# A New Pseudorandom Bit Generator Based on Mixing Three-Dimensional Chen Chaotic System with a Chaotic Tactics

Authors: Xuan Huang; Lingfeng Liu; Xiangjun Li; Minrong Yu; Zijie Wu
Journal: Complexity
 (2019)

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## Abstract

In this paper, a new chaotic system is proposed based on mixing three-dimensional Chen chaotic system with a chaotic tactics. This new system is proved to be chaotic under Wiggins’ chaos definition and can generate chaotic sequences with high complexity. Furthermore, we propose a new pseudorandom bit generator (PRBG) based on this new system. A coding algorithm is used to make the sequences uniform. Both statistical and security tests are provided to show the generated sequences are with good randomness and high complexity to withstand attacks.


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## Body

## 1. Introduction

Chaos is an interesting nonlinear physical phenomenon which exists in the natural world. The first mathematical definition of chaos was proposed by Li and York in [1], which is widely used for one-dimensional iterative map. After then, for different kinds of systems, some other chaos definitions have been proposed. Among them, the most widely used definitions are Devaney’s chaos, Wiggins’ chaos, and Smale’s chaos. All these definitions focus on the different performances of chaos; e.g., Li-York’s chaos focuses on the diffusion of chaotic trajectories, while Devaney’s chaos focuses on the topological properties. Till now, there is still no accurate mathematical definition which can cover all the performances of chaos. However, the lack of general definition does not hinder the application of chaos. On the contrary, chaos has been widely used in almost all fields of science, engineering, and even humanities [2–6].Pseudorandom bit sequence (PRBS) plays an important role in many fields, such as spread spectrum communication, numerical simulation, and cryptography [7, 8]. Recently, chaotic systems are regarded as effective nonlinear sources in generating PRBS for their wonderful properties, including sensitivity on initial conditions and parameters, ergodicity, long-term unpredictability, and pseudorandomness [9–11]. Overall, the one-dimensional chaotic map is the most widely used chaotic system for designing PRBG. Addabbo proposed a random bit generator based on one-dimensional piecewise-linear chaotic map [12]. In [13], a new pseudorandom number generator on graphics processing units is presented, which is based on the chaotic iterations. Szczepanski presented a method of generating pseudorandom numbers by applying discrete chaotic dynamical systems which are ergodic or preferably mixing [14]. However, some studies show that the one-dimensional chaotic system is not secure enough, which may be attacked by the phase space reconstruction technology [15, 16].Comparatively speaking, PRBG based on high-dimensional chaotic system is more secure in this sense and can offer a wider key space. In [17], the statistics and complexity of chaotic pseudorandom sequences generated by Lorenz system are analyzed. Shrestha presented an ultra-low-power, biologically inspired pseudo-random number generator based on the Hodgkin-Huxley silicon neuron circuit [18]. This kind of high-dimensional chaos-based PRBG is based on only one-dimensional chaotic orbit. In [19], Hu proposed a PRBG based on three-dimensional Chen chaotic system. This PRBG is based on a combination of three coordinates of chaotic orbit. However, due to the combination strategy being quite simple, [20] points out that this PRBG is still not secure enough.Motivated by the idea of [19], in this paper, we induce a chaotic tactics to select different dimensional coordinates of the three-dimensional Chen chaotic orbit. This chaotic tactics changes the three-dimensional chaotic orbit into one-dimensional orbit. This chaotic mixing tactics is obviously more secure than the fixed mixing tactics in [19]. We prove that the generated new system is still chaotic mathematically and show that the sequence generated by this new system is more complex than any dimensional variable of original Chen system numerically. Furthermore, we proposed a PRBG based on this new chaotic system. Both statistical and security analysis show that the generated PRBS is with good statistical characteristics and strong capacity to withstand attacks.In the numerical tests, we use the fourth-order Runge-Kutta method for the numerical simulations of the Chen chaotic system to keep the numerical results as near as possible to the original system behavior. Our PRBG has several advantages. The generated sequence has a more complex behavior than any dimensional variables of original Chen system, which complicates the revealing of information from the orbits. Since we do not utilize any particular properties of Chen chaotic system, many other differential equation-based chaotic systems can also be used, which means that this method is not limited by the choice of system. Furthermore, our generated PRBG can pass the TestU01 statistical test suite, which is better than many other proposed chaotic PRBGs.The rest of this paper is organized as follows. In Section2, we present a new chaotic system based on Chen chaotic system with chaotic tactics. We prove that the proposed system is strict chaotic under Wiggins’ chaos definition and analyze its complexity numerically in Section 3. In Section 4, we propose a PRBG based on this new system. The statistical and security tests are presented in Sections 5 and 6, respectively. Finally, Section 7 concludes the whole paper.


## 2. New System Based on Chen Chaotic System with Chaotic Tactics

The mathematical equation of Chen system can be described as follows [21]:(1)F≔dxdt=ay-xdydt=c-ax-xz+cydzdt=xy-bzwherea,b, andc refer to the control parameters. Equation (1) will be chaotic with some suitable parameters. For example, lettinga = 35,b = 3, andc = 28, the chaotic attractor of this system is shown in Figure 1.Figure 1
Chen attractor witha = 35,b = 3, andc = 28.The dynamical complexity of Chen chaotic system is rather high. The Lyapunov exponent of Chen system is about 2.168, which is larger than other most widely used chaotic systems. However, the complexity of each dimensional variable is rather low. Some numerical tests will be taken in the next section. Therefore, in order to generate one-dimensional sequences with high complexity from Chen system, in this paper, we present a chaotic tactics. Actually, this chaotic tactics is used to mix the three-dimensional state variables of Chen chaotic system by using chaotic number sequences. The following zero-mean Logistic chaotic map is used here.(2)pi+1=1-2pi2wherepi is the state variable. By setting an initial valuep0, we can generate a real-valued sequence:p0,p1, …,pm…. All these values locate in the interval [-1,1] if the initial valuep0 is selected in this domain as well.Divide the interval[-1,1] intoN subintervalsτi,i = 0, 1,...,N - 1. Denoteτi = [ti,ti+1),i= 0, 1, …,N- 2, andτN-1 = [tN-1,tN], where(3)ti=-cos⁡iNπ.Letα = {τ0,τ1, …, τN-1} be a finite measurable partition of [-1,1]. Ifpj is located in the subintervalτi, we change this real value into integeri. Then, the real-valued sequence:p0,p1, …,pm… will be changed into an integer sequence {bi} as follows:(4)bi=0,pi∈t0,t11,pi∈t1,t2⋯N-1,pi∈tN-1,tNFrom [22] we know, the integer sequence is uniformly distributed. In this paper, we chooseN = 3, and the interval [-1,1] is divided into three subintervals. The output signals of (1) can be mixed into one-dimensional sequence {si} by the integer sequence {bi} as(5)si=HxiT,yiT,ziT=xiT,bi=0yiT,bi=1ziT,bi=2whereT is the sampling interval of Chen chaotic system. We always setT= 0.01 through this paper. Denotex(iT) ≔xi,y(iT) ≔yi,z(iT) ≔zi in this paper. The sequence {si} can be considered as the output signal of systemG, whose schematic is briefly illustrated in Figure 2.Figure 2
The schematic of systemG.


## 3. Chaos and Complexity Performances

In this section, we first prove that the systemG is chaotic under Wiggins’ chaos definition and then evaluate the complexity of this new system by using approximate entropy (ApEn) and permutation entropy (PE).A system is chaotic under Wiggins’ chaos definition if and only if this system has sensitive dependence on initial conditions and transitivity, which can be defined as follows.Definition 1 (sensitive dependence on initial conditions, [23]).
F: S → S is said to have sensitive dependence on initial conditions if there existsε> 0, such that for anyx∈S and any neighborhoodUofx, there existsy∈U and an integern> 0 such that(6)Fnx-Fny>εwhereFn refers to the trajectory of the system aftern time iterations.Definition 2 (transitivity, [23]).
F: S → Sis topological transitive; i.e., for any open subsetsU,V∈S, there exists an integerk > 0 such that (7)FkU∩V≠⌀Theorems4 and 5 will show that systemG is mathematically chaotic. Before the proof, the following assumption is needed.Assumption 3.
The sequence{bi} is with good independence.In order to prove this assumption, we numerically calculated the correlation function of sequence{bi}, as shown in Figure 3. From Figure 3 we know that the sequence {bi} has a good independence and Assumption 3 holds.Figure 3
Correlation tests of sequence{bi}.Theorem 4.
The new systemG has sensitive dependence on initial conditions.Proof.
As the Chen systemF is chaotic, then, for its initial condition (x0,y0,z0) and its neighborV, there exist (x0’,y0’,z0’)∈V,n1,n2,...,nk∈Z+ andε> 0; we have(8)xnj,ynj,znj-xnj′,ynj′,znj′>εHere, · denotes the Euclidean norm,j = 1, 2,...,k.Therefore, at least one equation holds in the following three equations for differentj:(9)xnj-xnj′>ε3=ε1(10)ynj-ynj′>ε3=ε1(11)znj-znj′>ε3=ε1Without loss of generality, we assume (9) holds forj =j1,j2,...,jl, wherej1,j2,...,jl are selected from 1 tokand are different from each other.
As we known, the integer sequence{bi} is uniformly distributed. Therefore, the probability ofbi = 0 equals 1/3 approximately. Ask andl approach infinity, according to the uniform distribution of {bi} and Assumption 3, we have(12)Probbnjp≠0∣1≤p≤l=23l→0Then, there exists a value njp, 1 ≦p ≦l, which satisfies(13)bnjp=0Thus, we have(14)Gnjps0-Gnjps0′=snjp-snjp′=xnjp-xnjp′>ε1which concludes our proof.Theorem 5.
The new systemG has transitivity.Proof.
The above theorem is equivalent to the following: for anys0∈U and an open subsetV, there exists a natural numberk such thatGk(s0)=sk∈V.
F is a chaotic system, soF is transitive. Then, for its initial condition (x0,y0,z0)∈UF (UF is the attractor of systemF), there exist infinite number of natural numberski,i=1, 2,...,n, (15)Fkix0,y0,z0∈VFwhereVF can be any subset ofUF,. Then, fori=1, 2,...,n, we have(16)xki∈Vx,yki∈Vx,zki∈VxwhereVF =Vx ×Vy ×Vz. Without loss of generality, setb0 = 0. Asncan approach to infinity, according to the uniform distribution of {bi} and Assumption 3, we have(17)Probbki≠0∣1≤i≤n=23n→0Then, there exists a valuekj, 1 ≦j ≦n, which satisfies(18)bkj=0Thus, for anys0∈U, we have(19)Gkjs0=skj=xkj∈VxFor the arbitrariness of subsetVF, the subsetVx is arbitrary as well, which concludes our proof.Next, we use ApEn and PE to analyze the complexity of{si}. The results show that our mixed sequence {si} is more complex than the originalx,y,z-dimensional state variables of Chen chaotic system.ApEn is a complexity measure of time-series, which is introduced by Pincus in [24]. ApEn measures the probability of the new pattern generated in the sequences with the embedding dimension growing. The larger the probability is, the more complex the sequence is. Here, we calculate the ApEn of the sequence {si} and then compare with the ApEn ofx-dimensional,y-dimensional, andz-dimensional state variables of Chen chaotic system. The results are shown in Figure 4(a). From Figure 4(a) we can find that the ApEn of sequence {si} is much larger than the ApEn of any dimensional variable of Chen system, which indicates that the sequence {si} is much more complex in this sense.Figure 4
The complexity of sequence{si} andx-dimensional,y-dimensional, andz-dimensional variables of Chen system witha = 35,b = 3, andc = 28 (a) ApEn; (b) PE.
(a) (b)PE is a complexity measure which is introduced in [25] by Bandt and Pompe. PE compared the size of some consecutive values in the sequence and summed up different order types and then used Shannon’s entropy to measure the uncertainty of this ordering. As mentioned, PE is easily implemented and robust to noise, which has received wide attentions [26]. In this test, we select orderm = 6 and embedding delayD = 2 as suggested in [25]. The test results of sequence {si} and its comparison tox-dimensional,y-dimensional, andz-dimensional state variables of Chen chaotic system are shown in Figure 4(b). The results also show that the sequence {si} has greatly improved the complexity of each dimensional variable of Chen system.


## 4. The New PRBG

As shown in Section3, the systemG is chaotic and with high complexity. In this section, we propose a new PRBG based on systemG.The distribution of the Chen chaotic system is not uniform [19]. Therefore, we first make some effective changes to enhance the statistical properties of sequence {si}. Based on a large number of experiments, we propose the following coding algorithm:(20)ti=7000·si+100mod256,bi=07000·si+110mod256,bi=17000·si+120mod256,bi=2By using this algorithm, the output sequence {si} is coded to be uniform integer sequence {ti}. The sequence {ti} is in the finite fieldF256, whereF256 = {0, 1, 2,..., 255}. Finally, changeti into 8-bit binary number, (e.g., 0 is encoded into 00000000 and 255 is encoded into 11111111), and then, we can get our PRBS.The main frame of our PRBG is shown in Figure5. This PRBG is based on mixing three-dimensional state variables of Chen chaotic system. The mixture method is based on the zero-mean Logistic chaotic map. Furthermore, (20) improves its statistical characteristics. We will show that our PRBG is with good randomness in Section 5. Furthermore, for a sample integerT, this PRBG will generate 8-bit data, which is much faster than the original method with 1 b/T.Figure 5
The main frame of new PRBG.


## 5. Statistical Tests

Statistical tests are essential for PRBG which is proposed in cryptographic applications. In this section, we will make some famous statistical tests to evaluate our PRBS. In these tests, the parameters of Chen system are selected asa = 35,b = 3, andc = 28, the initial value are selected asp0 = 0.4231, (x0,y0,z0) = (1.3452, 4.2134, 6.7368).


### 5.1. Frequency Spectral Analysis

For an ideal pseudorandom sequence, there is no center frequency. While for a period sequence, a center frequency exists. The frequency spectral analysis of our PRBS is plotted in Figure6. Obviously, there is no center frequency in the spectrum, which indicates that our PRBG is not periodic.Figure 6
Frequency spectrum analysis of the PRBS.


### 5.2. Beker and Piper’s Statistical Tests

Beker and Piper’s statistical tests include the frequency test, serial test, poker test, runs test, and correlation test [27]. These five tests are famous and can be found in many different works, e.g., [28]. The test results of the first four tests are shown in Table 1, whereM refers to the threshold. The correlation tests are shown in Figure 7. Both results show that our PRBS are with good randomness.Table 1
Results of Beker and Piper’s statistical test.
Frequency test Serial Test Poker test Runs test m=2 m=3 m=4 40000 b 0.079 2.964 2.896 6.544 14.284 1.692 60000 b 0.016 2.583 2.567 4.972 11.457 1.471 80000 b 0.009 2.049 1.479 4.579 9.130 1.336 100000 b 0.006 1.847 1.137 4.069 7.621 1.249 120000 b 0.004 1.397 1.018 3.217 5.987 1.014 T 3.84 5.99 7.81 14.07 26.00 1.96Figure 7
Correlation tests of PRBS.


### 5.3. NIST Statistical Tests

Besides the Beker and Piper’s statistical test suite, the NIST test suite may be the most widely used statistical tests [29]. The NIST test suite includes 16 tests which cover almost all the features of random sequences. In this test, the significance level of each test in NIST is set to 0.01, which means that 99% of test samples pass the tests if the random numbers are truly random. Here, we generate 1000 different binary sequences randomly with different initial values; the NIST test results are shown in Table 2. From Table 2 we can find that our PRBS have passed all the test suite and can be regarded with good randomness.Table 2
The passing ratio and the uniformity of the P-values of each test from the NIST suite.
Test index Passing ratio P-value Results Approximate entropy 0.998 0.35974 SUCCESS Block frequency 0.999 0.35479 SUCCESS Cumulative sums 0.996 0.23154 SUCCESS FFT 0.998 0.65942 SUCCESS Frequency 0.999 0.54871 SUCCESS Linear complexity 0.997 0.62321 SUCCESS Random excursions 0.999 0.12654 SUCCESS Random excursions variant 0.996 0.32648 SUCCESS Longest runs of ones 0.999 0.15493 SUCCESS Overlapping template of all ones 0.997 0.21489 SUCCESS Rank 0.998 0.36541 SUCCESS Runs 0.998 0.21582 SUCCESS Serial 0.999 0.21648 SUCCESS Universal statistical 0.996 0.11713 SUCCESS Lempe-Ziv Compression Test 0.998 0.43546 SUCCESS


### 5.4. TestU01 Statistical Tests

In all the statistical test suites for random sequences, the TestU01 test is the most stringent testing. It is very difficult to pass all the tests successfully for PRBGs. In the TestU01 tests, three different crush type batteries are provided: there are Small-Crush, Crush, and Big-Crush, which evaluate sequences with 229 bit, 235 bit, and 238 bit, respectively. Based on the storage space of our computer, we only use Small-Crush and Crush batteries to evaluate the randomness of our PRBG. The test results are shown in Table 3. From Table 3, we can find that all the test suites have been passed, which performs better than other proposed chaotic PRBGs in [30–33].Table 3
TestU01 test results – Failure counts.
System Parameters Small-Crush(15) Crush(144) Our PRBG Standard 0 0 Ref. [19] Standard 1 6 Ref. [30] Standard 14 122 Ref. [31] Standard 2 12 Ref. [32] Standard 3 19 Ref. [33] Standard 3 15


## 5.1. Frequency Spectral Analysis

For an ideal pseudorandom sequence, there is no center frequency. While for a period sequence, a center frequency exists. The frequency spectral analysis of our PRBS is plotted in Figure6. Obviously, there is no center frequency in the spectrum, which indicates that our PRBG is not periodic.Figure 6
Frequency spectrum analysis of the PRBS.


## 5.2. Beker and Piper’s Statistical Tests

Beker and Piper’s statistical tests include the frequency test, serial test, poker test, runs test, and correlation test [27]. These five tests are famous and can be found in many different works, e.g., [28]. The test results of the first four tests are shown in Table 1, whereM refers to the threshold. The correlation tests are shown in Figure 7. Both results show that our PRBS are with good randomness.Table 1
Results of Beker and Piper’s statistical test.
Frequency test Serial Test Poker test Runs test m=2 m=3 m=4 40000 b 0.079 2.964 2.896 6.544 14.284 1.692 60000 b 0.016 2.583 2.567 4.972 11.457 1.471 80000 b 0.009 2.049 1.479 4.579 9.130 1.336 100000 b 0.006 1.847 1.137 4.069 7.621 1.249 120000 b 0.004 1.397 1.018 3.217 5.987 1.014 T 3.84 5.99 7.81 14.07 26.00 1.96Figure 7
Correlation tests of PRBS.


## 5.3. NIST Statistical Tests

Besides the Beker and Piper’s statistical test suite, the NIST test suite may be the most widely used statistical tests [29]. The NIST test suite includes 16 tests which cover almost all the features of random sequences. In this test, the significance level of each test in NIST is set to 0.01, which means that 99% of test samples pass the tests if the random numbers are truly random. Here, we generate 1000 different binary sequences randomly with different initial values; the NIST test results are shown in Table 2. From Table 2 we can find that our PRBS have passed all the test suite and can be regarded with good randomness.Table 2
The passing ratio and the uniformity of the P-values of each test from the NIST suite.
Test index Passing ratio P-value Results Approximate entropy 0.998 0.35974 SUCCESS Block frequency 0.999 0.35479 SUCCESS Cumulative sums 0.996 0.23154 SUCCESS FFT 0.998 0.65942 SUCCESS Frequency 0.999 0.54871 SUCCESS Linear complexity 0.997 0.62321 SUCCESS Random excursions 0.999 0.12654 SUCCESS Random excursions variant 0.996 0.32648 SUCCESS Longest runs of ones 0.999 0.15493 SUCCESS Overlapping template of all ones 0.997 0.21489 SUCCESS Rank 0.998 0.36541 SUCCESS Runs 0.998 0.21582 SUCCESS Serial 0.999 0.21648 SUCCESS Universal statistical 0.996 0.11713 SUCCESS Lempe-Ziv Compression Test 0.998 0.43546 SUCCESS


## 5.4. TestU01 Statistical Tests

In all the statistical test suites for random sequences, the TestU01 test is the most stringent testing. It is very difficult to pass all the tests successfully for PRBGs. In the TestU01 tests, three different crush type batteries are provided: there are Small-Crush, Crush, and Big-Crush, which evaluate sequences with 229 bit, 235 bit, and 238 bit, respectively. Based on the storage space of our computer, we only use Small-Crush and Crush batteries to evaluate the randomness of our PRBG. The test results are shown in Table 3. From Table 3, we can find that all the test suites have been passed, which performs better than other proposed chaotic PRBGs in [30–33].Table 3
TestU01 test results – Failure counts.
System Parameters Small-Crush(15) Crush(144) Our PRBG Standard 0 0 Ref. [19] Standard 1 6 Ref. [30] Standard 14 122 Ref. [31] Standard 2 12 Ref. [32] Standard 3 19 Ref. [33] Standard 3 15


## 6. Security Analysis

Besides statistical tests, some other indexes are still required for a secure PRBG.


### 6.1. Key Space

In this PRBG, the initial valuesp0 and (x0,y0,z0), as well as the control parameters of Chen systema,b, andc, can be commonly used as the secret keys. Let the greatest accuracy be 2−64. The key space size is approximately equal to 2448 >> 2128. Therefore, the key space of our PRBG is large enough to resist brute-force attack. Furthermore, when comparing to other studies, our method is still superior. Under the same accuracy, the key spaces of the PRBG proposed in [19, 34–36] are 2384, 2400, 2199, and 2128, respectively, which are all smaller than ours.


### 6.2. Key Sensitivity

Chaotic system is greatly sensitive to the initial values. In this test, we change the initial value slightly to generate new binary sequences and then compare the difference between them. DenoteH to be the number of differences between the new sequence and the primary one by each bit. The results are shown in Tables 4–7, where Table 4 shows the differences whenp0 changes; Tables 5, 6, and 7 show the differences whenx0,y0, andz0 change, respectively. All the results show that the variance ratio of each bit is near 50%, which means that the PRBG is extremely sensitive to the secret key.Table 4
The difference between generated binary sequences with differentp0.
(0.4231, 0.4231 - 10−14) (0.4231, 0.4231 + 10−14) H 49.96% 50.04%Table 5
The difference between generated binary sequences with differentx0.
(1.3452, 1.3452 - 10−14) (1.3452, 1.3452 + 10−14) H 49.90% 49.94%Table 6
The difference between generated binary sequences with differenty0.
(4.2134, 4.2134 - 10−14) (4.2134, 4.2134 + 10−14) H 50.06% 50.08%Table 7
The difference between generated binary sequences with differentz0.
(6.7368, 6.7368 - 10−14) (6.7368, 6.7368 + 10−14) H 50.04% 49.92%


### 6.3. Linear Complexity

For an ideal random sequence, its linear complexity should ben/2, wheren is the sequence length. We test the linear complexity of our PRBS, and the result is shown in Figure 8. From Figure 8 we have that the linear complexity curve of our PRBS is approximately equal to the ideal linen/2, which indicates that our PRBG is with high linear complexity.Figure 8
Linear complexity of PRBS.


### 6.4. Resistance to Differential Attack

Differential attack, or chosen-plaintext attack, is to analyze the effects of a small difference in input keys on the difference of corresponding output sequences. A PRBG is said to resist differential attack if the output sequences are completely different for tiny difference on secret keys. As the results in Section6.2, we can have that our PRBG is extremely sensitive to the secret keys and can effectively resist differential attacks.


### 6.5. Algorithm Speed Analysis

In this paper, the algorithms are simulated by Matlab R2014a on the 64-bit computer with 3.3GHz CPU and 4GB memory. The computation precision is 2−64. The mean speed of our PRBG algorithm is about 21.5054 MB/s, which is equivalent to the algorithm in [35] and is much faster than the algorithms in [37, 38], as seen in Table 8. These results indicate that our PRBG is competitive for practical use.Table 8
Algorithm Speed analysis.
Different algorithms Speed (Mb/s) Our algorithm 21.5054 Ref. [35] 20.5383 Ref. [36] 1.7 Ref. [37] 0.4844 Ref. [38] 0.2352


## 6.1. Key Space

In this PRBG, the initial valuesp0 and (x0,y0,z0), as well as the control parameters of Chen systema,b, andc, can be commonly used as the secret keys. Let the greatest accuracy be 2−64. The key space size is approximately equal to 2448 >> 2128. Therefore, the key space of our PRBG is large enough to resist brute-force attack. Furthermore, when comparing to other studies, our method is still superior. Under the same accuracy, the key spaces of the PRBG proposed in [19, 34–36] are 2384, 2400, 2199, and 2128, respectively, which are all smaller than ours.


## 6.2. Key Sensitivity

Chaotic system is greatly sensitive to the initial values. In this test, we change the initial value slightly to generate new binary sequences and then compare the difference between them. DenoteH to be the number of differences between the new sequence and the primary one by each bit. The results are shown in Tables 4–7, where Table 4 shows the differences whenp0 changes; Tables 5, 6, and 7 show the differences whenx0,y0, andz0 change, respectively. All the results show that the variance ratio of each bit is near 50%, which means that the PRBG is extremely sensitive to the secret key.Table 4
The difference between generated binary sequences with differentp0.
(0.4231, 0.4231 - 10−14) (0.4231, 0.4231 + 10−14) H 49.96% 50.04%Table 5
The difference between generated binary sequences with differentx0.
(1.3452, 1.3452 - 10−14) (1.3452, 1.3452 + 10−14) H 49.90% 49.94%Table 6
The difference between generated binary sequences with differenty0.
(4.2134, 4.2134 - 10−14) (4.2134, 4.2134 + 10−14) H 50.06% 50.08%Table 7
The difference between generated binary sequences with differentz0.
(6.7368, 6.7368 - 10−14) (6.7368, 6.7368 + 10−14) H 50.04% 49.92%


## 6.3. Linear Complexity

For an ideal random sequence, its linear complexity should ben/2, wheren is the sequence length. We test the linear complexity of our PRBS, and the result is shown in Figure 8. From Figure 8 we have that the linear complexity curve of our PRBS is approximately equal to the ideal linen/2, which indicates that our PRBG is with high linear complexity.Figure 8
Linear complexity of PRBS.


## 6.4. Resistance to Differential Attack

Differential attack, or chosen-plaintext attack, is to analyze the effects of a small difference in input keys on the difference of corresponding output sequences. A PRBG is said to resist differential attack if the output sequences are completely different for tiny difference on secret keys. As the results in Section6.2, we can have that our PRBG is extremely sensitive to the secret keys and can effectively resist differential attacks.


## 6.5. Algorithm Speed Analysis

In this paper, the algorithms are simulated by Matlab R2014a on the 64-bit computer with 3.3GHz CPU and 4GB memory. The computation precision is 2−64. The mean speed of our PRBG algorithm is about 21.5054 MB/s, which is equivalent to the algorithm in [35] and is much faster than the algorithms in [37, 38], as seen in Table 8. These results indicate that our PRBG is competitive for practical use.Table 8
Algorithm Speed analysis.
Different algorithms Speed (Mb/s) Our algorithm 21.5054 Ref. [35] 20.5383 Ref. [36] 1.7 Ref. [37] 0.4844 Ref. [38] 0.2352


## 7. Conclusions

In this paper, firstly, we propose a new system based on mixing three-dimensional Chen chaotic system with a chaotic tactics and prove that this new system is chaotic under Wiggins’ chaos definition. Complexity tests show that the output of this new system is more complex than any dimensional variable of Chen system. Then, we propose a new PRBG based on this new system. A coding algorithm is used to make the sequences uniform. Furthermore, we do several numerical simulations to prove that the generated PRBS is with good randomness characteristics and strong capacity to withstand different attacks, as well as high speed for practical use.



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