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    • Hemlata Kohad, Prof. V.R.Ingle,Dr.M.A.Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1518-1523Security Level Enhancement In Speech Encryption Using Kasami Sequence Hemlata Kohad1 , Prof. V.R.Ingle2,Dr.M.A.Gaikwad3 1(Electronics Engineering, R.C.E.R.T Chandrapur ,India ) 2(Electronics Engineering,B.D.C.O.E.,Sevagram,India) 3(Electronics Engineering,B.D.C.O.E.Sevagram,India)ABSTRACT Speech scrambling techniques are used to chaotic system properties such as sensitive to initialscramble clear speech into unintelligible signal in condition,ergodicity,mixing property,deterministicorder to avoid eavesdropping. The residual dynamics and structural complexity can beintelligibility of the speech signal can be reduced considered analogous to the confusion ,diffusion withby reducing correlation among the speech small change in plaintext/secret keysamples. Security level enhancement in speechencryption system using large set of kasamisequence is described.The speech signal is dividedinto segments, the polynomial is generated byusing chaotic map.Using this polynomial we aregenerating large set of kasami sequence.Using thissequence as a key the speech signal is encryptedwith AES-128 bit algorithm.The evaluation iscarried out with respect to noise attack.Keywords– Chaotic map, Kasami sequence,,LLR,IS,Cepstrum Distance, SNRI. INTRODUCTION Fig.1 Block diagram of proposed system While transmitting information throughinsecure channel their might be unwanted disclosure With the rapid development of internet ,as well as unauthorized modification of data if that security is becoming an important issue in the storagedata is not properly secured.Therefore certain and communication.In many secure fields such asmechanism are needed to protect the information public safety and military department characters arewhen it is transmitted through any insecure channel. required to be encrypted. Technologies ofOne way to provide such protection is to convert the cryptography are the core of information security.intelligible data into unintelligible form prior to But most ciphers can be broken with enoughtransmission and such a process of conversion with a computational efforts.So the information security iskey is called encryption[15]. At the receiver side the facing challenge. So in recent years chaoticencrypted message is converted back to the original encryption has become a new research field.intelligible form by reverse process called decryption. Mathematically chaos refers to a very Public key encryption(asymmetric key) specific kind of unpredictable deterministic behaviorschemes are not suitable for the encryption of large that is very sensitive to its initial conditions. Chaoticamounts of data and time sensitive application like systems consequently appear disordered and random.speech conversation because of relatively slow Chaotic system is a kind of complicated non-linearperformance due to its complexity. To provide more dynamic system.It has perfect security with thesecurity, two levels or even three levels of encryption following properties good pseudo-random, orbitalsystem can be employed. In this paper ,an efficient inscrutability, extreme sensitivity about initial valuehigh secured encryption system is introduced this and the control parameter. Chaos theory is a field ofsystem uses chaotic map for the generation of study in mathematics ,physics and philosophy,polynomial ,using that polynomial large set kasami studying the behavior of dynamical systems that aresequence is generated ,which is used as key for the highly sensitive to initial conditions.This sensitivityencryption . is popularly referred to as the butterfly effect.Small differences in initial conditions widely diverging outcomes for chaotic systems rendering long termII. CHAOTIC MAP prediction impossible.This happens even though Encryption using chaotic map is much better these systems are deterministic ,meaning that theirthan traditional encryption methods. It makes use of future behavior is fully determined by their initial 1518 | P a g e
    • Hemlata Kohad, Prof. V.R.Ingle,Dr.M.A.Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1518-1523conditions ,with no random elements involved. In properties but these sequences are deterministic , soother words the deterministic nature of these systems these sequences are called Pseudo noise sequences.does not make them predictable. This behavior is There are different types of PN sequences.known as deterministic chaos or simply chaos.The 1.m-sequencelogistic map is a very simple mathematical model of 2. Gold sequencechaotic map . The simple modified mathematical 3.Kasami sequenceform of the logistic map is given asXn+1= λ *xn(1-xn) IV. KASAMI SEQUENCE Where xn is a state variable which lies in the There are two types of kasami sequenceinterval [0,1] and λ is called system parameterwhich 1. Small setcan have any value between 1 and 4. 2. Large set 1)Small set: for the degree n=4 the possible primitive polynomial x4+x+1, the generated PN sequence will be PN1 = 100010011010111 ,the second sequence generated i.e.PN2 is result after decimation of PN1 by factor q where q is 2n/2+1=5, PN2=101 101 101 101 101.Kasami sequence generated by PN1+Time shifted version of PN2 mod 2.The number of Kasami sequences generated are 2n/2=4 [19] small set Kasami Sequence is K={PN1, PN1⊕PN2, PN1⊕T1PN2,…. PN1⊕T2n/2-2PN2},Cross correlation of small set kasami sequence lie in 3 values {-1,-1+2n/2,-1-2n/2} Fig.2 Logistic map 2)Large set Kasami sequence :Large set contain all Using two logistic maps we are generating a the sequence of small set and gold sequence Thepolynomial i.e. the binary sequence that we are here correlation function for the sequences takes on the values {-t(n), -s(n),-1,s(n)-2, t(n)-2}consider as a coefficients of polynomial. And the Where t(n)= 1+ 2(n+2)/2output is given to the lkasami sequence generator andthe output is given to AES(128 bit) algorithm. s(n)=1/2t(n)+1 u KL(u,n,k,m)= vIII. PN SEQUENCE u⊕Tkv k=0,……,2n-2 Pseudo random binary sequences (PRBSs), u ⊕Tmw m=0,…….,2n/2-2also known as pseudo noise (PN), linear feedback v ⊕ Tmw m=0,….,2n/2-2shift register (LFSR) sequences or maximal length u ⊕ Tkv ⊕ Tmw k=0,….,2n-2binary sequences (m sequences), are widely used in m=0,…,2n/2-2digital communications. In a truly random sequencethe bit pattern never repeats[16-19].However, u= m sequence with period 2n-1generation of such a sequence is difficult and, more v= decimation of u by 1+2n+2 /2 with period 2n-1importantly, such a sequence has little use in practical W=decimation of u by 1+2n /2 with period 2n/2-1systems. Applications demand that the data appearrandom to the channel but be predictable to the user. Code set size of KL is 2 n/2(2n+1)This is where the PRBS becomes useful. A pseudorandom binary sequence is a semi-random sequencein the sense that it appears random within thesequence length, fulfilling the needs of randomness,but the entire sequence repeats indefinitely. To acasual observer the sequence appears totally random,however to a user who is aware of the way thesequence is generated all its properties are known.PN sequences have several interesting properties,which are exploited in a variety of applications.Because of their good autocorrelation two similar PNsequences can easily be phase synchronised, evenwhen one of them is corrupted by noise. A PNsequence is an ideal test signal, as it simulates the Fig 3. Large set Kasami sequence generatorrandom Characteristics of a digital signal and can beeasily generated. PN sequence are sequences of 1’sand 0’s .These sequences have random noise 1519 | P a g e
    • Hemlata Kohad, Prof. V.R.Ingle,Dr.M.A.Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1518-1523V. ADVANCED ENCRYPTION this method we minimize the expected value of the STANDARD squared error E[e2(n)], which yields the equation Advanced Encryption Standard or AES wasinvented by Joan Daemen and Vincent Rijmen, andaccepted by the US federal government in 2001 fortop secret approved encryption algorithms. It is also for 1 ≤ j ≤ p, where R is the autocerrelation of signalreferred to as Rijndael, as it is based off the Rijndael xn, defined asalgorithm. Reportedly, this standard has never beencracked. AES has three approved key length: 128 ,bits, 192 bits, and 256 bits. To try to explain the and E is the expected value. The above equations are called the normalprocess in simple terms, an algorithm starts with a equations orYule-Walker equations. In matrix formrandom number, in which the key and data encrypted the equations can be equivalently written aswith it are scrambled though four rounds ofmathematical processes. The key that is used toencrypt the number must also be used to decrypt it. where the autocorrelation matrix R is a symmetric, p×p Toeplitz matrix with elements ri,j = VI. ANALYSIS OF SPEECH SIGNAL R(i − j), 0≤i,j<p, the vector r is the autocorrelation Speech quality assessment falls into one of vector rj = R(j), 0<j≤p, and the vector a is thethe two categories;subjective and objective quality parameter vector.measures. Subjective quality measures are based on Another, more general, approach is tocomparision of original and processed speech data by minimize the sum of squares of the errors defined ina listener or a panel of listeners.They rank the quality the formof the speech according to predetermined scalesubjectively[22-24].Evaluation results per listener where the optimisation problem searchingwill include some degree of variation in most over all must now be constrained withcases.This variation can be reduced byaveraging the . This constraint yields the sameresults from multiple listeners.Thus the results from a predictor as above but the normal equations are thenreasonable number of speakers need to be averagedfor controlled amount of variation in the overallmeasurement result.On the other hand, objective where the index i ranges from 0 to p, and R is aspeech quality measures are generally calculated (p + 1) × (p + 1) matrix.from the original undistorted speech and the distortedspeech using some mathematical formula.It does not The speech production process can berequire human listeners ,and so it is less expensive modeled efficiently with the linear production (LP)and less time consuming.There are several methods model.There are a number of objective measures thatfor analysis of speech.But the following methods are use the distance between two sets of linear predictioncommonly used coefficients(LPC) calculated on the original and the A. Short –time Fourier analysis distorted speech. B. Cepstral analysis C. Linear Prediction analysis Linear prediction is a mathematical where ac is the LPC vector for the cleanoperation where future values of a discrete –time speech, ad is the LPC vector for the distorted speech,signal are estimated as a Linear function of previous aT is the transpose of a, and Rc is the auto-samples.The most common representation is correlation matrix for the clean speech. The Itakura-Saito (IS) distortion measure is also a distance measure calculated from the LPC vector. This measure, dIS is given bywhere is the predicted signal value, the previous observed values, and thepredictor coefficients. The error generated by thisestimate is where σc2 and σd2 are the all-pole gains for the clean and degraded speech.The Cepstrum Distance (CD) is anwhere is the true signal value. estimate of the log-spectrum distance between clean The most common choice in optimization of and istorted speech. Cepstrum is calculated by takingparameters is the is the root mean square criterion the logarithm of the spectrum and converting back towhich is also called the autocorrelation criterion. In the time-domain. By going through this process, we can separate the speech excitation signal (pulse train 1520 | P a g e
    • Hemlata Kohad, Prof. V.R.Ingle,Dr.M.A.Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1518-1523 signals from the glottis) from the convolved vocal tract characteristics. Cepstrum can also be calculated from LPC parameters with a recursion formula. CD can be calculated as follows: where cc and cd are Cepstrum vectors for clean and distorted speech, and P is the order. Cepstrum distance is also a very efficient computationmethod of log-spectrum distance. It is more often used in speech recognition to match the input speech frame to the acoustic models . Spectral Distance Fig 9.Spectral Distance Vs SNR where and be the cepstral coefficients of the clean signal and the estimated signalVII. SECURITY ANALYSIS AND RESULTS A good encryption scheme should resist all kinds of known attacks .The security of the proposed speech cryptosystem is investigated under the noise attack.For evaluation purpose ,we used LLR ,SD,IS,CD . Typical results are presented in fig.9 to fig 12 shows distance measures from a comparision Fig 10.Input SNR Vs Output SNR of the original speech segment with the resulting scrambled speech . It is seen that the proposed system produces scrambled speech resulting in the largest distance for LLR and lower spectral distance indicating that it has the lowest residual intelligibility.VIII. CONCLUSION The performance of LPC technique, which is equivalent to auto regressive (AR) modeling of the speech signal, however degrades significantly in the presence of noise.As the signal to noise ratio Fig.11.LLR Vs SNR increases LLR,SD,CD decreases.In the evaluation of speech encrypted signal high value of these parameters represents low residual intelligibility and it gives more security. The results of computer simulation illustrate the proposed system has a high level of security and excellent audio quality. The encryption algorithm is very sensitive to the secret key with good diffusion and confusion properties. Experimental results show that the proposed cryptosystem randomize the signal and change the characteristics of it looks like random noisy signal. Fig12.Cepstrum Distance Vs SNR 1521 | P a g e
    • Hemlata Kohad, Prof. V.R.Ingle,Dr.M.A.Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1518-1523REFERENCESJournal Papers: 1) Lin Shan Lee Ger-Chih Chou ―A New 10) V. Anil Kumar, Abhijit Mitra and S.R. Time Domain Speech Scrambling System Mahadeva Prasanna ―On the Effectivity of Which Does Not Require Frame Different Pseudo – Noise and Orthogonal Synchronization‖ IEEE JOURNAL ON sequences for Speech Encryption from SELECTED AREAS IN Correlation Properties‖ International journal COMMUNICATIONS, VOL. SAC-2, NO, of information technology 2007 3, MAY 1984 443 11) Da-Peng Guo, Qiu-Hua Lin ―Fast 2) Jameel Ahmed Dr. Nassar Ikram Decryption utilizing correlation calculation ―Frequency-Domain Speech for BSS based speech encryption system ScramblingDescrambling Techniques ―2010 Sixth International Conference on Implementation and Evaluation on DSP Natural Computation (ICNC 2010) ―Proceedings IEEE INMlC 2003 12) Esmael H. Dinan &Bijan Jabbari, George 3) Mohammad Saeed Ehsani, Shahram Mason university ―Spreading codes for Etemadi Borujeni ―Fast Fourier Transform direct sequence CDMA & wideband CDMA Speech Scrambler ― 2002 FIRST cellular networks‖IEEE communication INTERNATIONAL IEEE SYMPOSIUM magazine 1998 "INTELLIGENT SYSTEMS", 13) S.Chattopadhyay(1),S.K.Sanyal(2),R.Nandi‖ SEPTEMBER 2002 DEVELOPMENT OF ALGORITHM FOR 4) E. Mosa, Nagy.W. Messiha, and O.Zahran THE GENERATION AND ―Chaotic Encryption of Speech Signals in CORRELATION STUDY OF MAXIMAL Transform Domains ――978-1-4244-5844- LENGTH SEQUENCES FOR 8/09/$26.00 ©2009 IEEE APPLICABILITIES IN CDMA MOBILE 5) R. H. Laskar, F. A. Talukdar, B. Bora, K. S. COMMUNICATION SYSTEMS ― P. Fernando, J. Anthony and L. Doley 9 14) Abhijit Mitra ―On the Construction of m- ―Complexity Reduced Multi-tier Perceptual Sequences via Primitive Polynomials with a Based Partial Encryption for Secure Speech Fast Identification Method ―World Academy Communication‖ 78–1–4244–4547– of Science, Engineering and Technology 45 9/09/$26.00 c 2009 IEEE 1 TENCON EEE 2008 TRANSACTIONS ON CIRCUITS AND 15) V. Anil Kumar, Abhijit Mitra and S. R. SYSTEMS—I: REGULAR PAPERS, VOL. Mahadeva Prasanna ―On the Effectivity of 55, NO. 4, MAY 2008 Different Pseudo-Noise and Orthogonal 6) Shujun Li, Chengqing Li, Kwok-Tung Lo, Sequences for Speech Encryption Member, IEEE, and Guanrong Chen, fromCorrelation Properties‖ International Fellow, IEEE ―Cryptanalyzing an Journal of Information and Communication Encryption SchemeBased on Blind Source Engineering 4:6 2008 Separation‖ IEEE TRANSACTIONS ON 16) Mark Goresky, Associate Member, IEEE, CIRCUITS AND SYSTEMS—I: and Andrew Klapper, Senior Member, IEEE REGULAR PAPERS, VOL. 55, NO. 4, “Pseudonoise Sequences Based on MAY 2008 Algebraic Feedback Shift Registers 7) Qiu-Hua Lin, Fu-Liang Yin, Tie-Min Mei, 17) Abhijit Mitra ―On the Properties of Pseudo and Hualou Liang, Senior Member, Noise Sequences with a Simple Proposal of IEEEIEEE ―A Blind Source Separation Randomness Test ―International Journal of Based Method for Speech Encryption‖ Electrical and Computer Engineering 3:3 TRANSACTIONS ON CIRCUITS AND 2008 SYSTEMS—I: REGULAR PAPERS, VOL. 18) Abhijit Mitra ― On Pseudo-Random and 53, NO. 6, JUNE 2006 Orthogonal Binary Spreading Sequences 8) Qiu-Hua Lin, Fu-Liang Yin, Tie-Min Mei , ―World Academy of Science, Engineering Hua-Lou Liang0 ―A Speech Encryption and Technology 48 2008 Algorithm Based on Blind Source 19) Yuh-Ren Tsai* and Xiu-Sheng Li ―Kasami Senaration‖ -7S03-8647-7/04/S20.000 2004 Code-Shift-Keying Modulation for Ultra IEEE. Wideband communication Systems‖ 9) Ate! Mermoul and Adel Belouchra ―A 20) John Mokhoul linear Prediction: A Tutorial SUBSPACE-BA ED METHOD FOR Review PROCEEDINGS OF THE IEEE, SPEECH ENCRYPTION‖ 10th VOL. 63, NO. 4, APRIL 1975 International Conference on Information 21) Lawrence R. Rabiner1 and Ronald W. Science, Signal Processing and their Schafer Introduction to Digital Speech Applications (ISSPA 2010) Processing Foundations and TrendsR_ in 1522 | P a g e
    • Hemlata Kohad, Prof. V.R.Ingle,Dr.M.A.Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.1518-1523 Signal Processing Vol. 1, Nos. 1–2 (2007) speech communication channels using also 1–194 2007 dynamic spectral features22) K. Kondo, Subjective Quality Measurement 24) Yi Hu and Philipos C. Loizou, Senior of Speech, 7 Signals and Communication Member, IEEE Evaluation of Objective Technology, DOI: 10.1007/978-3-642- Quality Measures for Speech Enhancement 27506-7_2, © Springer-Verlag Berlin IEEE TRANSACTIONS ON AUDIO, Heidelberg 2012‖ Speech Quality‖ SPEECH, AND LANGUAGE23) Shuzhen Wu and Louis C. W. Pols a distance PROCESSING, VOL. 16, NO. 1, measure for objective quality evaluation of JANUARY 2008 1523 | P a g e