北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】

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

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.

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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


確定性函數,用于求解帶終端的BSDE


條件在本文中,我們擴展了深度學習技術來解決


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】具有終端和邊界條件的偏微分方程。特別是,我們


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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

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

barrier is unlikely to be reached, they may prefer to buy a knock-out barrier

option for a lower premium. There are different methods to solve option prices,

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】ranging from an analytical solution, solving PDE numerically, and Monte Carlo

simulations. Recently, a different approach using machine learning has been

proposed.

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】Using machine learning to solve PDE was studied in [2]. In this work, a new

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

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

1

new method, the PDE is formulated as a stochastic control problem through

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

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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

network and deep-learning technique to learn these deterministic functions. The

mathematical foundation of this approach is based on a Kolmogorov-Arnold

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】representation theorem. This theorem states that any continuous function can

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

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】variable. Cybenko (see[3]) found that a feed-forward neural network is a natural

realization of the theorem and he provided a concrete implementation using a

sigmoid function.

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】In addition to [2], [4] extended the method to solve fully non-linear PDE and

second-order backward stochastic differential equation. Other works related to

this deep-learning method include [5] and [6]. In [5], a different way of simulating

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】the processes in the forward-backward stochastic differential equation (FBSDE)

is proposed. Rather than using a neural network to approximate the derivative

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

two new types of structures are proposed in [6] . These problems are in the

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

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】our work, we consider barrier options. We treat boundary conditions of barrier

options differently. Rather than using RBSDE with a penalty function or exercise options at a boundary, we incorporated the boundary conditions as terminal

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

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

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

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】u(t, x) solves a quasi-linear PDE. For both the forward and backward process,

we can use Euler scheme to approximate:

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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

(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

is the option

price and Zti

北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】障礙不太可能達到,他們可能更愿意購買淘汰障礙


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


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


提出了一種求解拋物型偏微分方程的新方法


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


?富國銀行公司模型風險部


——富國銀行公司模型風險部


1.


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


這種方法的數學基礎是基于Kolmogorov阿諾德的。


表示定理。這個定理表明,任何連續函數都可以


可以用單個函數的連續函數的有限合成來近似


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】變量Cybenko(見[3])發現,前饋神經網絡是一種自然現象


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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】二階倒向隨機微分方程。其他有關


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


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


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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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。


在這些工作中,BSDE被反射BSDE(RBSDE)代替。懲罰


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


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


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


以及我們從實驗中得到的結果。最后,我們制作一些


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】第5節的結束語。


2.


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】2通過機器學習解決BSDE的基本方法


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


Yt=h(XT)+?T


T


fs(Xs,Ys,Zs)ds?


?T


T


ZsdWs


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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,


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)


)(ti+1)? ti)+σti


(Xti)


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】)(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


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


機器代碼。


?您將更深入地了解x86-64指令的編碼方式。


?您將獲得更多使用GDB和OBJDUMP等調試工具的經驗。


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


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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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


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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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/


講師:$Attacklab::SERVER\u NAME是運行


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


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。


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


命令:tar-xvf targetk.tar。這將提取包含這些文件的目錄targetk


如下所述。


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。


警告:如果您在PC上擴展targetk.tar,請使用Winzip等實用程序,或讓


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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。


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】targetk中的文件包括:


README.txt:描述目錄內容的文件


ctarget:易受代碼注入攻擊的可執行程序


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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。


c:目標的“gadget farm”的源代碼,用于生成面向返回的


編程攻擊。


hex2raw:用于生成攻擊字符串的實用程序。


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


北美代写,Homework代写,Essay代寫-准时✔️高质✔最【靠谱】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。


?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


以下目的地:


–功能touch1、touch2或touch3的地址。


–注入代碼的地址


–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


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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