Review Topical Sections

Survey on security and privacy issues in cyber physical systems

  • Received: 13 December 2018 Accepted: 27 March 2019 Published: 16 April 2019
  • The notion of Cyber-Physical Systems (CPS) is proposed by the National ScientificFoundation to describe a type of systems which combine hardware and software components andbeing the next step in development of embedded systems. CPS includes a wide range of researchtopics ranging from signal processing to data analysis. This paper contains a brief review of the basicinfrastructure for CPS including smart objects and network aspects in relation to TCP/IP stack. AsCPS reflect the processes of the physical environment onto the cyber space, virtualisation as animportant tool for abstraction plays crucial role in CPS. In this context paper presents the challengesassociated with mobility and vritualisation; accordingly three main types of virtualisation, namelynetwork, devices and applications virtualisation are presented in the paper. These aspects are tightlycoupled with security and safety issues. Therefore, different threats, attack types with correspondingsubtypes and possible consequences are discussed as well as analysis of various approaches to copewith existing threats is introduced. In addition threat modelling approaches were also in scope of thiswork. Furthermore, needs and requirements for safety-critical CPS are reviewed. Thus the mainefforts of this paper are directed on introducing various aspects of the CPS with regard to securityand safety issues.

    Citation: Artem A. Nazarenko , Ghazanfar Ali Safdar. Survey on security and privacy issues in cyber physical systems[J]. AIMS Electronics and Electrical Engineering, 2019, 3(2): 111-143. doi: 10.3934/ElectrEng.2019.2.111

    Related Papers:

    [1] Jingjing Li, Zhigang Huang . Radial distributions of Julia sets of difference operators of entire solutions of complex differential equations. AIMS Mathematics, 2022, 7(4): 5133-5145. doi: 10.3934/math.2022286
    [2] Hong Li, Keyu Zhang, Hongyan Xu . Solutions for systems of complex Fermat type partial differential-difference equations with two complex variables. AIMS Mathematics, 2021, 6(11): 11796-11814. doi: 10.3934/math.2021685
    [3] Nan Li, Jiachuan Geng, Lianzhong Yang . Some results on transcendental entire solutions to certain nonlinear differential-difference equations. AIMS Mathematics, 2021, 6(8): 8107-8126. doi: 10.3934/math.2021470
    [4] Yeyang Jiang, Zhihua Liao, Di Qiu . The existence of entire solutions of some systems of the Fermat type differential-difference equations. AIMS Mathematics, 2022, 7(10): 17685-17698. doi: 10.3934/math.2022974
    [5] Guowei Zhang . The exact transcendental entire solutions of complex equations with three quadratic terms. AIMS Mathematics, 2023, 8(11): 27414-27438. doi: 10.3934/math.20231403
    [6] Wenju Tang, Keyu Zhang, Hongyan Xu . Results on the solutions of several second order mixed type partial differential difference equations. AIMS Mathematics, 2022, 7(2): 1907-1924. doi: 10.3934/math.2022110
    [7] Fengrong Zhang, Linlin Wu, Jing Yang, Weiran Lü . On entire solutions of certain type of nonlinear differential equations. AIMS Mathematics, 2020, 5(6): 6124-6134. doi: 10.3934/math.2020393
    [8] Wenjie Hao, Qingcai Zhang . The growth of entire solutions of certain nonlinear differential-difference equations. AIMS Mathematics, 2022, 7(9): 15904-15916. doi: 10.3934/math.2022870
    [9] Minghui Zhang, Jianbin Xiao, Mingliang Fang . Entire solutions for several Fermat type differential difference equations. AIMS Mathematics, 2022, 7(7): 11597-11613. doi: 10.3934/math.2022646
    [10] Zhiying He, Jianbin Xiao, Mingliang Fang . Unicity of transcendental meromorphic functions concerning differential-difference polynomials. AIMS Mathematics, 2022, 7(5): 9232-9246. doi: 10.3934/math.2022511
  • The notion of Cyber-Physical Systems (CPS) is proposed by the National ScientificFoundation to describe a type of systems which combine hardware and software components andbeing the next step in development of embedded systems. CPS includes a wide range of researchtopics ranging from signal processing to data analysis. This paper contains a brief review of the basicinfrastructure for CPS including smart objects and network aspects in relation to TCP/IP stack. AsCPS reflect the processes of the physical environment onto the cyber space, virtualisation as animportant tool for abstraction plays crucial role in CPS. In this context paper presents the challengesassociated with mobility and vritualisation; accordingly three main types of virtualisation, namelynetwork, devices and applications virtualisation are presented in the paper. These aspects are tightlycoupled with security and safety issues. Therefore, different threats, attack types with correspondingsubtypes and possible consequences are discussed as well as analysis of various approaches to copewith existing threats is introduced. In addition threat modelling approaches were also in scope of thiswork. Furthermore, needs and requirements for safety-critical CPS are reviewed. Thus the mainefforts of this paper are directed on introducing various aspects of the CPS with regard to securityand safety issues.


    In this paper, we investigate the common transcendental directions of derivatives, primitives and Jackson difference operators of f, which is a non-trivial solution of the linear differential equation

    f(n)+An1f(n1)+...+A0f=0, (1.1)

    where n(2) is an integer and Ai(z)(i=0,1,...,n1) are entire functions of finite lower order.

    To accurately describe and study our problems, we need some basic results on complex dynamics of transcendental meromorphic functions. Let f: CC{} be a transcendental meromorphic function in the complex plane C, and fn(z)=f(fn1)(z),nN denote the n-th iterate of f(z). The Fatou set of f is denoted by F(f), that is the set of points such that {fn}n=1 is defined and normal in a neighborhood of z and the Julia set J(f) is its complement. It is well-known that F(f) is open, J(f) is closed and non-empty. More basic knowledge of complex dynamics can be found in [3].

    For a transcendental entire function f, Baker [2] first observed that J(f) cannot lie in finitely many rays emanating from the origin. In 1994, Qiao [11] introduced the limiting directions of a Julia set from a viewpoint of angular distribution.

    Definition 1.1. [11] The ray argz=θ(θ[0,2π)) is said to be a limiting direction of J(f) if Ω(θε,θ+ε)J(f) is unbounded for any ε>0, where Ω(θε,θ+ε)={zCc|argz(θε,θ+ε)}.

    The set of arguments of all limit directions of J(f) is denoted by

    Δ(f)={θ[0,2π)|the ray argz=θis a limiting direction ofJ(f)}.

    It is known that Δ(f) is closed and measurable and we use mesΔ(f) stands for its linear measure. For brevity, we call a limiting direction of the Julia set of f a Julia limiting direction of f. The Nevanlinna theory is an important tool in this paper. In what follows, we use some standard notations such as proximity function m(r,f), counting function of poles N(r,f), Nevanlinna characteristic function T(r,f) and some basic results in [7]. The order ρ(f) and lower order μ(f) of f(z) are defined by

    ρ(f)=lim suprlog+T(r,f)logr,μ(f)=lim infrlog+T(r,f)logr,

    respectively. The deficiency of the value a is denoted by δ(a,f), and if δ(a,f)>0, we say that a is a Nevanlinna deficient value of f(z).

    Qiao[10] proved that if f(z) is a transcendental entire function of finite lower order, then mesΔ(f)min{2π,π/μ(f)}. Recall J(tanz)=R, then we know the conclusion fails for general meromorphic functions. But under some conditions, Qiao's result can be generalized. In [16], Zheng et al. proved that for a transcendental meromorphic function f(z) with μ(f)< and δ(,f)>0, if J(f) has an unbounded component, then mesΔ(f)min{2π,4μ(f)arcsinδ(,f)2}. In [12], Qiu and Wu showed that the conclusion is still valid without the assumption that J(f) has an unbounded component. Some new progress on Julia limiting directions can be found in [5,8,13,14,15,16]. Especially, in order to analyze the structure of the limit directions, Wang and Yao[15] introduced a new direction in which f grows faster than any polynomials is a limit direction of f.

    Definition 1.2. [15] A value θ[0,2π) is said to be a transcendental direction of f if there exists an unbounded sequence of {zn} such that

    limnargzn=θandlimnlog|f(zn)|log|zn|=+.

    We use TD(f) to denote the union of all transcendental directions of f. Clearly, TD(f) is non-empty and closed.

    It is known that the growth properties of the function can affect the geometry and topology of the Julia sets and we can find that even weak growth along some unbounded sequence could be closely related to Julia limiting directions. Indeed, Wang and Yao obtained the following.

    Theorem A.[15] Let f be a transcendental meromorphic function. If either f has a direct tract or J(f){} is uniformly perfect at some point in J(f). Then

    TD(f)Δ(f).

    Remark 1.1. Actually, every transcendental entire function has at least a direct, and if J(f) has an unbounded component, then J(f){} is uniformly perfect at some point in J(f). Theorem A implies that many Julia limiting directions come from transcendental directions. Therefore, it is interesting to study the properties of transcendental directions since this may help us study the structure of Julia limiting direction. Although the concept of the transcendental direction is not introduced before, the idea to associate the Julia limiting directions with the growth rate of f in the directions has already appeared in Qiao[10] and Zheng-Wang-Huang[16]. Here, we give an example to illustrate the concepts of the transcendental direction and the Julia limiting direction.

    Example 1.1. Let f(z)=λexpz(λC{0}). Clearly, TD(f)=[π2,π2]. When 0<λ1e, J(f) is contained in the half plane Re(z)1, so Δ(f)=TD(f), while Δ(expz)=[0,2π) since J(expz)=C. This also shows that TD(f)Δ(f) may happen.

    By the results in [10,12,16], the measure of Δ(f) has a lower bound for some transcendental meromorphic functions, then the relationship TD(f)Δ(f) in Theorem A motivates us to pose a question : Can we estimate the lower bound of measure of TD(f)? In [15], Wang and Yao partially answered this question.

    Theorem B.[15] Let f be a transcendental meromorphic function. If μ(f)< and δ(,f)>0, then

    mes(TD(f))min{2π,4μ(f)arcsinδ(,f)2}.

    Remark 1.2. From Theorem B, a natural question arises: For meromorphic functions with infinite lower order, what is sufficient conditions for the existence of lower bound of the measure of TD(f)? Moreover, for entire functions and their derivatives, the difference between their local properties are astonishing. Then there exists another question: What is the relation between the transcendental directions of entire functions and that of their derivatives?

    Inspired by Theorem B, we try to answer the two questions. Actually, if f is a transcendental meromorphic function of finite lower order, then it must have sequences of Pólya peaks (see Lemma 2.3). Therefore, we can estimate the lower bound of the measure of set of transcendental directions of transcendental meromorphic functions of finite lower order by using sequences of Pólya peaks. But for a transcendental meromorphic function f with infinite lower order, it is impossible to use sequences of Pólya peaks to consider the lower bound of the measure of TD(f) because Lemma 2.3 can only apply to a transcendental meromorphic function with finite lower order. Therefore, we need to seek a new method to study the transcendental directions of entire solutions with infinite lower order. Indeed, we shall show that the transcendental directions of f(z), its k-th derivatives and its k-th integral primitive have a large amount of common transcendental directions. Set I(f)=kZTD(f(k)), where f(k) denotes the k-th derivative of f(z) for k0 or k-th integral primitives of f(z) for k<0. Our result can be stated as follows.

    Theorem 1.1. Let Ai(z)(i=0,1,...,n1) be entire functions of finite lower order such that A0 is transcendental and m(r,Ai)=o(m(r,A0))(i=1,2,...,n1) as r. Then, every non-trivial solution f of Eq (1.1) satisfies mesI(f)min{2π,π/μ(A0)}.

    Remark 1.3. It is easy to see that every non-trivial solution f of Eq (1.1) is an entire function with infinite lower order. Since A0(z) is entire and transcendental, then limrm(r,A0)logr=. Applying the lemma of logarithmic derivatives to Eq (1.1) yields

    m(r,A0)ni=1m(r,f(i)f)+n1i=1m(r,Ai)+O(1)=O(logT(r,f)+logr)+o(m(r,A0)),

    outside of a possible exceptional set E of finite linear measure. Therefore, all non-trivial solutions of Eq (1.1) are entire functions with infinite lower order.

    Remark 1.4. In [14], Wang and Chen proved mes(kZΔ(f(k)))min{2π,π/μ(A0)} under the conditions of Theorem 1.1. Clearly, in the case that μ(A0)<1/2, we know all Julia limiting directions of f come from its transcendental directions.

    In [4], Cao et al. recalled the Jackson difference operator

    Dqf(z)=f(qz)f(z)qzz,zC{0},qC{0,1}.

    For kN{0}, the Jackson k-th difference operator is denoted by

    D0qf(z):=f(z),Dkqf(z):=Dq(Dk1qf(z)).

    Clearly, if f is differentiable,

    limq1Dkqf(z)=f(k)(z).

    From Theorem 1.1, we know the set of transcendental directions of derivatives of every non-trivial solution f(z) of Eq (1.1) must have a definite range of measure with some additional conditions. Naturally, a question arise: Can we estimate the lower bound of measure of TD(Dkqf)? Furthermore, what is the relation between the transcendental directions of entire functions and those of their Jackson difference operators?

    To answer these questions, we first need to figure out the growth of Dkqf(z). In 2020, Long et al. [9] considered the growth of q difference operator ˆDqf(z):=f(qz)f(z) of the transcendental meromorphic function and obtained the following result.

    Theorem C.[9] If f is a transcendental meromorphic function and |q|1, then ρ(ˆDqf)=ρ(f) and μ(ˆDqf)=μ(f).

    Moreover, in the introduction of [9], the authors pointed out that this result also holds for Jackson difference operators. In fact, we know the set of transcendental directions of a transcendental entire function with finite lower order must have a definite range of measure from Theorem B. Hence, the left is the case of entire functions with infinite lower order. From Remark 1.3, we know that every non-trivial solution f of Eq (1.1) is an entire function with infinite lower order, and by [9], the k-th Jackson difference operator Dkqf of f is also of infinite lower order if f is a transcendental meromorphic with infinite lower order. Therefore, motivated by these facts, we try to study the common transcendental directions of solutions of Eq (1.1) and their Jackson difference operator. Here, we denote R(f)=kN{0}TD(Dkqf), where q(0,+){1}. Then we have the following result.

    Theorem 1.2. Let Ai(z)(i=0,1,...,n1) be entire functions of finite lower order such that A0 is transcendental and m(r,Ai)=o(m(r,A0))(i=1,2,...,n1) as r. Then, every non-trivial solution f of Eq (1.1) satisfies mesR(f)min{2π,π/μ(A0)} for all q(0,+){1}.

    Remark 1.5. Although limq1Dkqf(z)=f(k)(z), it just means that the k-th derivative is the limit of a family of Jackson difference operator. In Theorem 1.1, our calculation of the integral primitives of f(z) depends on the lemma of logarithmic derivatives in the angular domain. At present, we only have the Jackson difference analogue of logarithmic derivative lemma for meromorphic functions with zero order in the whole plane, see [4]. Therefore, for k<0, we do not have sufficient conditions to estimate the lower bound of measure of TD(Dkqf). Thus, Theorem 1.1 still makes sense.

    Usually, we cannot expect too much close relations of transcendental directions between ˆDqf and f. But Theorem C shows us that f and ˆDqf have both the same order and lower order. So we may also consider the measure of transcendental directions of k-th q difference operators of meromorphic functions with infinite lower order. Now, we denote the k-th q difference operator by

    ˆD0qf(z):=f(z),ˆDkqf(z):=ˆDq(ˆDk1qf(z)),

    where kN{0}. Here, we denote E(f)=kN{0}TD(ˆDkqf), where q(0,+){1}. Then we have the following result.

    Theorem 1.3. Let Ai(z)(i=0,1,...,n1) be entire functions of finite lower order such that A0 is transcendental and m(r,Ai)=o(m(r,A0))(i=1,2,...,n1) as r. Then, every non-trivial solution f(z) of Eq (1.1) satisfies mesE(f)min{2π,π/μ(A0)} for all q(0,+){1}.

    Before introducing lemmas, we recall the Nevanlinna characteristic in an angle, see[6,17]. Assuming 0<α<β<2π, we denote

    Ω(α,β)={zC|argz(α,β)},
    Ω(α,β,r)={zC|zΩ(α,β),|z|<r},
    Ω(r,α,β)={zC|zΩ(α,β),|z|>r},

    and use ¯Ω(α,β) to denote the closure of Ω(α,β).

    Let g(z) be meromorphic on the angular ¯Ω(α,β), we define

    Aα,β(r,g)=ωπr1(1tωtωr2ω){log+|g(teiα)|+log+|g(teiβ)|}dtt,Bα,β(r,g)=2ωπrωβαlog+|g(reiθ)|sinω(θα)dθ,Cα,β(r,g)=21<|bn|<r(1|bn|ω|bn|ωr2ω)sinω(βnα),

    where ω=π/(βα), and bn=|bn|eiβn are the poles of g(z) in ¯Ω(α,β) according to their multiplicities. The Nevanlinna angular characteristic is defined as follows:

    Sα,β(r,g)=Aα,β(r,g)+Bα,β(r,g)+Cα,β(r,g).

    In particular, we use σα,β(g)=lim suprlogSα,β(r,g)logr to denote the order of Sα,β(r,g). The following lemmas play the key roles in proving our results.

    Lemma 2.1. [17] Let f(z) be a meromorphic function on Ω(αε,β+ε) for ε>0 and 0<α<β<2π. Then

    Aα,β(r,ff)+Bα,β(r,ff)K(log+Sαε,β+ε(r,f)+logr+1).

    Lemma 2.2. [8] Let z=rexp(iψ),r0+1<r and αψβ, where 0<βα2π. Suppose that n(2) is an integer, and that g(z) is analytic in Ω(r0,α,β) with σα,β<. Choose α<α1<β1<β, then, for every ε(0,βjαj2)(j=1,2,...,n1) outside a set of linear measure zero with

    αj=α+j1s=1εsandβj=β+j1s=1εs,j=2,3,...,n1,

    there exist K>0 and M>0 only depending g, ε1,...,εn1 and Ω(αn1,βn1), and not depending on z such that

    |g(z)g(z)|KrM(sink(ψα))2

    and

    |g(n)(z)g(z)|KrM(sink(ψα)n1j=1sinkj(ψαj))2

    for all zΩ(αn1,βn1) outside an R-set D, where k=π/(βα) and kεj=π/(βjαj(j=1,2,...,n1)).

    To estimate the measure of TD(f), we need to find the directions in which f grows faster than any polynomial. For this we will use the following result of Baernstein.

    Lemma 2.3. [1] Let f(z) be a transcendental meromorphic function of finite lower order μ, and have one deficient value a. Let Λ(r) be a positive function with Λ(r)=o(T(r,f)) as r. Then for any fixed sequence of Pólya peaks {rn} of order μ, we have

    lim infrmesDΛ(rn,a)min{2π,4μarcsinδ(a,f)2}, (2.1)

    where DΛ(r,a) is defined by

    DΛ(r,)={θ[π,π):|f(reiθ)|>eΛ(r)},

    and for finite a,

    DΛ(r,a)={θ[π,π):|f(reiθ)a|<eΛ(r)}.

    Proof of Theorem 1.1. The assertion mesI(f)σ:=min{2π,π/μ(A0)} would be obtained by reduction to absurdity. Suppose on the contrary that mesI(f)<σ:=min{2π,π/μ(A0}. Then t:=σmesI(f)>0. For every kZ, TD(f(k)) is closed, and so I(f) is a closed set. Denoted by S:=(0,2π)I(f) the complement of I(f). Then S is open, so it consists of at most countably many open intervals. We can choose finitely many open intervals Ii=(αi,βi)(i=1,2,...,m) in S such that

    mes(Smi=1Ii)<t4. (3.1)

    For every θiIi, argz=θi is not a transcendental direction of f(k) for some kZ. Then there exists an angular domain Ω(θiξθi,θi+ξθi) such that

    (θiξθi,θi+ξθi)IiandΩ(θiξθi,θi+ξθi)TD(f(k))=, (3.2)

    where ξθi is a constant depending on θi. Hence, θiIi(θiξθi,θi+ξθi) is an open covering of [αi+ε,βiε] with 0<ε<min{(βiαi)/6,i=1,2,...,m}. By Heine-Borel theorem, we can choose finitely many θij, such that

    [αi+ε,βiε]sij=1(θijξθij,θij+ξθij).

    From (3.2) and the definition of transcendental direction, we have

    |f(k)(z)|=O(|z|d),zΩ(αij,βij), (3.3)

    where d is a positive constant, αij=θijξθij+ε and βij=θij+ξθijε.

    Case 1. Suppose k0. We note the fact that

    f(k1)(z)=z0f(k)(ζ)dζ+c,

    where c is a constant, and the integral path is the segment of a straight line from 0 to z. From this and (3.3), we can deduce f(k1)(z)=O(|z|d+1) for zΩ(αij,θij). Repeating the discussion k times, we can obtain

    f(z)=O(|z|d+k),zΩ(αij,βij).

    It means that

    Sαij,βij(r,f)=O(logr). (3.4)

    Case 2. Suppose k<0. Clearly, for f(k)(z), there is not just one primitive, but a whole family. However, for any integral primitive f(k+1)(z), we have

    Sαij+ε,βijε(r,f(k+1))Sαij+ε,βijε(r,f(k+1)f(k))+Sαij+ε,βijε(r,f(k))

    for |k|ε=ε.

    By (3.3) and Lemma 2.1, we can obtain

    Sαij+ε,βijε(r,f(k+1))=O(logr).

    Using the discussion |k| times, we have

    Sαij+ε,βijε(r,f)=O(logr). (3.5)

    It follows from (3.4) and (3.5) that whatever k is positive or not, we always have

    Sαij+ε,βijε(r,f)=O(logr). (3.6)

    Therefore, by Lemma 2.2, there exists two constants M>0 and K>0 such that

    |f(s)(z)f(z)|KrM,(s=1,2,...,n) (3.7)

    for all zmi=1sij=1Ω(θijξθij+3ε,θij+ξθij3ε) outside a R-set H.

    Next, we define

    Λ(r)=max{logr,m(r,A1),...,m(r,An1)}m(r,A0).

    It is clear that Λ(r)=o(m(r,A0)) and m(r,Ai)=o(Λ(r)),i=1,2,...,n. Since A0 is entire, is a deficient value of A0 and δ(,A0)=1. By Lemma 2.3, there exists an increasing and unbounded sequence {rk} such that

    mesDΛ(rk)σt/4, (3.8)

    where

    DΛ(r):=DΛ(r,)={θ[π,π):log|A0(reiθ)|>Λ(r)}, (3.9)

    and all rk{|z|:zH}. Set

    U:=n=1EnwithEn:=k=nDΛ(rk), (3.10)

    one can see that

    mes(U)=mes(n=1En)=limnmes(En). (3.11)

    From (3.10), we know DΛ(rn)En for each n. Then

    limnmes(En)lim infnmes(DΛ(rn)). (3.12)

    It follows from (3.8), (3.11) and (3.12) that

    mes(U)lim infnmes(DΛ(rn))σt/4.

    Clearly,

    mes[(mi=1Ii)U]=mes(SU)mes[(Smi=1Ii)U]mes(U)mesI(f)mes(Smi=1Ii)σt4mesI(f)t4=t2.

    Let Jij=(θijξθij+3ε,θij+ξθij3ε). Then

    mes(mi=1sij=1Jij)mes(mi=1Ii)(3m+6ζ)ε,

    where ζ=mi=1si. Choosing ε small enough, we can deduce

    mes[(mi=1sij=1Jij)U]t4.

    Thus there exists an open interval Ji0j0 such that

    mes(Ji0j0U)>t4ζ>0.

    Let F=Ji0j0U, from (3.9), there exists a subsequence {rkj} of {rk} such that

    Flog+|A0(rkjeiθ)|dθt4ζΛ(rkj). (3.13)

    On the other hand, coupling (1.1) and (3.7) leads to

    Flog+|A0(rkjeiθ)|dθF(n1i=1log+|Ai(rkjeiθ)|)dθ+O(logrkj)n1i=1m(rkj,Ai)+O(logrkj). (3.14)

    (3.13) and (3.14) give out

    t4ζΛ(rkj)ni=1m(rkj,Ai)+O(logrkj), (3.15)

    which is impossible since m(r,Ai)=o(Λ(r))(i=1,...,n1) as r. Hence, we get

    mesI(f)σ.

    Proof of Theorem 1.2. Firstly, we suppose that

    mesR(f)<σ:=min{2π,π/μ(A0}. (3.16)

    Then t:=σmesR(f)>0. Similarly as in the proof of Theorem 1.1, we have

    |Dkqf(z)|=O(|z|d),zΩ(αij,βij), (3.17)

    where d is a positive constant, αij=θijξθij+ε and βij=θij+ξθijε.

    By the definition of Jackson k-th difference operator,

    |Dkqf(z)|=|Dk1qf(qz)Dk1qf(z)||qzz|=O(|z|d),zΩ(αij,βij). (3.18)

    Therefore,

    |Dk1qf(qz)Dk1qf(z)|=O(|z|d+1),zΩ(αij,βij). (3.19)

    Thus, there exists a positive constants C such that

    |Dk1qf(qz)Dk1qf(z)|C(|z|d+1),zΩ(αij,βij). (3.20)

    Case 1. Suppose q(0,1). Clearly, there exists a positive integer m such that (1q)m|z|(1q)m+1 for sufficiently large |z|. Therefore, 1|qmz|1q. Then there exists a positive constant M1 such that |Dk1qf(qmz)|M1 for all z{z|1|qmz|1q}. Using inequality (3.20) repeatedly, we have

    |Dk1qf(z)Dk1qf(qz)|C(|z|d+1),|Dk1qf(qz)Dk1qf(q2z)|C(|qz|d+1),...|Dk1qf(qm1z)Dk1qf(qmz)|C(|qm1z|d+1). (3.21)

    Taking the sum of all inequalities, we get

    |Dk1qf(z)||Dk1qf(z)Dk1qf(qz)|+|Dk1qf(qz)Dk1qf(q2z)|+...+|Dk1qf(qm1z)Dk1qf(qmz)|+|Dk1qf(qmz)|C(|z|d+1)+C(|qz|d+1)+...+C(|qm1z|d+1)+M1mC(1+qd+1+...+q(m1)(d+1))|z|d+1+M1=O(|z|d+1),zΩ(αij,βij). (3.22)

    Therefore,

    |Dk1qf(z)|=O(|z|d+1),zΩ(αij,βij). (3.23)

    Repeating the operations from (3.17) to (3.23), we have

    |f(z)|=O(|z|d+k1),zΩ(αij,βij). (3.24)

    Case 2. Suppose q(1,+). If |z| is sufficiently large, there exists a positive integer n such that qn|z|qn+1. And this is exactly 1|zqn|q. Thus, there exists a positive constant M2 such that |Dk1qf(zqn)|M2 for all z{z|1|zqn|q}. Using inequality (3.20) repeatedly, we have

    |Dk1qf(z)Dk1qf(zq)|C(|zq|d+1),|Dk1qf(zq)Dk1qf(zq2)|C(|zq2|d+1),...|Dk1qf(zqn1)Dk1qf(zqn)|C(|zqn|d+1). (3.25)

    Taking the sum of all inequalities, we get

    |Dk1qf(z)||Dk1qf(z)Dk1qf(zq)|+|Dk1qf(zq)Dk1qf(zq2)|+...+|Dk1qf(zqn1)Dk1qf(zqn)|+|Dk1qf(zqn)|C(|zq|d+1)+C(|zq2|d+1)+...+C(|zqn|d+1)+M2nC(1qd+1+1q2(d+1)+...+1qn(d+1))|z|d+1+M2=O(|z|d+1),zΩ(αij,βij). (3.26)

    Therefore,

    |Dk1qf(z)|=O(|z|d+1),zΩ(αij,βij). (3.27)

    Similarly as in (3.17)–(3.20), we have

    |f(z)|=O(|z|d+k1),zΩ(αij,βij). (3.28)

    It means that

    Sαij,βij(r,f)=O(logr). (3.29)

    By the same reasoning as in (3.6) to (3.15), we can get a contradiction. Hence, we get

    mesR(f)σ.

    This completes the proof of Theorem 1.2.

    Proof of Theorem 1.3. Using ˆDkqf(z) instead of Dkqf(z) in the proof of Theorem 1.2 and by the definition of k-th q difference operator, we can prove Theorem 1.3 similarly.

    In this article, we obtained the lower bound of the measure of the set of transcendental directions of a class of transcendental entire functions with infinite lower order, which are solutions of linear differential equations. We also discussed the case for the Jackson difference operators and q difference operators of such functions. Actually, we know the set of transcendental directions of a transcendental entire function with finite lower order must have a definite range of measure from Theorem B. Hence, the left is the case of entire functions with infinite lower order. Usually, it is difficult to estimate the lower bound of measure of the set of transcendental directions of a transcendental entire function with infinite lower order. However, our article obtained some results on the transcendental directions for a class of entire functions with infinite lower order. But for more general cases, we still need to find other ways to investigate the lower bound of the measure of the set of transcendental directions.

    The work was supported by NNSF of China (No.11971344), Research and Practice Innovation Program for Postgraduates in Jiangsu Province (KYCX20\_2747).

    The authors declare that they have no conflict of interest.



    [1] National Science Foundation (NSF): Cyber-Physical Systems, USA, 2015. Available from: http://www.nsf.gov/pubs/2015/nsf15541/nsf15541.pdf.
    [2] Camarinha-Matos LM, Goes J, Gomes L, et al. (2013) Contributing to the Internet of Things, In: Doctoral Conference on Computing, Electrical and Industrial Systems, pp. 312. Springer, Berlin, Heidelberg.
    [3] Hermann M, Pentek T, Otto B (2015) Design Principles for Industrie 4.0 Scenarios: A Literature Review. Working Paper, Technical University of Dortmund, Dortmund, Germany.
    [4] Evans PC, Annunziata M (2012) Industrial Internet: Pushing the Boundaries of Minds and Machines. Available from: http://www.ge.com/docs/chapters/Industrial_Internet.pdf.
    [5] Schmidt DC, White J, Gill CD (2014) Elastic Infrastructure to Support Computing Clouds for Large-scale Cyber-Physical Systems, In: 2014 IEEE 17th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing, pp. 5663, IEEE.
    [6] Koubaa A, Björn A (2009) A Vision of Cyber-Physical Internet, In: 8th International Workshop on Real Time Networks (RTN'09), pp. 16. Instituo Politécnico do Porto. Instituto Superior de Engenharia do Porto.
    [7] Tan Y, Goddard S, Pérez LC (2008) A prototype architecture for cyber-physical systems. ACM SIGBED Review 5: 26.
    [8] Sztipanovits J, Koutsoukos X, Karsai G, et al. (2012) Toward a science of cyberphysical system integration. Proceedings of the IEEE 100: 2944.
    [9] Kocabas O, Soyata T, Aktas MK (2016) Emerging Security Mechanisms for Medical Cyber Physical Systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics 13: 401416.
    [10] Gunes V, Peter S, Givargis T, et al. (2014) A Survey on Concepts, Applications, and Challenges in Cyber-Physical Systems. KSII T Internet Inf 8: 42424268.
    [11] Baheti R, Gill H (2011) Cyber-physical Systems. The Impact of Control Technology 12: 161166.
    [12] Ding W, Engel W, Goode A, et al. (2016) Declarative Modeling Cases of Cyber Physical Systems. In: 2016 International Conference on Logistics, Informatics and Service Sciences (LISS), pp. 16. IEEE.
    [13] Ahmad A, Paul A, Rathore MM, et al. (2016) Smart cyber society: Integration of capillary devices with high usability based on CyberPhysical System. Future Gener Comp Sy 56: 493503.
    [14] Molina E, Jacob E (2017) Software-defined networking in cyber-physical systems: A survey. Comput Electr Eng 66: 407419.
    [15] Ashibani Y, Mahmoud QH (2017) Cyber physical systems security: Analysis, challenges and solutions. Comput Secur 68: 8197.
    [16] Heath S (2002) Embedded Systems Design. 2nd Edition, Newnes, Oxford, UK.
    [17] Mascolo C, Hailes S, Lymberopoulos L, et al. (2005) Survey of middleware for networked embedded systems. Project report. Available from: http://erepo.usiu.ac.ke/bitstream/handle/11732/12/IST-RUNES_D5.1.pdf?sequence=1&isAllowed=y.
    [18] Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52: 22922330.
    [19] Vasseur J-P, Dunkels A (2010) Interconnecting Smart Objects with IP: The Next Internet. Morgan Kaufmann.
    [20] Akyildiz IF, Kasimoglu IH (2004) Wireless sensor and actor networks: research challenges. Ad Hoc Netw 2: 351367.
    [21] Weiser M (1999) Some computer science issues in ubiquitous computing. ACM SIGMOBILEMobile Computing and Communications Review 3: 12.
    [22] Friedewald M, Raabe O (2011) Ubiquitous computing: An overview of technology impacts. Telematics and Informatics, 28: 5565.
    [23] Mayer S, Verborgh R, Kovatsch M, et al. (2016) Smart Configuration of Smart Environments. IEEE T Autom Sci Eng 13: 12471255.
    [24] IERC-European Research Cluster on the Internet of Things, 2014. Available from: http://www.internet-of-things-research.eu/about_iot.htm.
    [25] ITU-International Telecommunication Union, 2012. Recommendation Y.2069: Terms and definitions for the Internet of things. Available from: https://www.itu.int/rec/T-REC-Y.2069-201207-I/en.
    [26] Weber RH and Studer E (2016) Cybersecurity in the Internet of Things: Legal aspects. Computer Law & Security Review 32: 715728.
    [27] Chaouchi H (Ed.) (2013) The Internet of Things Connecting Objects to the Web. John Wiley & Sons.
    [28] Li H, Dimitrovski A, Song JB, et al. (2014) Communication Infrastructure Design in Cyber Physical Systems with Applications in Smart Grids: A Hybrid System Framework. IEEE Communications Surveys & Tutorials 16: 16891708.
    [29] Szczodrak M, Yang Y, Cavalcanti D, et al. (2013) An open framework to deploy heterogeneous wireless testbeds for Cyber- Physical Systems. In: 2013 8th IEEE International Symposium on Industrial Embedded Systems (SIES), pp. 215224.
    [30] Lee J, Bagheri B, Kao HA (2015) A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters 3: 1823.
    [31] Hu L, Xie N, Kuang Z, et al. (2012) Review of Cyber-Physical System Architecture. In: 2012IEEE15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops, pp. 2530.
    [32] Rixner S (2008) Network virtualization: Breaking the performance barrier. Queue 6: 37.
    [33] Rauchfuss H, Wild T, Herkersdorf A (2010) A network interface card architecture for I/O virtualization in embedded systems. In: Proceedings of the 2nd conference on I/O virtualization, pp. 22. USENIX Association.
    [34] Ganegedara T, Jiang W, Prasanna V (2011) Multiroot: Towards memory-efficient router virtualization. In: 2011IEEE International Conference on Communications (ICC), pp. 15.
    [35] Egi N, Greenhalgh A, Handley M, et al. (2007) Evaluating Xen for Router Virtualization. In: 2007 16th International Conference on Computer Communications and Networks (ICCCN), pp. 12561261.
    [36] Wen H, Tiwary PK, Le-Ngoc T (2013) Network Virtualization: Overview. In: Wireless Virtualization, pp. 510. Springer, Cham.
    [37] Canonico R, Di Gennaro P, Vittorio M, et al. (2007) Virtualization Techniques in Network Emulation. In: European Conference on Parallel Processing, pp. 144153. Springer, Berlin, Heidelberg.
    [38] Carapinha J, Jiménez J (2009) Network virtualization: a view from the bottom. In: Proceedings of the 1st ACM workshop on Virtualized infrastructure systems and architectures, pp. 7380. ACM.
    [39] Martínez NL, Martínez JF, Díaz VH (2014) Virtualization of Event Sources in Wireless Sensor Networks for the Internet of Things. Sensors 14: 2273722753.
    [40] Taherkordi A, Eliassen F (2014) Towards Independent in-Cloud Evolution of Cyber-Physical Systems. In: 2014 IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pp. 1924.
    [41] Kuehnle H (2014) Smart Equipment and Virtual Resources trigger Network Principles in Manufacturing. In: IOP Conference Series: Material Science and Engineering, Vol. 58, p. 012002. IOP Publishing.
    [42] Karnouskos S (2011) Cyber-physical systems in the smartgrid. In: 2011 9th IEEE International Conference on Industrial Informatics, pp. 2023. IEEE.
    [43] Gokhale A, McDonald MP, Poff L (2010) Resource Provisioning and Dynamic Resource Management in Intelligent Transportation Systems. In: 11th International Conference on Mobile Data Management, Kansas City, USA.
    [44] García-Valls M, Cucinotta T, Lu C (2014) Challenges in real-time virtualization and predictable cloud computing. J Syst Architect 60: 726-740. doi: 10.1016/j.sysarc.2014.07.004
    [45] Al-Fuqaha AI, Guizani M, Mohammadi M, et al. (2015) Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials 17: 23472376.
    [46] Pham Q, Malik T, Glavic B, et al. (2015) LDV: Light-weight database virtualization. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE), pp. 11791190.c
    [47] Verdouw CN, Beulens AJM, Reijers HA, et al. (2015) A control model for object virtualization in supply chain management. Comput Ind 68: 116131.
    [48] Verdouw CN, Wolfert J, Beulens AJM, et al. (2016) Virtualization of food supply chains with the internet of things. J Food Eng 176: 128136.
    [49] Liu N, Li X, Shen W (2014) Multi-granularity resource virtualization and sharing strategies in cloud manufacturing. J Netw Comput Appl 46: 7282.
    [50] Kertesz A, Kecskemeti G, Brandic I (2014) An interoperable and self-adaptive approach for SLA-based service virtualization in heterogeneous Cloud environments. Future Gener Comp Sy 32: 5468.
    [51] Márquez FG, Jimenez M, Ralli C, et al. (2015) Developing your first application using FI-WARE. Available from: http://cattelefonica.webs.upv.es/Fiware/developingyourfirstapplicationusingfiware.pdf.
    [52] Gonizzi P, Ferrari G, Gay V, et al. (2015) Data dissemination scheme for distributed storage for IoT observation systems at large scale. Inform Fusion 22: 1625.
    [53] Janak J, Schulzrinne H (2016) Framework for Rapid Prototyping of Distributed IoT Applications Powered by WebRTC, In: 2016Principles, Systems and Applications of IP Telecommunications (IPTComm), pp. 17. IEEE.
    [54] Girau R, Martis S, Atzori L (2017) Lysis: A Platform for IoT Distributed Applications Over Socially Connected Objects. IEEE Internet Things 4: 4051.
    [55] McMahan HB, Moore E, Ramage D, et al. (2017) Communication-Efficient Learning of Deep Networks from Decentralized Data. International Conference on Artificial Intelligence and Statistics, 12731282.
    [56] Larsen RB, Carron A, Zeilinger MN (2017) Safe Learning for Distributed Systems with Bounded Uncertainties. IFAC-PapersOnLine 50: 25362542.
    [57] Vincent H, Wells L, Tarazaga P, et al. (2015) Trojan Detection and Side-Channel Analyses for Cyber-Security in Cyber- Physical Manufacturing Systems. Procedia Manufacturing 1: 7785.
    [58] Friedberg I, McLaughlin K, Smith P, et al. (2017) STPA-SafeSec: Safety and security analysis for cyber-physical systems. Journal of information security and applications 34: 183196.
    [59] Govindarasu M, Hann A, Sauer P (2012) Cyber-Physical Systems Security for Smart Grid. Future Grid InitiativeWhite Paper, PSERC.
    [60] Alcaraz C, Lopez J, Wolthusen SD (2016) Policy enforcement system for secure interoperable control in distributed Smart Grid systems. J Netw Comput Appl 59: 301314.
    [61] Di Sarno C, Garofalo A, Matteucci I, et al. (2016) A novel security information and event management system for enhancing cyber security in a hydroelectric dam. Int J Crit Infr Prot 13: 3951.
    [62] Lenzini G, Mauw S, Ouchani S (2015) Security analysis of socio-technical physical systems. Comput Electr Eng 47: 258274.
    [63] Perkins C, Muller G (2015) Using Discrete Event Simulation to Model Attacker Interactions with Cyber and Physical Security Systems. Procedia Computer Science 61: 221226.
    [64] Cherdantseva Y, Burnap P, Blyth A, et al. (2016) A review of cyber security risk assessment methods for SCADA system. Comput Secur 56: 127.
    [65] Cardenas AA, Amin S, Sinopoli B, et al. (2009) Challenges for Securing Cyber Physical Systems. Workshop on future directions in cyber-physical systems security 5.
    [66] Mo Y, Kim THJ, Brancik K, et al. (2011) CyberPhysical Security of a Smart Grid Infrastructure. P IEEE 100: 195209.
    [67] Ozturk M, Aubin P (2011) SCADA Security: Challenges and Solutions. White Paper, Telemetry & Remote SCADA Solutions, Schneider Electric.
    [68] Alcaraz C, Zeadally S (2013) Critical Control System Protection in the 21st Century. Computer 46: 7483.
    [69] Creery A, Byres EJ (2005) Industrial cybersecurity for power system and SCADA networks. In: Record of Conference Papers Industry Applications Society52nd Annual Petroleum and Chemical Industry Conference, pp. 303309. IEEE.
    [70] Humayed A, Lin J, Li F, et al. (2017) Cyber-Physical Systems Security-A Survey. IEEE Internet Things 4: 18021831.
    [71] Papp D, Ma Z, Buttyan L (2015) Embedded Systems Security: Threats, Vulnerabilities, and Attack Taxonomy. In: 2015 13th Annual Conference on Privacy, Security and Trust (PST), pp. 145152.
    [72] Nur AY, Tozal ME (2016) Defending Cyber-Physical Systems against DoS Attacks. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 13. IEEE.
    [73] Neumann PG (2006) Risks to the Public. ACM SIGSOFT Software Engineering Notes 30.
    [74] Jokar P, Arianpoo N, Leung VCM (2013) Spoofing detection in IEEE 802.15.4 networks based on received signal strength. Ad Hoc Netw 11: 26482660.
    [75] Su Z, Wassermann G (2006) The Essence of Command Injection Attacks in Web Applications. In: Acm Sigplan Notices 41: 372382.
    [76] Shoukry Y, Martin P, Tabuada P, et al. (2013) Non-invasive Spoofing Attacks for Anti-lock Braking Systems. In: International Workshop on Cryptographic Hardware and Embedded Systems, pp. 5572. Springer, Berlin, Heidelberg.
    [77] Chen Y, Kar S, Moura JMF (2016) Cyber Physical Attacks with Control Objectives. In: 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 11251130. IEEE.
    [78] Cazorla L, Alcaraz C, Lopez J (2018) Cyber Stealth Attacks in Critical Information Infrastructures. IEEE Syst J 12: 17781792.
    [79] Wurm J, Jin Y, Liu Y, et al. (2017) Introduction to Cyber-Physical System Security: A Cross-Layer Perspective. IEEE Transactions on Multi-Scale Computing Systems 3: 215227.
    [80] Puttonen J, Afolaranmi SO, Moctezuma LG (2015) Security in Cloud-based Cyber-physical Systems. In: 201510th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp. 671676.
    [81] Ntalampiras S (2016) Automatic identification of integrity attacks in cyber-physical systems. Expert Syst Appl 58: 164173.
    [82] Altawy R, Youssef AM (2016) Security Tradeoffs in Cyber Physical Systems: A Case Study Survey on Implantable Medical Devices. IEEE Access 4: 959979.
    [83] Konstantinou C, Maniatakos M, Saqib F, et al. (2015) Cyber-Physical Systems: A Security Perspective. In: 2015 20th IEEE European Test Symposium (ETS), pp. 18. IEEE.
    [84] Teixeira A, Pérez D, Sandberg H (2012) Attack Models and Scenarios for Networked Control Systems. In: Proceedings of the 1st international conference on High Confidence Networked Systems, pp. 5564. ACM.
    [85] Gollmann D, Gurikov P, Isakov A, et al. (2016) Cyber-Physical Systems Security Experimental Analysis of a Vinyl Acetate Monomer Plant. In: Proceedings of the1st ACM Workshop on Cyber-Physical System Security, pp. 112. ACM.
    [86] DeSmit Z, Elhabashy AE, Wells LJ, et al. (2016) Cyber-Physical Vulnerability Assessment in Manufacturing Systems. Procedia Manufacturing 5: 10601074.
    [87] Rahman MS, Mahmud MA, Oo AMT, et al. (2016) Multi-Agent Approach for Enhancing Security of Protection Schemes in Cyber-Physical Energy Systems. IEEE Transactions on Industrial Informatics 13: 436447.
    [88] Steger M, Karner M, Hillebrand J, et al. (2016) A Security Metric for Structured Security Analysis of Cyber-Physical Systems Supporting SAE J3061. In: 2016 2nd International Workshop on Modelling, Analysis, and Control of Complex CPS (CPS Data), pp. 16.
    [89] Burton J, Dubrawsky I, Osipov V, et al. (2003) Secure Intrusion Detection Systems. Syngress Publishing, Inc., Rockland, USA.
    [90] Rehman RU (2003) Intrusion Detection Systems with Snort Advanced IDS Techniques Using Snort, Apache, MySQL, PHP, and ACID. Prentice Hall Professional.
    [91] Mitchell R, Chen IR (2014) A Survey of Intrusion Detection Techniques for Cyber-Physical Systems. ACM Computing Surveys (CSUR) 46: 55.
    [92] Scarfone K, Mell P (2007) Guide to Intrusion Detection and Prevention Systems (IDPS): Recommendations of the National Institute of Standards and Technology. NIST No. Special Publication (NIST SP)-800-94.
    [93] Alcaraz C, Cazorla L, Fernandez G (2014) Context-Awareness Using Anomaly-Based Detectors for Smart Grid Domains. In: International Conference on Risks and Security of Internet and Systems, pp. 1734. Springer, Cham.
    [94] Abbas W, Laszka A, Vorobeychik Y, et al. (2015) Scheduling Intrusion Detection Systems in Resource-Bounded Cyber- Physical Systems. In: Proceedings of the 1st ACM Workshop on Cyber-Physical Systems-Security and/or Privacy, pp. 5566. ACM.
    [95] Naghnaeian M, Hirzallah N, Voulgaris PG (2015) Dual Rate Control for Security in Cyber-physical Systems. In: 2015 54th IEEE Conference on Decision and Control (CDC), pp. 141451420.
    [96] Ivanov R, Pajic M, Lee I (2016) Attack-Resilient Sensor Fusion for Safety-Critical Cyber-Physical Systems. ACM Transactions on Embedded Computing Systems (TECS) 15: 21.
    [97] Zimmer C, Bhat B, Mueller F, et al. (2010) Time-Based Intrusion Detection in Cyber-Physical Systems. In: Proceedings of the1st ACM/IEEE International Conference on Cyber-Physical Systems, pp. 109118. ACM.
    [98] Joseph AD, Laskov P, Roli F, et al. (2013) Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371), In: Dagstuhl Manifestos, Vol. 3. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
    [99] Nguyen TTT, Armitage GJ (2008) A survey of techniques for internet traffic classification using machine learning. IEEE Commun Surv Tut 10: 5676.
    [100] Paridari K, Mady AE-D, La Porta S, et al. (2016) Cyber-Physical-Security Framework for Building Energy Management System. In: 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS), p. 18. IEEE.
    [101] Udd R, Asplund M, Nadjm-Tehrani S, et al. (2016) Exploiting Bro for Intrusion Detection in a SCADA System. In: Proceedings of the2nd ACM International Workshop on Cyber-Physical System Security, pp. 4451.
    [102] Chinchore A, Xu G, Jiang F (2016) Classifying Sybil in MSNs using C4.5. In: 2016International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), pp. 16.
    [103] Palenzuela F, Shaffer M, Ennis M, et al. (2016) Multilayer Perceptron Algorithms for Cyberattack Detection. In: 2016IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), pp. 248252.
    [104] Livadas C, Walsh R, Lapsley DE (2006) Using Machine Learning Techniques to Identify Botnet Traffic. In: LCN, pp. 967974.
    [105] DeLoach J, Caragea D, Ou X (2016) Android Malware Detection with Weak Ground Truth Data. In: 2016IEEE International Conference on Big Data (Big Data), pp. 34573464.
    [106] Yerima SY, Sezer S, Muttik I (2015) High accuracy android malware detection using ensemble learning. IET Information Security 9: 313320.
    [107] Song C, Perez-Pons A, Yen KK (2016) Building a Platform for Software-Defined Networking Cybersecurity Applications. In: 2016 15th IEEE International Conference on Machine Learning and Applications, pp. 482487.
    [108] Jianguo J, Qi B, Zhixin S, et al. (2016) Botnet Detection Method Analysis on the Effect of Feature Extraction. In: 2016IEEE Trustcom/BigDataSE/ISPA, pp. 18821888.
    [109] Cohena A, Nissima N, Rokacha L, et al (2016) SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods. Expert Syst Appl 63: 324343.
    [110] Goh KL, Singh AK (2015) Comprehensive Literature Review on Machine Learning Structures for Web Spam Classification. Procedia Computer Science 70: 434441.
    [111] Buczak AL, Guven E (2016) A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Commun Surv Tut 18: 11531176.
    [112] Huda S, Miah S, Hassan MM, et al. (2017) Defending unknown attacks on cyber-physical systems by semi-supervised approach and available unlabeled data. Inform Sciences 379: 211228.
    [113] Seiger R, Keller C, Niebling F, et al. (2015) Modelling complex and flexible processes for smart cyber-physical environments. Journal of Computational Science 10: 137148.
    [114] Kroiß C, Bureš T (2016) Logic-based modeling of information transfer in cyberphysical multi-agent systems. Future Gener Comp Sy 56: 124139.
    [115] Khaitan SK, McCalley JD (2015) Design Techniques and Applications of Cyberphysical Systems: A Survey. IEEE SYST J 9: 350365.
    [116] Petnga L, Austin M (2016) An ontological framework for knowledge modeling and decision support in cyber-physical systems. Adv Eng Inform 30: 7794.
    [117] Kelly RA, Jakeman AJ, Barreteau O, et al. (2013) Selecting among five common modelling approaches for integrated environmental assessment and management. Environ Modell Softw 47: 159181.
    [118] Strasser U, Vilsmaier U, Prettenhaler F, et al. (2014) Coupled component modelling for inter- and transdisciplinary climate change impact research: Dimensions of integration and examples of interface design. Environ Modell Softw 60: 180187.
    [119] Burmester M, Magkos E, Chrissikopoulos V (2012) Modeling security in cyberphysical systems. Int J Crit Infr Prot 5: 118126.
    [120] Marrone S, Rodríguez RJ, Nardone R, et al. (2015) On synergies of cyber and physical security modelling in vulnerability assessment of railway systems. Comput Electr Eng 47: 275285.
    [121] Akella R, Tang H, McMillin BM (2010) Analysis of information flow security in cyberphysical systems. Int J Cri Infr Prot 3: 157173.
    [122] Wan J, Canedo A, Al Faruque MA (2015) Security-Aware Functional Modeling of Cyber-Physical Systems. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 14. IEEE.
    [123] Amullen EM, Shetty S, Keel LH (2016) Model-based resilient control for a multi-agent system against Denial of Service attacks. In: 2016 World Automation Congress (WAC), pp. 16.
    [124] Tsigkanos C, Pasquale L, Ghezzi C, et al. (2015) Ariadne: Topology Aware Adaptive Security for Cyber-Physical Systems. In: Proceedings of the 37th IEEE International Conference on Software Engineering, pp. 729732. IEEE Press.
    [125] Kriaa S, Pietre-Cambacedes L, Bouissou M, et al. (2015) A survey of approaches combining safety and security for industrial control systems. Reliab Eng Syst Safe 139: 156178.
    [126] Kornecki AJ, Subramanian N, Zalewski J (2013) Studying Interrelationships of Safety and Security for Software Assurance in Cyber-Physical Systems: Approach Based on Bayesian Belief Networks. In: 2013 Federated Conference on Computer Science and Information Systems, pp. 13931399.
    [127] Bak S, Abad FAT, Huang Z, et al. (2013) Using Run-Time Checking to Provide Safety and Progress for Distributed Cyber-Physical Systems. In: 2013IEEE 19th International Conference on Embedded and Real-Time Computing Systems and Applications, pp. 287296.
    [128] Kuschnerus D, Bilgic A, Bruns F, et al. (2015) A Hierarchical Domain Model for Safety-Critical Cyber-Physical Systems in Process Automation. In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), pp. 430436.
    [129] Knight JC (2002) Safety critical systems: challenges and directions. In: Proceedings of the24th International Conference on Software Engineering, pp. 547550.
    [130] Neuman C (2009) Challenges in Security for Cyber-Physical Systems. In: DHS Workshop on Future Directions in Cyber- Physical Systems Security, pp. 2224.
    [131] Sun H, Liu J, Chen X, et al. (2015) Specifying Cyber-Physical System Safety Properties with Metric Temporal-Spatial Logic. In: 2015 Asia-PacificSoftware Engineering Conference (APSEC), pp. 254260.
    [132] Baldoni R, Montanari L, Rizzuto M (2015) On-line failure prediction in safety-critical systems. Future Gener Comp Sy 45: 123132.
    [133] Masrur A, Kit M, Matena V, et al. (2016) Component-based design of cyber-physical applications with safety-critical requirements. Microprocess Microsy 42: 7086.
    [134] Nguyen HH, Tan R, Yau DKY (2014) Safety-Assured Collaborative Load Management in Smart Grids. In: 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), pp. 151162.
    [135] Weissnegger R, Schuss M, Kreiner C, et al. (2016) Simulation-based Verification of Automotive Safety-CriticalSystems based on EAST-ADL. Procedia computer science 8: 245252.
    [136] Ishigooka T, Saissi H, Piper T, et al. (2014) Practical Use of Formal Verification for Safety Critical Cyber-Physical Systems: A Case Study. In: 2014 IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, pp. 712.
    [137] Piesik E, Śliwiński M, Barnert T (2016) Determining and verifying the safety integrity level of the safety instrumented systems with the uncertainty and security aspects. Reliab Eng Syst Safe 152: 259272.
    [138] Zheng X, Julien C, Kim M, et al. (2015) Perceptions on the State of the Art in Verification and Validation in Cyber- Physical Systems. IEEE Syst J 11: 26142627.
    [139] Fallah YP, Huang CL, Sengupta R, et al. (2010) Design of Cooperative Vehicle Safety Systems Based on Tight Coupling of Communication, Computing and Physical Vehicle Dynamics. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, pp. 159167.
    [140] Schmittner C, Ma Z, Schoitsch E, et al. (2015) A Case Study of FMVEA and CHASSIS as Safety and Security Co- Analysis Method for Automotive Cyber-physical Systems. In: Proceedings of the 1st ACM Workshop on Cyber-Physical System Security, pp. 6980.
    [141] Adhikari U, Morris TH, Pan S (2014) A Causal Event Graph for Cyber-Power System Events Using Synchrophasor. In: 2014 IEEE PES General Meeting|Conference & Exposition, pp. 15. IEEE.
    [142] Al-Hammadi Y, Aickelin U (2010) Behavioural Correlation for Detecting P2P Bots. In: 2010 2nd International Conference on Future Networks (ICFN), pp. 323327.
    [143] Petrovski A, Rattadilok P, Petrovski S (2015) Designing a Context-Aware Cyber Physical System for Detecting Security Threats in Motor Vehicles. In: Proceedings of the 8th International Conference on Security of Information and Networks, pp. 267270.
    [144] Skormin V, Dolgikh A, Birnbaum Z (2014) The Behavioral Approach to Diagnostics of Cyber-Physical Systems. In: 2014 IEEE AUTOTEST, pp. 2630. IEEE.
    [145] Wang A, Iyer M, Dutta R, et al. (2013) Network Virtualization: Technologies, Perspectives, and Frontiers. J Lightwave Technol 31: 523537.
    [146] Wardell DC, Mills RF, Peterson GL, et al. (2016) A Method for Revealing and Addressing Security Vulnerabilities in Cyber-Physical Systems by Modeling Malicious Agent Interactions with Formal Verification. Procedia Computer Science 95: 2431.
    [147] McAfee Special report: How Collaboration Can Optimize Security Operations. The new secret weapon against advanced threats, 2016. Available from: https://abyteofcyber.com/DOCS/rp-soc-collaboration-advanced-threats.pdf.
    [148] Mrabet ZE, Kaabouch N, Ghazi HE, et al. (2018) Cyber-security in smart grid: Survey and challenges. Comput Electr Eng 67: 469482.
    [149] Leeds M, Atkison T (2016) Preliminary Results of Applying Machine Learning Algorithms to Android Malware Detection. In: 2016International Conference on Computational Science and Computational Intelligence, pp. 10701073.
    [150] Suh-Lee C, Jo J-Y, Kim Y (2016) Text Mining for Security Threat Detection Discovering Hidden Information in Unstructured Log Messages. In: 2016 IEEE Conference on Communications and Network Security (CNS), pp. 252260.
    [151] Morales-Ortega S, Escamilla-Ambrosio PJ, Rodríguez-Mota A, et al. (2016) Native Malware Detection in Smartphones with Android OS Using Static Analysis, Feature Selection and Ensemble Classifiers. In: 2016 11th International Conference on Malicious and Unwanted Software (MALWARE), pp. 18.
    [152] Hu W, Liao Y, Vemuri VR (2003) Robust Anomaly Detection Using Support Vector Machines. In: Proceedings of the International Conference on Machine Learning, pp. 282289.
    [153] Gouveia A, Correia M (2016) Feature Set Tuning in Statistical Learning Network Intrusion Detection. In: 2016 IEEE 15th International Symposium on Network Computing and Applications, pp. 6875.
    [154] Kamarudin MH, Maple C, Watson T, et al. (2015) Packet Header Intrusion Detection with Binary Logistic Regression Approach in Detecting R2L and U2R attacks. In: 20154th International Conference on Cyber Security, Cyber Warfare, and Digital Forensic, pp. 101106.
    [155] Alshammari R, Zincir-Heywood AN (2015) Identification of VoIP encrypted traffic using a machine learning approach. Journal of King Saud University Computer and Information Sciences 27: 7792.
    [156] Li Y, Guo L (2007) An Efficient Network Anomaly Detection Scheme Based on TCM-KNN Algorithm and Data Reduction Mechanism. In: 2007IEEE SMC Information Assurance and Security Workshop, pp. 221227.
    [157] Wang W, Lee XD, Hu AL, et al. (2013) Co-Training based Semi-Supervised Web Spam Detection. In: 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 789793.
    [158] Baig M, El-Alfy E-SM, Awais MM (2014) Intrusion Detection Using a Cascade of Boosted Classifiers (CBC), In: 2014 International Joint Conference on Neural Networks, pp. 13861392.
    [159] Farid DM, Harbi N, Rahman MZ (2010) Combining Naïve Bayes and Decision Tree for Adaptive Intrusion Detection. International Journal of Network Security & Its Applications (IJNSA) 2: 1225.
    [160] Stein G, Chen B, Wu AS, et al. (2005) Decision Tree Classifier For Network Intrusion Detection With GA-based Feature Selection. In: Proceedings of the 43rd annual Southeast regional conference, pp. 136141.
    [161] Kumar PAR, Selvakumar S (2013) Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems. Comput Commun 36: 303319.
    [162] Hu W, Hu W, Maybank SJ (2008) AdaBoost-Based Algorithm for Network Intrusion Detection. Systems Man and Cybernatics 38: 577583.
    [163] Laskov P, Schäfer C, Kotenko I, et al. (2004) Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines. Praxis der Informationsverarbeitung und Kommunikation 27: 228236.
    [164] Zhang J, Luo X, Perdisci R, et al. (2011) Boosting the Scalability of Botnet Detection Using Adaptive Traffic Sampling. In: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, pp. 124134.
    [165] Syarif I, Zaluska E, Prugel-Bennett A, et al. (2012) Application of Bagging, Boosting and Stacking to Intrusion Detection. In: MLDM'12 Proceedings of the8th international conference on Machine Learning and Data Mining in Pattern Recognition, pp. 593602.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(11946) PDF downloads(3430) Cited by(26)

Figures and Tables

Figures(7)  /  Tables(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog