
This research aims to describe and analyze phishing emails. The problem of phishing, types of message content of phishing emails, and the basic techniques of phishing email attacks are explained by way of introduction. The study also includes a review of the relevant literature on Web of Science and analyzes articles that deal with the threat of phishing attacks and defense against them. Data collected within a time interval of two months from two email accounts of one of the authors of the study was used for the analysis of 200 email messages. Data has been resented in tabular form, to allow further statistical processing using functions such as sum, average and frequency analysis. The core part of the study involved the classification and segmentation of emails according to the main goals of the sent message. The text analytical software Tovek, was used for the analysis, Contribution of the manuscript is in the understanding of phishing emails and extending the knowledge base in education and training in phishing email defense. The discussion compares the results of this research with those of the studies mentioned in the "Introduction" and "Literature review" sections. Furthermore, the emerging problems and limitations of the use of text analytical software are described, and finally the issue is devoted to problems with obtaining personal data from recipients' emails. The "Conclusion" section summarizes the contributions of this research.
Citation: Ladislav Burita, Petr Matoulek, Kamil Halouzka, Pavel Kozak. Analysis of phishing emails[J]. AIMS Electronics and Electrical Engineering, 2021, 5(1): 93-116. doi: 10.3934/electreng.2021006
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This research aims to describe and analyze phishing emails. The problem of phishing, types of message content of phishing emails, and the basic techniques of phishing email attacks are explained by way of introduction. The study also includes a review of the relevant literature on Web of Science and analyzes articles that deal with the threat of phishing attacks and defense against them. Data collected within a time interval of two months from two email accounts of one of the authors of the study was used for the analysis of 200 email messages. Data has been resented in tabular form, to allow further statistical processing using functions such as sum, average and frequency analysis. The core part of the study involved the classification and segmentation of emails according to the main goals of the sent message. The text analytical software Tovek, was used for the analysis, Contribution of the manuscript is in the understanding of phishing emails and extending the knowledge base in education and training in phishing email defense. The discussion compares the results of this research with those of the studies mentioned in the "Introduction" and "Literature review" sections. Furthermore, the emerging problems and limitations of the use of text analytical software are described, and finally the issue is devoted to problems with obtaining personal data from recipients' emails. The "Conclusion" section summarizes the contributions of this research.
Modeling derivative products in mathematical finance usually starts with a system of stochastic differential equation that correspond to state variables like stock, interest rate and volatility. For financial analysts, economic auditors and risk managers in financial organizations, securities analysis is very important, thus in last decades they have tried to design new models that help to change markets react and solve some problems in the market. Modeling challenges has created a common link between applied mathematicians and financial researchers as the production of knowledge in this area [4,15].
As an important model in the original Black–Scholes option valuation theory the volatility and the interest rate are both assumed constant. A popular expansion of the Black–Scholes model has been proposed by Heston. This expansion allows also for correlation between the volatility and the asset price, so that one can capture skewness effects [16]. In addition to the volatility the values of many practically important securities are sensitive to interest rates, necessitating the construction of interest rate models [17].
Consider an agent who at each time has a portfolio valued at X(t). This portfolio invests in a money market account paying a constant rate of interest r, and in a stock modeled by the geometric Brownian motion,
dS(t)=αS(t)dt+δS(t)dW(t), | (1.1) |
Suppose at each time t, the investor holds Δ(t) shares of stock. The position Δ(t) can be random but must be adapted to the filtration associated with the Brownian motion W(t),t≥0. The remainder of the portfolio value, X(t)−Δ(t)S(t), is invested in the money market account [12].
In addition to, we consider the following model which presents the system of three SDEs which they have composed from different assets called hybrid model. The asset price model in following can be viewed as an extension of the popular Heston stochastic volatility model where the interest rate is not constant but also follows a stochastic process, described here by the Hull–White model.
dSτ=RτSτdτ+Sτ√VτdW1τ,S(0)>0, | (1.2) |
dVτ=k(η−Vτ)dτ+δ1√VτdW2τ,V(0)>0, |
dRτ=a(b(τ)−Rτ)dτ+δ2√RτdW3τ,R(0)>0, |
In this model for 0<τ≤T with T>0 the given maturity time of an option. The stocks process Sτ,Vτ, and Rτ denote random variables that represent the asset price, its variance, and the interest rate, at time τrespectively. The variance process Vτ was proposed by Heston and interest rate process Rτ, by Cox, Ingersoll and Ross in Eq.(1.2). Parameters k,η,δ1,δ2, and a, are given positive real numbers and dW1τ,dW2τ,dW3τ are Brownian motions with given correlation factors ρ1,ρ2,ρ3∈[−1,1]. Also b, is a given deterministic positive function of time which can be chosen such that to match the current term structure of interest rates [12,13].
The introduction of Legendre wavelet method for the variational problems by Razzaghi and Yousefi in 2000 and 2001 [3,11,14], several works applying this method were born. The Legendre wavelets method (LWM) is transformed a boundary value problem (BVP) into a system of algebraic equations [14]. The unknown parameter of this system is the vector that its components are the decomposition coefficients of the BVP solution into Legendre wavelets basis. In this paper we apply the multi- Legendre wavelet method to solve B-S and HCIR partial differential equations.
This paper is organized as follows: In section 2, PDE models of Black-Scholes equation and Heston-Cox-Ingersoll-Ross equation have derived from SDE models, Section 3 has described properties and formulation of Legendre wavelet and Multi-dimensional Legendre Wavelet, Section4, metodology of multi-dimensional LWM is applied on PDEs that use in section 2. In section 5, numerical results and figures are presented and section 6, Conclusion is used to review.
In his section we discuss about the B-S PDE model and HCIR PDE model that they have derived from SDE models [1,2,19].
Theorem 2.1. (Ito-Doeblin formula for Brownian motion). Let f(t,x) be a function for which the partial derivatives ft(t,x),fx(t,x) and fxx(t,x) are defined and continues and let W(t) be a Brownian motion. Then for every T≥0,
f(t,W(t))=f(0,W(0))+∫T0ft(t,W(t))dt | (2.1) |
+∫T0fx(t,W(t))dt+∫T0fxx(t,W(t))dt, |
Proof: see [6].
Remark 2.1: In the Ito- Doeblin formula we can write in differential form [6],
df(t,W(t))=ft(t,W(t))+ft(t,W(t))dW(t)+12ft(t,W(t))dW(t)dW(t),+ftx(t,W(t))dtdW(t)+12fxx(t,W(t))dtdt, | (2.2) |
But
dW(t)dW(t)=d(t),d(t)dW(t)=dW(t)d(t)=0,d(t)d(t)=0, | (2.3) |
And the Ito- Doeblin formula in differential form simplifies to
df(t,W(t))=ft(t,W(t))dt+fx(t,W(t))dW(t)+12fxxdt. | (2.4) |
Definition 2.1: let W(t),t≥0 be a Brownian motion and let F(t),t≥0 be an associated filtration. An Ito process as the form following
X(t)=X(0)+∫t0△(u)dW(u)+∫t0θ(u)d(u), | (2.5) |
Where X(0) is nonrandom and △(u) and θ(u) are adapted stochastic process.
Lemma 2.1: the quadratic variation of the Ito process Eq.(2.5) is
[X,X](t)=∫t0△2(u)du. | (2.6) |
Proof: see [6].
Definition 2.2: let X(t),t≥0 be an Ito process as described in Definition 2.1 and let Γ(t),t≥0 be an adapted process. We define the integral with respect to an Ito process,
∫t0Γ(u)dX(u)=∫t0Γ(u)△(u)dW(u)+∫t0Γ(u)θ(u)du. | (2.7) |
Theorem 2.2. (Ito-Doeblin formula for an Ito process). Let X(t),t≥0 be an Ito process as described in Definition 2.1 and let f(t,x) be a function for which the partial derivatives ft(t,x),fx(t,x) and fxx(t,x) are defined and continues. Then for every T≥0,
f(T,X(T))=f(0,X(0))+∫0(T)ft(t,X(t))dt | (2.8) |
+∫T0fx(t,X(t))dX(t)+12∫T0fxx(t,X(t))d[X,X](t)= |
f(0,X(0))+∫T0ft(t,X(t))dt+∫T0fx(t,X(t))△(t)dW(t)dt |
+∫T0fx(t,X(t))θdt+12∫T0fxx(t,X(t))△(t)2(t) |
We may rewrite Eq.(2.8),
df(t,x(t))=ft(t,X(t))dt+fx(t,X(t))dX(t)+12fxx(t,X(t))dX(t)dX(t). | (2.9) |
proof: See [6].
A SDE has defined as te following
dX(u)=β(u,X(u))du+γ(u,X(u))dW(u). | (2.10) |
The functions β(u,X(u)) and γ(u,X(u)) called drift and diffusion respectively. Where t≥0 final goal in these problems is to find a stochastic procesX(T) which is governed by
X(t)=x, | (2.11) |
X(t)=X(t)+∫Ttβ(u,X(u))du+∫Ttγ(u,X(u))dW(u), | (2.12) |
Under conditions of the functions β(u,x) and γ(u,x) there exists a unique process X(T),T≥t Satisfying Eq.(2.11) and Eq.(2.12) and generally this is a SDE form of following
dX(u)=(a(u)+b(u)X(u))du+(γ(u)+δ(u)X(u))dW(u), | (2.13) |
Where a(u),b(u),δ(u) and γ(u) are non-random functions of time and we permit on the right- hand side of Eq.(2.10) is the randomness inherent in the solution X(u) and in the driving Brownian motion W(u).Then the stochastic differential equation for geometric Brownian motion is
dS(u)=αS(u)du+γS(u)dW(u), | (2.14) |
The rate of change dSS, can be decomposed in to two parts in Eq.(2.14): One is deterministic, the other one is random.
The deterministic part can be modeled by dSS=μdt, where μ is a measure of the growth rate of the asset and random part modeled by a Brownian motion δdW(t) that δ is the variance of the return and is called the volatility. The overall asset price model is then given by
dSS=μdt+γdW(t), | (2.15) |
Eq.(2.15), is a SDE and it can be expressed as follows
dS(t)=μ(S(t))dt+δ(S(t))dWs(t). | (2.16) |
An important lemma for finding their solution is the following Ito lemma.
Lemma 2.2: Suppose S(t) satisfies the stochastic differential Eq.(2.16), and u(S,t) is a smooth function. Then u(S(t),t) satisfies the following stochastic differential equation [8].
du=(ut+μus+12δ2uss)dt+δusdWs. | (2.17) |
By expanding Eq.(2.17) with applying different variables in Ito lemma to earn B-S PDE and HCIR PDE equations, as following Black-Scholes PDE equation [1].
Option transaction contract is divided into different types which the most important types are call option and put option. call option contract means that buyer have to buy the certain asset with given price in certain time, as the same way the put option contract leads to sale of asset with given price and certain time by seller. Maturity validity includes American option and European option [5]. Where W(t), is a standard Brownian motion. We also assume that interest rate are constant so that one unit of currency invested in the cash account at time 0, will be worth
B(t)=exp(rt), |
at time t. We will value of call option or put option at time t. By Ito Lemma we know that,
du(S,t)=(μS(t)∂u∂S+∂u∂t+12δ2S2(t)∂2u∂S2)dt+δS(t)∂u∂SdW(t), | (2.18) |
Let us now consider a self-financing trading strategy where at each time t, we hold units xt, of the cash account and yt, units of the stock. Then pt, value of this strategy satisfies,
pt=xtBt+ytSt, | (2.19) |
We will choose xt and yt in such a way that the strategy replicates the value of the option the self-financing assumption implies that
dpt=xtdBt+ytdSt, | (2.20) |
=rxtBtdt+yt(μStdt+δStdWt) | (2.21) |
=(rxtBtdt+ytμSt)dt+ytδStdWt, |
Note that Eq.(2.20) is consistent with our earlier definition of financing. In particular gains or losses on the portfolio are due entirely to gain or losses in the underlying securities i.e. the cash account and stock and not due to change in holding xt, and yt.
Returning to our derivation, we can equate times in Eq.(2.18), to obtain
yt=∂u∂S, | (2.22) |
rxtBt=∂u∂S+12δ2S2∂2(u)∂S2, |
If we set
C0=P0, |
that C and P are call option and put option in finance, the interval value of strategy, then it must be the case that
Ct=Pt, |
since call option or put option have the same dynamics this is true by construction after we equate terms in Eq.(2.19), with the corresponding terms in Eq.(2.22).
Substituting Eq.(2.22) into Eq.(2.19), we obtain the Black-Scholes PDE
rSt∂u∂S+∂u∂t+12δ2S2∂2(u)∂S2−ru=0. | (2.23) |
In order to solve Eq.(2.23), boundary conditions must be provided in the case of call option or put option.
By applying system of Eq.(1.1) in Eq.(2.17), for construction of PDE model we need part of deterministic section of a stochastic differential equation model as,
du(S,v,r,t)=ut(S,v,r,t)dt+us(S,v,r,t)d(S(t))+ur(S,v,r,t)d(r(t))+uv(S,v,r,t)d(v(t))+12uss(S,v,r,t)d(S(t))d(S(t))+12urr(S,v,r,t)d(r(t))d(r(t))+12uvv(S,v,r,t)d(v(t))d(v(t))+usv(S,v,r,t)d(S(t))d(v(t))+usr(S,v,r,t)d(S(t))d(r(t))+uvr(S,v,r,t)d(v(t))d(r(t)). | (2.24) |
Heston-Cox-Ingersoll-Ross PDE equation [6,7,8,9],
dudt=rSdudS+k(η−v)dudv+a(b(t−T)−r)dudr+12S2v∂2u∂S2+12δ12v∂2u∂v2 | (2.25) |
+12δ22r∂2u∂r2+ρ12δ1Sv∂2u∂S∂v+ρ13S√vr∂2u∂S∂r+ρ23δ1δ2√vr∂2u∂v∂r−ru. |
By Eq.(2.25) we are obtained PDE model of the HCIR equation.
In this study Legendre wavelet well-addressed in [5,10,18], are employed.The wavelet basis is constructed from a single function, which is called the mother wavelet. These basis functions are called wavelets and they are an orthonormal set. One of the most important wavelets is Legendre wavelets. The Legendre wavelets are obtained from Legendre polynomials. In the past decade special attention has been given to applications of wavelets. The main characteristic of Legendre wavelet is that it reduces to a system of algebraic equation.
The function ψ(x)∈L2(R) is a mother wavelet and ψ(x)uv=|u|−12ψ(x−vu) which u,v∈R and u≠0 is a family of continuous wavelets. If we choose the dilation parameter u=a−n and the translation parameterv=ma−nb where a>1,b>0,n and m are positive integer, we have the discrete orthogonal wavelets set.
{ψ(x)n,m=|a|n2ψ(anx−mb):m,n∈Z} |
The Legendre wavelet is constructed from Legendre function [4,15]. One dimension Legendre wavelets over the interval [0,1] are defined as,
ψm,n(t)=√(m+12)2k2Pm(2kx−2n+1),ˆn−12k−10,o.w | (3.1) |
With n=1,2...,2k−1,m=0,1,2,...,M−1. In Eq.(3.1) Pm are ordinary Legendre functions of order defined over the interval [-1, 1]. Legendre wavelet is an orthonormal set as,
∫10ψ(x)n,mψ(x)n′,m′dx=δn,n′δm,m′, | (3.2) |
The function f(x)∈L2([0,1]×[0,1]), may be expanded as the following,
f(x)≅2k−1∑n=1M−1∑m=0cn,mψ(x)n,m, | (3.3) |
Now we defined four dimensions Legendre wavelets in L4([0,1]×[0,1]×[0,1]×[0,1]), as the form
ψn1,2,3,4,m1,2,3,4(x)={Am1,2,3,4(4∏i=1Pmi(2kixi−2ni+1)),ni−12ki−1≤xi≤ni2ni−1,i=1,..,40,o.w | (3.4) |
that
Am1,2,3,4=√4∏i=1(mi+12)24∑i=1ki2,mi=0,1,...,Mi−1,ni=1,2,...,2k1−1,i=1,..,4 |
Four dimensions Legendre wavelets are orthonormal set over [0,1]×[0,1]×[0,1]×[0,1].
∫10∫10∫10∫10ψn1,2,3,4,m1,2,3,4(x,y,z,q)ψn′1,2,3,4,m′1,2,3,4(x,y,z,q)dxdydzdq= | (3.5) |
δn1,n′1,δn2,n′2,δn3,n′3,δn4,n′4,δm1,m′1,δm2,m′2,δm3,m′3,δm4,m′4. |
The function u(x,y,z,q)∈L4([0,1]×[0,1]×[0,1]×[0,1]), can be expanded as fallows
u(x,y,z,q)=X(x)Y(y)Z(z)Q(q)≅4∏i=1(2ki−1∑ni=12Mi−1∑mi=1cni,miψni,mi(x,y,z,q)). | (3.6) |
Where
cn1,2,3,4,m1,2,3,4=∫10∫10∫10∫10X(x)Y(y)Z(z)Q(q)ψn1,2,3,4,m1,2,3,4dxdydzdq. |
The truncated version of Eq.(3.6), can be expressed as the following,
u(x,y,z,q)=CTΨ(x,y,z,q), | (3.7) |
Where C and Ψ(x,y,z,q), are coefficients matrix and wavelets vector matrix, respectively. The dimensions of those are {∏4i=1(2ki−1Mi)}×1, and are given as the form
C=[c10101010,...,c1010101M1−1,c10101020,...,c1010102M2−1,c10101030,...,c1010103M3−1, |
c1010102k1−10,...,c1010102k1−1M1−1,...,c1110101M1−1,...,c1120,...,c112M1−1, |
...,c2k1−1M1−12k2−102k3−102k4−10,...,c2k1−1M1−12k2−1M2−12k3−1M3−12k4−1M4−1,...]T, |
and also
Ψ=[ψ10101010,...,ψ1010101M1−1,ψ10101020,...,ψ1010102M2−1,ψ10101030,...,ψ1010103M3−1, |
ψ1010102k1−10,...,ψ1010102k1−1M1−1,...,ψ1110101M1−1,...,ψ1120,...,ψ112M1−1, |
...,ψ2k1−1M1−12k2−102k3−102k4−10,...,ψ2k1−1M1−12k2−1M2−12k3−1M3−12k4−1M4−1,...]T. |
The integration of the product of four Legendre wavelet function vectors is obtained as
∫10∫10∫10∫10Ψ(x,y,z,q)ΨT(x,y,z,q)=I. | (3.8) |
Where I, is diagonal unit matrix.(All of procedures in section 3, are established)
Lemma 3.1: if h∈C([0,T],L2[0,1]) then the functions Cn,m(t) are continuous on [0,1] [13].
Lemma 3.2: if h∈C1((0,T),L2[0,1]) then the function Cn,m(t) coefficient belong to C1(0,T), furthermore if ∂h∂t∈L2((0,T),L2[0,1]) then,
dCn,m(t)dt=∫10∂h(t,x)∂tψn,m(x)dx. | (3.9) |
Proof: see [13].
The integration matrix for one variable x, define as follows
∫x0Ψ(x′,y,z,q)dx′=PxΨ(x,y,z,q). | (3.10) |
In which
Px=1M12k1[ LFF⋯F0LF⋯F⋮⋮⋱⋱⋮000⋯L], |
Px, is a 2k1−12k2−12k3−12k4−1M1M2M3M4×2k1−12k2−12k3−12k4−1M1M2M3M4 matrix and L,F and O, are 2k1−1×M1×2k2−1×M2×2k3−1×M3×2k4−1×M4 matrices [14]. (In case of two variables Eq.(3.10), is established clearly).
Theorem 3.1. A function f(x,y,z,l)∈L4([0,1)×[0,1)×[0,1)×[0,1)) is with bounded second derivative say |f′′(x,y,z,l)≤M, can be expanded as an infinite sum of Legendre wavelets and the series converges uniformly to the function f(x,y,z,l) that is [15],
f(x,y,z,l)=∞∑n1=1∞∑m1=0∞∑n2=1∞∑m2=0∞∑n3=1∞∑m3=0∞∑n4=1∞∑m4=0cn1,..,4,m1,..,4ψn1,..,4,m1,..,4(x,y,z,l), | (3.11) |
Proof:
we have proved this theorem for multi-dimension states as following,
cn1,..,4,m1,..,4=∫10∫10∫10∫10f(x,y,z,l)dxdydzdl= |
∫^n1+12k^n1−12k∫^n2+12k^n2−12k∫^n3+12k^n3−12k∫^n4+12k^n4−12kf(x,y,z,l)(2m1+12)12(2m2+12)12(2m3+12)12(2m4+12)12 |
2k1+k2+k3+k42P(2k1x−ˆn)P(2k2x−ˆn)P(2k3x−ˆn)P(2k4x−ˆn)dxdydzdl. |
Now let,
2k1x−ˆn=t1,2k2x−ˆn=t2,2k3x−ˆn=t3,2k4x−ˆn=t4, |
then,
dx=12k1dt1,dy=12k2dt2,dz=12k3dt3,dl=12k4dt4. |
cn1,..,4,m1,..,4=∫1−1∫1−1∫1−1∫1−1f(^n1+t12k1)f(^n2+t22k2)f(^n3+t32k3)f(^n4+t42k4)(2m1+12)12 |
(2m2+12)12(2m3+12)12(2m4+12)122k1+k2+k3+k42P(t1)P(t2)P(t3)P(t4)dxdydzdl2k1+k2+k3+k4= |
with get out the constants as followes
(2m1+12k1+1)12(2m2+12k2+1)12(2m3+12k3+1)12(2m4+12k4+1)12∫1−1∫1−1∫1−1∫1−1f(^n1+t12k1) |
f(^n2+t22k2)f(^n3+t32k3)f(^n4+t42k4)Pm1(t1)Pm2(t2)Pm3(t3)Pm4(t4)dt1dt2dt3dt4= |
and polynomials are substituted
(12k1+1(2m1+1))12(12k2+1(2m2+1))12(12k3+1(2m3+1))12(12k4+1(2m4+1))12 |
∫1−1∫1−1∫1−1∫1−1f(^n1+t12k1)f(^n2+t22k2)f(^n3+t32k3)f(^n4+t42k4)d(Pm1+1(t1)−Pm1−1(t1)) |
d(Pm2+1(t2)−Pm2−1(t2))d(Pm3+1(t3)−Pm3−1(t3))d(Pm4+1(t4)−Pm4−1(t4))= |
(2m1+12k1+1)12(2m2+12k2+1)12(2m3+12k3+1)12(2m4+12k4+1)12f(^n1+t12k1)f(^n2+t22k2)f(^n3+t32k3)f(^n4+t42k4) |
(Pm1+1(t1)−Pm1−1(t1))(Pm2+1(t2)−Pm2−1(t2))(Pm3+1(t3)−Pm3−1(t3)) |
(Pm4+1(t4)−Pm4−1(t4))|1−1|1−1|1−1|1−1=−(123k1+1(2m1+1))12 |
Constants in terms of polynomials are calculated with the others,
(123k2+1(2m2+1))12(123k3+1(2m3+1))12(123k4+1(2m4+1))12∫1−1∫1−1∫1−1∫1−1f(^n1+t12k1) |
f(^n2+t22k2)f(^n3+t12k3)f(^n4+t42k4)d(Pm1+2(t1)−Pm1(t1)2m1+3−Pm1(t1)−Pm1−2(t1)2m1−1) |
d(Pm2+2(t2)−Pm2(t2)2m2+3−Pm2(t2)−Pm2−2(t2)2m2−1)d(Pm3+2(t3)−Pm3(t3)2m3+3−Pm3(t3)−Pm3−2(t3)2m3−1) |
d(Pm4+2(t4)−Pm4(t4)2m4+3−Pm4(t4)−Pm4−2(t4)2m4−1) |
that,
=|∫1−1∫1−1∫1−1∫1−1f′′(^n1+t12k1)f′′(^n2+t22k2)f′′(^n3+t12k3)f′′(^n4+t42k4) |
(Pm1+2(t1)−Pm1(t1)2m1+3−Pm1(t1)−Pm1−2(t1)2m1−1)(Pm2+2(t2)−Pm2(t2)2m2+3−Pm2(t2)−Pm2−2(t2)2m2−1) |
(Pm3+2(t3)−Pm3(t3)2m3+3−Pm3(t3)−Pm3−2(t3)2m3−1)(Pm4+2(t4)−Pm4(t4)2m4+3−Pm4(t4)−Pm4−2(t4)2m4−1)|2 |
|∫1−1∫1−1∫1−1∫1−1f′′(^n1+t12k1)f′′(^n2+t22k2)f′′(^n3+t12k3)f′′(^n4+t42k4) |
((2m1−1)Pm1+2(t1)−(4m1+2)Pm1(t1)+(2m1+3)2m1+3)... |
Using integration properties as following,
((2m4−1)Pm4+2(t4)−(4m4+2)Pm4(t4)+(2m1+3)2m4+3)dt1dt2dt3dt4|2≤ |
|∫1−1∫1−1∫1−1∫1−1f′′(^n1+t12k1)f′′(^n2+t22k2)f′′(^n3+t12k3)f′′(^n4+t42k4)dt1dt2dt3dt4|2× |
∫1−1∫1−1∫1−1∫1−1|((2m1−1)Pm1+2(t1)−(4m1+2)Pm1(t1)+(2m1+3)2m1+3)...dt1dt2dt3dt4|2 |
2M22M22M22M2∫1−1∫1−1∫1−1∫1−1((2m1−1)Pm1+2(t1)−(4m1+2)Pm1(t1)+(2m1+3)2m1+3) |
...dt1dt2dt3dt4= |
Upper bounds are noted as,
16M16(2m1+3)2(2m1−1)2...(2m4+3)2(2m4−1)2((2m1−1)222m1+5+ |
(4m1+2)222m1+1+(2m1+3)222m1−3)...((2m4−1)222m4+5+ |
(4m4+2)222m4+1+(2m4+3)222m4−3)< |
Now (2m1−1)2<(2m1+3)2, and (2m1+1)2<(2m1+3)2, then
=331776M16(2m1−1)((2m1−3)...(2m4−1)(2m4−3))2. |
Therefore we have,
|cn1,..,4,m1,..,4|<(576)121(2n)121(2m1−3)2...(2m4−3)2. |
Thus the series ∑∞n1=1∑∞m1=0∑∞n2=1∑∞m2=0∑∞n3=1∑∞m3=0∑∞n4=1∑∞m4=0cn1,..,4,m1,..,4, is ab-solute convergent, it follows that
∞∑n1=1∞∑m1=0∞∑n2=1∞∑m2=0∞∑n3=1∞∑m3=0∞∑n4=1∞∑m4=0cn1,..,4,m1,..,4ψn1,..,4,m1,..,4(x,y,z,l), |
convergence to the function f(x,y,z,l), uniformly.
Notice: This result for two dimensions in theorem (3.1), is calculated as upper bound,
|cn1n2m1m2|<(288)1212n521(2m1−3)2(2m2−3)2. |
In this methodology section, the Black-Scholes model for American put problem have used in the form of moving-boundary problem.
Lemma 4.1: For American options, we have [5,6]
ⅰ) The optimal exercise time for American call option is T and we have
C=c, |
ⅱ) The optimal exercise time for American put option as earlier as possible, i.e. t and we have
P≥p, |
ⅲ) The put-call parity for American option:
S−K<C−P<S−ke−t(T−t), |
As a consequence,
P≤K. |
Letting S be the price of an asset A at time t the American early exercise constraint leads to the following model for the value P(S,t) of an American put option to sell the asset A.
∂P∂t+12δ2S2∂2P∂S2+rS∂P∂S−rP=0,0≤t<T | (4.1) |
P(S,T)=max(E−S,0),S⩾0, |
∂P∂S(¯S,t)=−1, |
P(S,t)=E−S,0≤S<S(¯t). |
Where S(¯t) represent the free boundary. The parameters δ,r and E represent the volatility of the underlying asset, the interest rate, and the exercise price of the option. And the partial differential equation (PDE) associated with Eq.(2.25) for the fair values of European- style options forms a time- dependent connection- diffusion- reaction equation with mixed spatial- derivative terms.
∂u∂t=12S2v∂2u∂S2+12δ21v∂2u∂v2+12δ22r∂2u∂r2+ρ12δ1Sv∂2u∂S∂v+ρ13δ2S√vr∂2u∂S∂r | (4.2) |
ρ23δ1δ2√vr∂2u∂v∂r+rS∂u∂S+k(η−v)∂u∂v+a(b(T−t)−r)∂u∂r−ru. |
On the unbounded three- dimensional spatial domain S>0,v>0,r>0 with 0<t≤T. u(S,v,r,t) denotes the fair value of a European- style option if at the time τ=T−t the asset price, its variance, and the interest rate be equal to S,v and r respectively. Eq.(4.2) is known as the Heston- Cox- Ingersoll- Ross (HCIR) PDE. This equation usually appears with initial and boundary conditions which are determined by the specific option under consideration. Initial conditions in European call options equation is usually as follows,
u(S,v,r,T)=max(0,S−k). | (4.3) |
With a given strike price k>0.
In this methodology section the Eq.(4.1) and Eq.(4.2), have been solved using two dimensional and four dimensional Legendre wavelets respectively,
First we assume for B-S PDE,
∂P∂t=−α∂2P∂S2−α∂P∂S+αP, | (4.4) |
With boundary condition P(S,0)=β1(S,t), and ∂P(S,0)∂t=β2(S,t).
Those coefficients of αi the same parameters and variables which be in Eq.(4.1), in practice they will be calculate with this technique in follow.
∂P∂t=CT1(S,t)Ψ(S,t), | (4.5) |
Integrating (4.4) with respect to second variable over [0,t], we get
P(S,t)=CT1(S,t)PΨ(S,t)+β2(S,t), | (4.6) |
And also
∂P∂S=dCT(S,t)dSPΨ(S,t)+dβ2(S,t)dS, | (4.7) |
∂2P∂S2=d2CT(S,t)dS2PΨ(S,t)+d2β2(S,t)dS2, |
We have also
1=dTΨ(x),x=eTΨ(x). | (4.8) |
Substituting Eqs.(4.4) to (4.8) in Eq.(4.1), we obtain
CT1(S,t)Ψ=−α1(d2CT(S,t)dS2PΨ+d2β2(S,t)dS2dTΨ)−α2(dCT(S,t)dSPΨ+dβ2(S,t)dSdTΨ) | (4.9) |
+α3(CT1(S,t)PΨ+β2dTΨ). |
The solution of B-S PDE, P(S,t) can be calculate when Eq.(4.9) can be solved For unknown coefficients of the vector C(S,t).
C=[0.12177112177−0.2214022140e−1t,−0.1661746162+0.3451318952t,0.7030464532e−1−0.1278266279e−1t,−0.9594095941e−1+0.1992619926t]T. |
Let for HCIR PDE in Eq.(4.2), we assume as following,
∂u∂t=α1∂2u∂S2+α2∂2u∂v2+α3∂2u∂r2+α4∂2u∂S∂v+α5∂2u∂S∂r+α6∂2u∂v∂r+α7∂u∂S | (4.10) |
+α8∂u∂v+α9∂u∂r+α10u. |
Also with boundary condition
u(S,v,r,t)=β1(S,v,r,t), |
and
∂u(S,v,r,0)∂t=β2(S,v,r,t). |
Parameters of Eq.(4.2), by using method as following,
∂u∂t=CT1(S,v,r,t)Ψ(S,v,r,t), | (4.11) |
Integrating right hand side in Eq.(4.10), with respect to differential equations over [0, t] we get As well as
∂u∂S=dCT(S,v,r,t)dSPΨ(S,v,r,t)+dβ2(S,v,r,t)dS, | (4.12) |
∂u∂v=dCT(S,v,r,t)dvPΨ(S,v,r,t)+dβ2(S,v,r,t)dv, |
∂u∂r=dCT(S,v,r,t)drPΨ(S,v,r,t)+dβ2(S,v,r,t)dr. |
We have
∂2u∂S2=d2CT(S,v,r,t)dS2PΨ(S,v,r,t)+d2β2(S,v,r,t)dS2, | (4.13) |
∂2u∂v2=d2CT(S,v,r,t)dv2PΨ(S,v,r,t)+d2β2(S,v,r,t)dv2, |
∂2u∂r2=d2CT(S,v,r,t)dr2PΨ(S,v,r,t)+d2β2(S,v,r,t)dr2. |
And also
∂2u∂S∂v=ddSdCT(S,v,r,t)dvPΨ(S,v,r,t)+ddSdβ2(S,v,r,t)dv, | (4.14) |
∂2u∂S∂r=ddSdCT(S,v,r,t)drPΨ(S,v,r,t)+ddSdβ2(S,v,r,t)dr, |
∂2u∂v∂r=ddvdCT(S,v,r,t)drPΨ(S,v,r,t)+ddvdβ2(S,v,r,t)dr. |
Substituting (4.11) to (4.14) in (48), we obtain
CT(S,v,r,t)Ψ=α1(∂2CT(S,v,r,t)∂S2PΨ+d2β2(S,v,r,t)dS2dTΨ) | (4.15) |
+α2(∂2CT(S,v,r,t)∂v2PΨ+d2β2(S,v,r,t)dv2dTΨ)+α3(∂2CT(S,v,r,t)∂r2PΨ+d2β2(S,v,r,t)dr2dTΨ) |
+α4(ddSdCT(S,v,r,t)dvPΨ+ddSdβ2(S,v,r,t)dvdTΨ)+α5(ddSdCT(S,v,r,t)drPΨ+ddSdβ2(S,v,r,t)drdTΨ) |
+α6(ddvdCT(S,v,r,t)drPΨ+ddvdβ2(S,v,r,t)drdTΨ)+α7(∂CT(S,v,r,t)∂SPΨ+dβ2(S,v,r,t)dSdTΨ) |
+α8(∂CT(S,v,r,t)∂vPΨ+dβ2(S,v,r,t)dvdTΨ)+α9(∂CT(S,v,r,t)∂rPΨ+dβ2(S,v,r,t)drdTΨ) |
−α10(CT(S,v,r,t)PΨ+β2dTΨ). |
This system can be solved for unknown coefficients, which are calculated,
C[1,1]=0.1816780437e−2×sqrt(v)×s×t+.2310089049−0.3191757882e−2×s×t2−0.2243167683e−2×sqrt(v)×s−0.3663030704×s×t+0.5319596471e−3×t2+0.4571378943×s+0.8386506682e−2×t, |
C[1,2]=−0.4720134035e−3×sqrt(v)×s×t+0.8292430227e−3×s×t2+0.5827920594e−3×sqrt(v)×s+0.9516832935e−1×s×t−0.1382071705e−3×t2−0.1187679088×s−0.2178878351e−2×t+0.1997897471, |
C[1,3]=0,C[1,4]=0,C[1,5]=0.2097293811,C[1,6]=0.1705107084,C[1,7]=0,C[1,8]=0,C[1,9]=−0.04660652914,C[1,10]=−0.3789126853e−1,C[1,11]=0,C[1,12]=0,C[1,13]=−0.4036243822e−1,C[1,14]=−0.3281480113e−1,C[1,15]=0,C[1,16]=0. |
In this section, values of parameters are used in B-S PDE model (Figure 1): k1=k2=1,r=0.2,E=0.2,δ=0.001, and HCIR PDE model (Figures 2 and 3): k=0.1,η=0.12,ρ12=0.6,ρ13=0.2,ρ23=0.4,δ1=0.04,δ2=0.03, respectively. Appling values of parameters in models, numerical results are calculated. Due to the accuracy and convergence of spectral method which has been reviewed in [0,1], the numerical results obtained from this method can be assured. Therefore it can be used for future scams to compare with the same methods.
By choosing M=2,4 and k=1 in Eq.(2.23) and M=1,2 in Eq.(2.25) the models are solved from the coefficients vector of C that was found. Then the results were compared by Adomian decomposition method (ADM), as a semi-analytical method [20,21]. Tables 1 and 2 give values of pricing options solutions in B-S and HCIR models.
t | P(LWM,M=2) | P(LWM,M=4) | P(ADM) | Error |
0.1 | 0.3060753725 | 0.3060954392 | 0.3068021827 | 0.000726810 |
0.2 | 0.02615249781 | 0.02664386902 | 0.02613346987 | 0.000019028 |
0.4 | 0.04431200702 | 0.04942003420 | 0.04430950409 | 0.000005503 |
0.6 | 0.05847852762 | 0.05882487996 | 0.05862488320 | 0.000146355 |
0.8 | 0.08565205963 | 0.08534345864 | 0.08534345864 | 0.000308601 |
1 | 0.05083260299 | 0.0509281709 | 0.05075733333 | 0.000075268 |
(t,s) | U(LWM,M=1) | U(LWM,M=2) | U(ADM) | Error |
(5,50) | 0.1950128206e+3 | 0.1955141009e+3 | 0.1951180661e+3 | 0.0003960348 |
(10,100) | 0.2721204331e+4 | 0.2725170985e+4 | 0.2789681554e+4 | 0.0064510569 |
(12,150) | 0.6421235221e+4 | 0.6430612189e+4 | 0.6448380051e+4 | 0.0017767862 |
(15,200) | 0.1461103897e+5 | 0.1470153697e+5 | 0.1486797294e+5 | 0.0016643597 |
(17,250) | 0.2471265331e+5 | 0.2474067533e+5 | 0.2471107229e+5 | 0.0002960304 |
(19,300) | 0.3852234601e+5 | 0.3858603408e+5 | 0.3807240307e+5 | 0.0051363101 |
(22,380) | 0.6881012512e+5 | 0.6890201411e+5 | 0.6860384740e+5 | 0.0029816671 |
The multi-dimensional Legendre wavelet method has been applied to solve the Black-Scholes and Heston-Cox-Ingersoll-Ross models which these PDE equations have been derived from stochastic differential equations by using one of the important stochastic calculous Lemma named Ito. Generally the present method (LWM) reduces the partial differential equation into a set of algebraic equation and it can be useful in high degree derivatives problems, therefore considering the properties and using structure of LWM leads to save the time. Multi- dimensional Legendre wavelet is orthonormal set and its integration of the product is a diagonal matrix as in the case of the one dimension Legendre wavelet. In addition the Theorem 3.1 has been proved in paper which it could be determine an upper bound for coefficients of vector C. Also in this work results of LWM have been compared with the Adomian Decomposition Method (ADM) as a semi-analytical method and they have been shown for different parameters in Tables 1–2 and Figures 1–3, finally.
The authors declare that there is no conflicts of interest in this paper.
[1] | Hong J (2012) The state of phishing attacks. Communications of the ACM 55: 74-81. |
[2] | Singer PW, Friedman A (2014) Cybersecurity: What Everyone Needs to Know. 1st Eds, Oxford University Press. |
[3] | Krombholz K, Hobel H, Huber M, et al. (2015) Advanced social engineering attacks. J Inf Secur Appl 22: 113-122. |
[4] | Ducklin P (2020) Phishingové triky aneb 10 nejběžnějších podvodů roku 2020 (Phishing tricks or the 10 most common scams of 2020). IT SYSTEMS. Available from: https://www.systemonline.cz/it-security/phishingove-triky.htm |
[5] | Becton L (2020) The Importance of Digital Literacy in K-12, Available from: https://www.educationcorner.com/importance-digital-literacy-k-12.html |
[6] |
Parsons K, Butavicius M, Delfabbro P, et al. (2019) Predicting susceptibility to social influence in phishing emails. Int J Hum-Comput St 128: 17-26. doi: 10.1016/j.ijhcs.2019.02.007
![]() |
[7] | Lin T, Capecci DE, Ellis DM, et al. (2019) Susceptibility to Spear-Phishing Emails: Effects of Internet User Demographics and Email Content. ACM T Comput-Hum Int 26: 1-28. |
[8] |
Adewumi OA, Akinyelu AA (2016) A hybrid firefly and support vector machine classifier for phishing email detection. Kybernetes 45: 977-994. doi: 10.1108/K-07-2014-0129
![]() |
[9] |
Sami S, Nauman A, Li Z (2018) Detection of online phishing email using dynamic evolving neural network based on reinforcement learning. Decis Support Syst 107: 88-102. doi: 10.1016/j.dss.2018.01.001
![]() |
[10] | Zhao M, An B, Kiekintveld C (2016) Optimizing Personalized Email Filtering Thresholds to Mitigate Sequential Spear Phishing Attacks. In: Proceedings of 30th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence 30: 658-664. |
[11] | Wang J, Li Y, Rao HR (2016) Overconfidence in Phishing Email Detection. J Assoc Inf Syst 17: 759-783. |
[12] |
Williams EJ, Polage D (2019) How persuasive is phishing email? The role of authentic design, influence and current events in email judgements. Behav Inform Technol 38: 184-197. doi: 10.1080/0144929X.2018.1519599
![]() |
[13] |
Ferreira A, Teles S (2019) Persuasion: How phishing emails can influence users and bypass security measures. Int J Hum-Comput St 125: 19-31. doi: 10.1016/j.ijhcs.2018.12.004
![]() |
[14] |
Seifollahi S, Bagirov A, Layton R, et al. (2017) Optimization Based Clustering Algorithms for Authorship Analysis of Phishing Emails. Neural Process Lett 46: 411-425. doi: 10.1007/s11063-017-9593-7
![]() |
[15] |
Canfield CI, Fischhoff B, Davis A (2019) Better beware: comparing metacognition for phishing and legitimate emails. Metacognition and Learning 14: 343-362. doi: 10.1007/s11409-019-09197-5
![]() |
[16] | Nowak J, Korytkowski M, Wozniak M, et al. (2019) URL-based Phishing Attack Detection by Convolutional Neural Networks. Aust J Intell Inf Process Syst 15: 60-67. |
[17] |
Wei W, Ke Q, Nowak J, et al. (2020) Accurate and fast URL phishing detector: A convolutional neural network approach. Comput Netw 178: 107275. doi: 10.1016/j.comnet.2020.107275
![]() |
[18] | The text analytical software TOVEK, 2020. Available from: https://www.tovek.cz. |
[19] | Tobac R (2020) Social Engineer & Ethical Hacker. Live Hacking Demo: Hacking the Human, Sophos Evolve - Cybersecurity Summit, Webinar presentation. |
[20] | DZRO FVT-2, KYBERSILY. Project of faculty research: Cyber forces and resources. University of Defence, Faculty of Military Technologies, Brno, Czech Republic, 2021. |
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2. | Lei Fu, Huanhe Dong, Chaudry Masood Khalique, Hongwei Yang, CONSERVATION LAWS, EXACT SOLUTIONS OF TIME-SPACE FRACTIONAL GENERALIZED GINZBURG-LANDAU EQUATION FOR SHALLOW WAKE FLOWS, 2021, 11, 2156-907X, 874, 10.11948/20200053 | |
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t | P(LWM,M=2) | P(LWM,M=4) | P(ADM) | Error |
0.1 | 0.3060753725 | 0.3060954392 | 0.3068021827 | 0.000726810 |
0.2 | 0.02615249781 | 0.02664386902 | 0.02613346987 | 0.000019028 |
0.4 | 0.04431200702 | 0.04942003420 | 0.04430950409 | 0.000005503 |
0.6 | 0.05847852762 | 0.05882487996 | 0.05862488320 | 0.000146355 |
0.8 | 0.08565205963 | 0.08534345864 | 0.08534345864 | 0.000308601 |
1 | 0.05083260299 | 0.0509281709 | 0.05075733333 | 0.000075268 |
(t,s) | U(LWM,M=1) | U(LWM,M=2) | U(ADM) | Error |
(5,50) | 0.1950128206e+3 | 0.1955141009e+3 | 0.1951180661e+3 | 0.0003960348 |
(10,100) | 0.2721204331e+4 | 0.2725170985e+4 | 0.2789681554e+4 | 0.0064510569 |
(12,150) | 0.6421235221e+4 | 0.6430612189e+4 | 0.6448380051e+4 | 0.0017767862 |
(15,200) | 0.1461103897e+5 | 0.1470153697e+5 | 0.1486797294e+5 | 0.0016643597 |
(17,250) | 0.2471265331e+5 | 0.2474067533e+5 | 0.2471107229e+5 | 0.0002960304 |
(19,300) | 0.3852234601e+5 | 0.3858603408e+5 | 0.3807240307e+5 | 0.0051363101 |
(22,380) | 0.6881012512e+5 | 0.6890201411e+5 | 0.6860384740e+5 | 0.0029816671 |
t | P(LWM,M=2) | P(LWM,M=4) | P(ADM) | Error |
0.1 | 0.3060753725 | 0.3060954392 | 0.3068021827 | 0.000726810 |
0.2 | 0.02615249781 | 0.02664386902 | 0.02613346987 | 0.000019028 |
0.4 | 0.04431200702 | 0.04942003420 | 0.04430950409 | 0.000005503 |
0.6 | 0.05847852762 | 0.05882487996 | 0.05862488320 | 0.000146355 |
0.8 | 0.08565205963 | 0.08534345864 | 0.08534345864 | 0.000308601 |
1 | 0.05083260299 | 0.0509281709 | 0.05075733333 | 0.000075268 |
(t,s) | U(LWM,M=1) | U(LWM,M=2) | U(ADM) | Error |
(5,50) | 0.1950128206e+3 | 0.1955141009e+3 | 0.1951180661e+3 | 0.0003960348 |
(10,100) | 0.2721204331e+4 | 0.2725170985e+4 | 0.2789681554e+4 | 0.0064510569 |
(12,150) | 0.6421235221e+4 | 0.6430612189e+4 | 0.6448380051e+4 | 0.0017767862 |
(15,200) | 0.1461103897e+5 | 0.1470153697e+5 | 0.1486797294e+5 | 0.0016643597 |
(17,250) | 0.2471265331e+5 | 0.2474067533e+5 | 0.2471107229e+5 | 0.0002960304 |
(19,300) | 0.3852234601e+5 | 0.3858603408e+5 | 0.3807240307e+5 | 0.0051363101 |
(22,380) | 0.6881012512e+5 | 0.6890201411e+5 | 0.6860384740e+5 | 0.0029816671 |