated by a finite composition of continuous functions of a single

variable. Cybenko (see[3]) found that a feed-forward neural network is a natural

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】realization of the theorem and he provided a concrete implementation using a

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】sigmoid function.

In addition to [2], [4] extended the method to solve fully non-linear PDE and

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】second-order backward stochastic differential equation. Other works related to

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】this deep-learning method include [5] and [6]. In [5], a different way of simulating

the processes in the forward-backward stochastic differential equation (FBSDE)

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】is proposed. Rather than using a neural network to approximate the derivative

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】of a PDE solution, the network is used to directly approximate the PDE solution

and the derivative is calculated using automatic differentiation. A number of

different choices for building the neural network and learning structure and

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】two new types of structures are proposed in [6] . These problems are in the

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】framework of a PDE with some terminal conditions. These PDE can be solved

by an equivalent BSDE.

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】In the aforementioned standard framework, the PDE solved has no boundary conditions. There are some works on PDE with free boundary conditions.

In these works, a BSDE is replaced by a reflected BSDE (RBSDE). A penalty

term is added to the loss function to take into account the free boundary condition in order to solve the RBSDE. Again, machine learning can be used in

solving these problems. This approach is used in [7] to solve American options.

Bermudan Swaptions is solved by exercising the option at a boundary in [8]. In

our work, we consider barrier options. We treat boundary conditions of barrier

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】options differently. Rather than using RBSDE with a penalty function or exercise options at a boundary, we incorporated the boundary conditions as terminal

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】conditions. To our best knowledge, this approach has not previously been done.

In this paper, we organize as follows. In section 2, we present the standard

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】framework, which is designed for Cauchy problem. In section 3, we describe how

we extend the standard framework to handle barrier options, which corresponds

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】to a Cauchy-Dirichlet problem. In section 4, we present numerical considerations

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】and the results we obtained from our experiments. Finally, we make some

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】concluding remarks in section 5.

2

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】2 Basic method to solve BSDE by machine learning

We briefly introduced the deep-learning-based numerical BSDE algorithm proposed in [2]. We start from an FBSDE, which is first proposed in [9].

Xt = X0 +

? t

0

bs(Xs)ds +

? t

0

σs(Xs)dWs

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】Yt = h(XT ) + ? T

t

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】fs(Xs, Ys, Zs)ds ?

? T

t

ZsdWs

Here, {Ws}0<s<T is a Brownian motion and h(XT ) is the terminal condition.

The pair (Y, Z)0<t<T solves the BSDE. It is known that there exists a deterministic function u = u(t, x) such that Yt = u(t, Xt), Zt = ?u(t, Xt)σt(Xt) and

u(t, x) solves a quasi-linear PDE. For both the forward and backward process,

we can use Euler scheme to approximate:

Xti+1 ≈ Xti + bti

(Xti

)(ti+1 ? ti) + σti

(Xti

)(Wti+1 ? Wti

) (1)

Yti+1 ≈ Yti ? fti

(Xti

, Yti

, Zti

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】)(ti+1 ? ti) + Zti

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】(Wti+1 ? Wti

) (2)

Note that we have made the backward process to be forward; this is a commonly

used technique in treating FBSDEs. This set of equations has the following

interpretation on a given path: Xti

is the underlying price; Yti

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】is the option

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】price and Zti

is related to the delta at time ti

.

In the deep-learning-bas

1導言


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】障礙期權是一種衍生品,其收益取決于


標的資產已突破預定的壁壘價格。簡單來說


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在障礙情況下,可使用分析定價公式(見[1])。因為障礙


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】期權有附加條件,它們往往有更便宜的保費


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】比沒有障礙的可比選擇更有效。因此,如果交易者相信


障礙不太可能達到,他們可能更愿意購買淘汰障礙


選擇較低的保費。解決期權價格有不同的方法,


從解析解、數值求解偏微分方程到蒙特卡羅


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】模擬。最近,人們提出了一種使用機器學習的不同方法


提出。


文獻[2]研究了利用機器學習求解偏微分方程。在這項工作中,一個新的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】提出了一種求解拋物型偏微分方程的新方法


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】終端條件,我們將在下文中稱之為標準框架。在這個


?公司模型風險,富國銀行,必應。yu@wellsfargo.com


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?富國銀行公司模型風險部


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】——富國銀行公司模型風險部


1.


在新方法中,PDE被描述為一個隨機控制問題


費曼-卡克公式。在此公式中,PDE之間的連接用于


制定期權價格和BSDE。期權價格是通過求解


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】BSDE而不是解決PDE。BSDE的解決方案表示為


兩個確定性函數。一項創新(如[2]所示)是使用神經網絡


網絡和深度學習技術來學習這些確定性函數。這個


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】這種方法的數學基礎是基于Kolmogorov阿諾德的。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】表示定理。這個定理表明,任何連續函數都可以


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】可以用單個函數的連續函數的有限合成來近似


變量Cybenko(見[3])發現,前饋神經網絡是一種自然現象


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】實現了這個定理,他用一個


S形函數。


除了[2],[4]將該方法推廣到求解完全非線性的偏微分方程和偏微分方程


二階倒向隨機微分方程。其他有關


這種深度學習方法包括[5]和[6]。在[5]中,一種不同的模擬方法


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】正倒向隨機微分方程(FBSDE)中的過程


這是提議的。而不是使用神經網絡來近似導數


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】對于PDE解決方案,網絡用于直接近似PDE解決方案


導數的計算采用自動微分法。一些


構建神經網絡和學習結構的不同選擇


[6]中提出了兩種新型結構。這些問題都存在


具有某些終端條件的PDE框架。這些偏微分方程是可以解決的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】通過等效的BSDE。


在上述標準框架中,求解的PDE沒有邊界條件。有一些關于自由邊界條件的偏微分方程的工作。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在這些工作中,BSDE被反射BSDE(RBSDE)代替。懲罰


為了求解RBSDE,在損失函數中加入了一項,以考慮自由邊界條件。同樣,機器學習也可以用于


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】解決這些問題。[7]中使用了這種方法來解決美式期權。


百慕大互換期權通過在[8]中的邊界行使期權來解決。在里面


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】我們的工作,我們考慮障礙選項。我們處理勢壘的邊界條件


選擇不同。我們將邊界條件合并為終端條件,而不是在邊界處使用RBSDE和懲罰函數或行使選項


條件據我們所知,這種方法以前從未采用過。


在本文中,我們組織如下。在第2節中,我們介紹了標準


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】框架,它是為柯西問題而設計的。在第3節中,我們將介紹如何


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】我們擴展了標準框架來處理障礙選項,這與


一個柯西-狄里克萊問題。在第4節中,我們介紹了數值方面的考慮


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】以及我們從實驗中得到的結果。最后,我們制作一些


第5節的結束語。


2.


2通過機器學習解決BSDE的基本方法


我們簡要介紹了[2]中提出的基于深度學習的數值BSDE算法。我們從[9]中首次提出的FBSDE開始。


Xt=X0+


?t


0


bs(Xs)ds+


?t


0


σs(Xs)dWs


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】Yt=h(XT)+?T


T


fs(Xs,Ys,Zs)ds?


?T


T


ZsdWs


這里,{Ws}0<s<T是布朗運動,h(XT)是終端條件。


對(Y,Z)0<t<t求解BSDE。已知存在一個確定性函數u=u(t,x),使得Yt=u(t,Xt),Zt=?u(t,Xt)σt(Xt)和


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】u(t,x)解一個擬線性偏微分方程。無論是向前還是向后,


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】我們可以使用Euler格式來近似:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】Xti+1≈ Xti+bti


(Xti)


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】)(ti+1)? ti)+σti


(Xti)


)(Wti+1)? Wti


15-213, Fall 20xx The Attack Lab: Understanding Buffer Overflow Bugs Assigned: Tue, Sept. 29 Due: Thu, Oct. 8, 11:59PM EDT Last Possible Time to Turn in: Sun, Oct. 11, 11:59PM EDT
時間: 2021-08-26 10:08:29

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】1 Introduction This assignment involves generating a total of five attacks on two programs having different security vulnerabilities. Outcomes you will gain from this lab include: ? You will learn different ways that attackers can exploit security vulnerabilities when programs do not safeguard themselves well enough against buffer overflows. ? Through this, you will get a better understanding of how to write programs that are more secure, as well as some of the features provided by compilers and operating systems to make programs less vulnerable. ? You will gain a deeper understanding of the stack and parameter-passing mechanisms of x86-64 machine code. ? You will gain a deeper understanding of how x86-64 instructions are encoded. ? You will gain more experience with debugging tools such as GDB and OBJDUMP. Note: In this lab, you will gain firsthand experience with methods used to exploit security weaknesses in operating systems and network servers. Our purpose is to help you learn about the runtime operation of programs and to understand the nature of these security weaknesses so that you can avoid them when you write system code. We do not condone the use of any other form of attack to gain unauthorized access to any system resources. You will want to study Sections 3.10.3 and 3.10.4 of the CS:APP3e book as reference material for this lab

1導言


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】此任務涉及對具有不同安全漏洞的兩個程序總共生成五次攻擊。您將從本實驗室獲得的成果包括:


?您將了解攻擊者在程序不存在漏洞時利用安全漏洞的不同方式


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】充分保護自己,防止緩沖區溢出。


?通過本課程,您將更好地了解如何編寫更安全的程序,如


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】以及編譯器和操作系統提供的一些使程序更少的功能


脆弱的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?您將更深入地了解x86-64的堆棧和參數傳遞機制


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】機器代碼。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?您將更深入地了解x86-64指令的編碼方式。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?您將獲得更多使用GDB和OBJDUMP等調試工具的經驗。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】注意:在本實驗室中,您將獲得利用安全漏洞的方法的第一手經驗


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】操作系統和網絡服務器。我們的目的是幫助您了解


并了解這些安全弱點的性質,以便您在


編寫系統代碼。我們不允許使用任何其他形式的攻擊來獲取未經授權的訪問權限


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】任何系統資源。


您需要學習CS:APP3e手冊的第3.10.3節和第3.10.4節,作為本實驗室的參考資料2 Logistics As usual, this is an individual project. You will generate attacks for target programs that are custom generated for you. 2.1 Getting Files You can obtain your files by pointing your Web browser at: http://$Attacklab::SERVER_NAME:15513/ INSTRUCTOR: $Attacklab::SERVER_NAME is the machine that runs the attacklab servers. You define it in attacklab/Attacklab.pm and in attacklab/src/build/driverhdrs.h The server will build your files and return them to your browser in a tar file called targetk.tar, where k is the unique number of your target programs. Note: It takes a few seconds to build and download your target, so please be patient. Save the targetk.tar file in a (protected) Linux directory in which you plan to do your work. Then give the command: tar -xvf targetk.tar. This will extract a directory targetk containing the files described below. You should only download one set of files. If for some reason you download multiple targets, choose one target to work on and delete the rest. Warning: If you expand your targetk.tar on a PC, by using a utility such as Winzip, or letting your browser do the extraction, you’ll risk resetting permission bits on the executable files. The files in targetk include: README.txt: A file describing the contents of the directory ctarget: An executable program vulnerable to code-injection attacks rtarget: An executable program vulnerable to return-oriented-programming attacks cookie.txt: An 8-digit hex code that you will use as a unique identifier in your attacks. farm.c: The source code of your target’s “gadget farm,” which you will use in generating return-oriented programming attacks. hex2raw: A utility to generate attack strings. In the following instructions, we will assume that you have copied the files to a protected local directory, and that you are executing the programs in that local directory. 2 2.2 Important Points Here is a summary of some important rules regarding valid solutions for this lab. These points will not make much sense when you read this document for the first time. They are presented here as a central reference of rules once you get started. ? You must do the assignment on a machine that is similar to the one that generated your targets. ? Your solutions may not use attacks to circumvent the validation code in the programs. Specifically, any address you incorporate into an attack string for use by a ret instruction should be to one of the following destinations: – The addresses for functions touch1, touch2, or touch3. – The address of your injected code – The address of one of your gadgets from the gadget farm. ? You may only construct gadgets from file rtarget with addresses ranging between those for functions start_farm and end_farm. 3 Target Programs Both CTARGET and RTARGET read strings from standard input. They do so with the function getbuf defined below: 1 unsigned getbuf() 2 { 3 char buf[BUFFER_SIZE]; 4 Gets(buf); 5 return 1; 6 } The function Gets is similar to the standard library function gets—it reads a string from standard input (terminated by ‘\n’ or end-of-file) and stores it (along with a null terminator) at the specified destination. In this code, you can see that the destination is an array buf, declared as having BUFFER_SIZE bytes. At the time your targets were generated, BUFFER_SIZE was a compile-time constant specific to your version of the programs. Functions Gets() and gets() have no way to determine whether their destination buffers are large enough to store the string they read. They simply copy sequences of bytes, possibly overrunning the bounds of the storage allocated at the destinations. If the string typed by the user and read by getbuf is sufficiently short, it is clear that getbuf will return 1, as shown by the following execution examples: unix> ./ctarget 3 Cookie: 0x1a7dd803 Type string: Keep it short! No exploit. Getbuf returned 0x1 Normal return Typically an error occurs if you type a long string: unix> ./ctarget Cookie: 0x1a7dd803 Type string: This is not a very interesting string, but it has the property ... Ouch!: You caused a segmentation fault! Better luck next time (Note that the value of the cookie shown will differ from yours.) Program RTARGET will have the same behavior. As the error message indicates, overrunning the buffer typically causes the program state to be corrupted, leading to a memory access error. Your task is to be more clever with the strings you feed CTARGET and RTARGET so that they do more interesting things. These are called exploit strings. Both CTARGET and RTARGET take several different command line arguments: -h: Print list of possible command line arguments -q: Don’t send results to the grading server -i FILE: Supply input from a file, rather than from standard input Your exploit strings will typically contain byte values that do not correspond to the ASCII values for printing characters. The program HEX2RAW will enable you to generate these raw strings. See Appendix A for more information on how to use HEX2RAW. Important points: ? Your exploit string must not contain byte value 0x0a at any intermediate position, since this is the ASCII code for newline (‘\n’). When Gets encounters this byte, it will assume you intended to terminate the string. ? HEX2RAW expects two-digit hex values separated by one or more white spaces. So if you want to create a byte with a hex value of 0, you need to write it as 00. To create the word 0xdeadbeef you should pass “ef be ad de” to HEX2RAW (note the reversal required for little-endian byte ordering). When you have correctly solved one of the levels, your target program will automatically send a notification to the grading server. For example: unix> ./hex2raw < ctarget.l2.txt | ./ctarget Cookie: 0x1a7dd803 Type string:Touch2!: You called touch2(0x1a7dd803) Valid solution for level 2 with target ctarget PASSED: Sent exploit string to server to be validated. NICE JOB! 4 Phase Program Level Method Function Points 1 CTARGET 1 CI touch1 10 2 CTARGET 2 CI touch2 25 3 CTARGET 3 CI touch3 25 4 RTARGET 2 ROP touch2 35 5 RTARGET 3 ROP touch3 5 CI: Code injection ROP: Return-oriented programming Figure 1: Summary of attack lab phases The server will test your exploit string to make sure it really works, and it will update the Attacklab scoreboard page indicating that your userid (listed by your target number for anonymity) has completed this phase. You can view the scoreboard by pointing your Web browser at http://$Attacklab::SERVER_NAME:15513/scoreboard Unlike the Bomb Lab, there is no penalty for making mistakes in this lab. Feel free to fire away at CTARGET and RTARGET with any strings you like. IMPORTANT NOTE: You can work on your solution on any Linux machine, but in order to submit your solution, you will need to be running on one of the following machines: INSTRUCTOR: Insert the list of the legal domain names that you established in buflab/src/config.c. Figure 1 summarizes the five phases of the lab. As can be seen, the first three involve code-injection (CI) attacks on CTARGET, while the last two involve return-oriented-programming (ROP) attacks on RTARGET. 4 Part I: Code Injection Attacks For the first three phases, your exploit strings will attack CTARGET. This program is set up in a way that the stack positions will be consistent from one run to the next and so that data on the stack can be treated as executable code. These features make the program vulnerable to attacks where the exploit strings contain the byte encodings of executable code. 4.1 Level 1 For Phase 1, you will not inject new code. Instead, your exploit string will redirect the program to execute an existing procedure. Function getbuf is called within CTARGET by a function test having the following C code:

2物流


像往常一樣,這是一個單獨的項目。您將為自定義生成的目標程序生成攻擊。


2.1獲取文件


您可以通過將Web瀏覽器指向以下位置來獲取文件:


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】http://$Attacklab::服務器名稱:15513/


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】講師:$Attacklab::SERVER\u NAME是運行


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】攻擊實驗室服務器。您可以在attacklab/attacklab.pm和中定義它


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】attacklab/src/build/driverhdrs.h


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】服務器將生成您的文件,并將它們返回到名為targetk.tar的tar文件中的瀏覽器,其中


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】k是目標計劃的唯一編號。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】注意:構建和下載目標需要幾秒鐘,所以請耐心等待。


將targetk.tar文件保存在您計劃在其中執行工作的(受保護的)Linux目錄中。然后給


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】命令:tar-xvf targetk.tar。這將提取包含這些文件的目錄targetk


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】如下所述。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】您應該只下載一組文件。如果出于某種原因下載了多個目標,請選擇一個


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】要處理的目標并刪除其余的。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】警告:如果您在PC上擴展targetk.tar,請使用Winzip等實用程序,或讓


瀏覽器執行提取操作時,可能會重置可執行文件上的權限位。


targetk中的文件包括:


README.txt:描述目錄內容的文件


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】ctarget:易受代碼注入攻擊的可執行程序


rtarget:易受面向返回編程攻擊的可執行程序


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】cookie.txt:一個8位十六進制代碼,在攻擊中用作唯一標識符。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】c:目標的“gadget farm”的源代碼,用于生成面向返回的


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】編程攻擊。


hex2raw:用于生成攻擊字符串的實用程序。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】在以下說明中,我們假設您已將文件復制到受保護的本地目錄,


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】并且您正在本地目錄中執行程序。


2.


2.2要點


以下是關于本實驗室有效解決方案的一些重要規則的摘要。這些要點不會說明問題


當您第一次閱讀此文檔時,您會覺得非常有意義。它們在這里作為中心參考


一旦你開始了,你就會有很多規則。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】?您必須在與生成目標的機器類似的機器上執行任務。


?您的解決方案可能不會使用攻擊繞過程序中的驗證代碼。明確地


任何合并到攻擊字符串中供ret指令使用的地址都應該是


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】以下目的地:


–功能touch1、touch2或touch3的地址。


–注入代碼的地址


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】–小工具場中一個小工具的地址。


?您只能從文件rtarget構建小工具,其地址介于函數start_farm和end_farm的地址之間。


3個目標項目


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】CTARGET和RTARGET都從標準輸入讀取字符串。它們是通過函數getbuf實現的


定義如下:


1個未簽名的getbuf()


2 {


3字符buf[緩沖區大小];


4個(buf);


5返回1;


6 }


函數get類似于標準庫函數get,它


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