A Noise-Tolerant Audio Encryption Framework Designed by the Application of S8 Symmetric Group and Chaotic Systems

University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), H-12, Islamabad 44000, Pakistan Department of Mathematics, Statistics and Physics, Qatar University, Doha 2713, Qatar Department of Information Security, 0e Islamia University of Bahawalpur, Bahawalpur, Pakistan


Introduction
e recent decade has witnessed an unprecedented growth of multimedia and its applications. Multimedia data are the majority of data exchanged on modern open communication systems. erefore, multimedia systems are highly prone to attacks and face significant security challenges. Keeping in view, its deemed necessary to develop secure, fast, and stable security frameworks to ensure confidentiality, privacy, and authentication of multimedia applications. Encryption is a fundamental tool to achieve security and researchers across the world are acquainted with modern and innovative assaults for the security of these platforms. e ability to cope with the security requirements of these modern multimedia platforms is an eminent research domain [1]. e audio data applications hold utmost importance and prevalence in multimedia systems [2,3]. Digital audio communications are witnessing a tremendous surge and prominence in various areas, such as military, telephone banking, education, and confidential voice conferencing. e laps in audio data security will significantly impact the security and resiliency of those applications. e audio signals in contrast to images possess a large size and strong correlation among data samples and further involve real-time processing. Moreover, modern audio or voice applications also require low power consumption and less computation time. Despite cryptographic forte, the conventional encryption algorithms like AES [4], DES [5], and RSA [6] are not feasible with the latest low-profile audio applications due to high computational complexity. erefore, the development of audio encryption algorithms with low power consumption, high presence for real-time systems, and fast multimedia processing is a significant area in the current research arena [7][8][9][10][11][12][13].
Shannon [14] presents an idea of substitution permutation network (SPN) that becomes the forefront of all modern cryptosystems. SPN is considered a key design element to yield resilient and secure symmetric encryption algorithms [15]. is elementary concept is grounded on two key principles, that is, confusion and diffusion. ese key parameters eliminate local correlation among adjacent data samples and alter them by substituting pseudorandom values. e confusion is attained through the application of a sole nonlinear component called substitution box or S-box. It is like a nucleus for topnotch symmetric cryptosystems and their endurance counts on the strength of deployed S-boxes. is work does not demand to review the extensive literature on S-box's algebraic properties but readers can be referred to [16][17][18] for understanding. Numerous frameworks are rendered to seed novel and resilient S-boxes such as algebraic structures, chaos theory, analytical methods, the theory of automata, and control mapping. In modern applications, elliptic curve (EC) cryptography is in the limelight due to its resilience and acts as the backbone of blockchain applications. Recently, Hayat et al. [19,20] devised a framework using EC to yields 8 × 8 S-boxes. e cryptosystems based on ECs are robust and secure in comparison to conventional schemes. Considering the importance of Mordell elliptic curves (MEC) on a finite field, MEC along withS8 symmetric group is employed to synthesize a collection of S-boxes. In this work, an SPN-based audio encryption scheme is proposed and a cryptographically potent S-box is designed and deployed to ensure the resiliency of the algorithm.
In recent years, the discipline of nonlinear dynamical systems particularly chaos has hailed substantial advancements. e chaotic solutions possess unique peculiarities including hypersensitive dependence, ergodicity, and pseudorandomness, which adds extensive unpredictability in deterministic nonlinear systems. e predominant applications of chaos are multimedia encryption applications. Chaotic maps were extensively deployed in various image encryption schemes [21][22][23][24][25][26][27][28]. In particular, chaos-oriented audio encryption algorithms are in limelight due to excellent cryptographic attributes. ese algorithms handle redundant and bulky audio files and lead to a high level of security. Roy and Misra [29] employed an audio encryption scheme using chaotic Hénon map and lifting wavelet transforms. e lifting wavelet is used to transform audio signal to data signal and then encrypted via the chaotic map. e transformation adds additional processing overhead and makes it hard to comment on its employment in low-profile applications. Liu et al. [30] offered a cryptosystem through the application of a multiscroll chaotic to generate confusion and diffusion in audio data. Kordov [31] deployed an SPN structure with chaotic maps to design an audio encryption algorithm. Recently, Shah et al. [32] proposed another audio encryption scheme by the application of SPN. e algorithm attained confusion through Mobius transformation and diffusion via chaotic Hénon map. Abdelfatah [33] proposed a novel audio encryption scheme through the combination of four methods self-adaptive scrambling, multiple chaotic maps, cipher feedback, and DNA encoding. e proposed scheme tends to enhance keyspace and provide strength against brute force attacks. Naskar et al. [34] presented a novel audio encryption technique that adopts block ciphering via DNA encoding and logistic chaotic map. e channel shuffling is further introduced to secure against cryptanalysis. To cater to demands of real-time low-profile audio applications in constricted environments. Peng et al. [35] proposed an ultra-lightweight secure architecture for underwater acoustic networks. e algorithm is based on chaotic maps and Feistel structure to cater to demands of low bandwidth with limited overhead. Adeel et al. [36] introduced a novel lightweight chaotic encryption algorithm for next-generation audio-visual hearing equipment. Nagakrishan and Revathi [37] offered a speech authentication system by the applications of chaotic maps and DNA.
Lightweight ciphers have the drawback that they are prone to channel noise. erefore, the researchers have focused on developing noise-tolerant ciphers for multimedia applications. e noise-tolerant ciphers on the other hand possess processing load and inadequate for systems that require constrained computing power. Recently, chaos has been employed in several noise-tolerant image encryption schemes. Ahmad et al. [38] utilized properties of orthogonal matrices and proposed a chaotic image encryption scheme with the ability to handle channel noise. Elashry [39] proposed another noise-tolerant image encryption design through baker chaotic using three modes of operation. In another method, Patro et al. [40] implemented a noise-resistant image encryption algorithm utilizing hyperchaos with DNA shuffling. ere are no substantial and comprehensive implementations of chaos targeting noise tolerance in audio applications. Hussein et al. [41] employed double chaotic masking on the audio signal to increase keyspace and achieve higher security. e immunity technique is further implanted to reduce the effect of noise. However, a thorough security review is lacking to ascertain the resilience of the algorithm against attacks. Michel-Macarty et al. [42] proposed a multiuser communication scheme for applications related to telemedicine. e scheme utilizes a chaotic Hénon map with code division multiple access (CDMA) technique to ensure privacy and shows robustness against noise. Sheela et al. [43] came with a new audio encryption algorithm based on chaotic maps and DNA encoding. e algorithm offers reasonable security and achieves a prominent signal-to-noise ratio (SNR). In [44] through 5G and IoT, chaos drove secure real-time nextgeneration audio-video hearing aid framework is proposed. e system has the potential for the acquisition of highquality speech restoration in noisy environments.
e aforementioned survey of current literature depicts that no major study is available for current real-time lowprofile audio applications with the ability to handle noise, especially to address the security concerns related to military voice communications in warzones. Moreover, the chaosbased audio encryption schemes largely lack cryptanalysis and inadequate security evaluations were performed to verify the stability of these cryptosystems against attacks. In this paper, an SPN-based structure is proposed by the application of three secure chaotic maps to develop an audio encryption application. e proposed scheme is explicitly intended to protect military communications focused on security. Detailed evaluations of the cryptosystem demonstrate the endurance of the system.
ere are a number of integral outcomes achieved through this work. A highly nonlinear S-box is designed and yielded through the application of MEC on a finite field and symmetric group of permutations S 8 . Moreover, a real-time secure chaotic audio encryption scheme is presented through the SPN paradigm for modern communication platforms particularly targeting military communications. e scheme employs chaotic maps along with proposed S-boxes, permutation, and cyclic shift operation. e proposed audio encryption scheme is resistant to noise induced in audio signals either through channel disturbance or deliberately by an eavesdropper. e strength of the scheme is evaluated against noise through experiments and the result yields a promising output. e eminent performance of the proposed scheme is depicted through the execution of series of analyses and equating them with modern renowned schemes.

Problem Statement
In the modern era, the electronic revolution and communication technologies witnessed a staggering rise in multimedia systems. Particularly, secure transmission and multimedia processing is a challenging research area to convey with the requirements of modern innovative applications. erefore, multimedia data security poses significant concerns and confronts various forms of attacks. e audio data signals constitute a crucial role and prominence in multimedia systems. Digital audio communications are critical for military and defense and their security is of prime significance. Modern forms of warfare revolve around military communications. To ensure that all aspects of command and control (C2) are successfully shared, military forces require a broad spectrum of real-time voice communications capability. Secure voice messaging with high availability is the lifeline on the battlefield and border areas. e main threats to voice or audio communications in a military environment are eavesdropping, impersonation, and illegal access to the medium. erefore, the encryption of audio is highly desirable and critical from a security perspective. Unlike images and text, the encryption of audio requires a unique transformation of maps and strong security. e adjacent audio data samples possess a strong correlation and slow time varying and is substantially large.
Eavesdropping is not the only problem encountered during audio messaging in the military environment. e enemies intentionally add noise to corrupt encrypted audio signals. In cases, the whole channel is chocked with noise to deprive the opposition of the means to communicate, whereas conventional radio military apparatus also induces noise over communication channels. Due to the intrinsic nature of these audio applications, conventional encryption schemes such as DES, AES, and RSA are not appropriate for achieving the desired results. ese traditional encryption schemes are also not resistant to noise through the channel and or added deliberately by opponents. e advanced warfare communication technologies drive through the competition between electronic countermeasures (ECM) and electronic counter-countermeasures (ECCM) [45]. Frequency hopping is a modern and promising technique employed by the military, where the transmitter rapidly switches the carrier wave frequency [45]. e frequency hopping is used in line with digital encryption to make the communication secure and reliable. At the same time, the frequency hopping inherently adds noise to the transmission. Considering the ECM and ECCM, developing a robust noise-resistant audio encryption algorithm with low computational complexity is a challenge, especially in battlegrounds, where secure realtime point-to-point audio messaging is of prime significance.
ese technological mandates, security concerns, integration, and interoperability requirements provide a catalyst to develop next-generation secure encryption algorithms.

Chaotic Maps for Proposed Cryptosystem.
is section briefly presents chaotic maps deployed for our proposed audio encryption scheme.
ree different maps, that is, Chebyshev, Tangent Delay Ellipse Reflecting Cavity Map System (TD-ERCS), and circle chaotic maps, are deployed for our proposed encryption scheme, while engaging three distinct chaotic maps escalates the randomness of the proposed scheme and has a larger keyspace than a single chaotic system.
It is well known that Chebyshev polynomial exhibits chaotic behavior and is significantly deployed in secure communication. In [46], a modified version of Chebyshev chaotic map was employed to encrypt thermal images and achieved promising output. Proposed scheme implements modified Chebyshev chaotic map and is mathematically expressed as Here, n is an integer and x has a range [−1 1]. TD-ERCS chaotic map yields two chaotic sequences and is represented in the following equation [47]: where Mathematical Problems in Engineering 1), and m = 2, 3, ..., n; now, e third map employed in the proposed scheme is a one-dimensional circle chaotic map. Circle map shows good chaotic behavior when applied to any data and its mathematical form is given as where x n � 0.4, Ω � 0.5, x n + 1 is computed as mod 1, and k is constant. e chaotic ranges of all three maps are thoroughly examined and only those ranges are considered where maps show chaotic behavior. e chaotic sequences are then selected from the application of these three chaotic maps. e initial values of those sequences will serve as an encryption key in the proposed cryptographic scheme.

S 8 MEC S-Box.
In recent years, EC cryptography has been widely adopted to develop powerful and exceptionally secure cryptosystems. e key offerings of these elliptical curve cryptosystems are their efficiency and tolerance against modern cryptanalysis. Ullah et al. [48] proposed a pseudorandom number generator (PRNG) through the application of ordered MEC to seed distinct and mutually uncorrelated S-boxes. Now, the S-boxes are further employed as a seed to yield new S 8 MEC S-boxes. Hussain et al. [49] devised an approach to yield highly nonlinear S-boxes through the action of the symmetric group S 8 . e design structure of this algorithm relies upon symmetric group S 8 that is the permutations group of order 40328. ese permutations are incorporated in the algebraic structure of the resultant S-boxes. In this paper, we engage the action of S 8 symmetric group on the MEC S-box to construct a collection of 40320 unique S 8 MEC S-boxes of identical strength and features. e sample 8 × 8 S-box is depicted in Figure 1. e cryptosystem designed by applying a single S-box lacks adequate stability and reliability to address the security requirements of modern applications. In particular, multimedia applications such as audio files possess highly correlated data. erefore, the cryptosystems designed through the applications of multiple S-boxes are more resilient and achieve high confusion and diffusion. e collection of multiple S 8 MEC S-boxes is extremely pertinent to the scenario of the current target application and improves the security of the proposed scheme. e scenario is explained in Figure 2 where 8 identical pixel values are transformed through single as well as with 8 different S-boxes. e figure reveals that transformation with one S-box has no effect on correlation though multiple S-box transformations that give unique values and enhanced correlation. e transformation effect via multiple S-boxes will optimize the cryptosystem and achieve desired security using fewer rounds.

Proposed Audio Encryption Algorithm
It is evident through results that S 8 permutation will totally alter the values and yield unique S-boxes after application. e algebraic characteristics acquired by new S-boxes are identical to the parent S-box and play a significant to establish a resilient cryptosystem. In this section, the architecture of the proposed audio encryption scheme is presented. e scheme is based on the SPN framework and contains three main steps cyclic shift, substitution, and permutation repeated for a set of four rounds. e overall idea is depicted in Figure 3 where an audio file of size M is served as an input to the encryption scheme. e audio input is divided into samples with digital values. e values in the original audio were in the range [− 1 1]. To make input coherent with the proposed scheme, a preprocessing step was taken first. A positive shift of 128 was applied and further rescaled 128 times, resulting in a sequence of values between 0 and 256. e sequence was further separated into a range of length 65535 and then translated into a matrix of size 256 × 256. Each element of the matrix has a range [0 256] and can be addressed in 8 bits. e element of the matrix is represented as A (u, v, m) or simply A (u, v) where u and v represent row and column and m denotes the frame number. e schemes comprise three steps and each step takes unique keys as input.
e first chaotic sequence utilized in the proposed scheme is derived from Chebyshev chaotic map denoted as x. Moreover, x o and P are the preliminary chaotic values acquired from its range.
ese preliminary values are considered as the initial two encryption keys for the proposed scheme; that is, x o � k 1 and P � k 2 . e length of this Chebyshev chaotic sequence is (d * e * f ) with an initial range that lies within [− 1 1]. In order to employ the proposed scheme, the range of chaotic sequence is amplified by multiplying with 100 and then applying modulo 8 to restrict value within 255. e value A(u, v) is now converted into an 8-bit binary denoted as A b (u,v). e left cyclic shift operation of x q bits is further performed and cyclic shifted value is termed as A c (u,v), where x � 1,2,3, . . ..d * e * f. is cyclic shift is understood with the help of an example; that is, suppose A b (u,v) � 11011001 and x q � 3; then A c (u,v) � 10111001. 4 Mathematical Problems in Engineering Consider that TD-ERCS chaotic map and two chaotic sequences o and t are considered with initial values as x 0 , tan φ, µ, and n. e next secret keys k 3 , k 4 , k 5 , and k 6 are considered from these initial chaotic values.  A d (u,v). e diffusion operation is done through the permutation of rows and columns of A d (u, v) and permuted value is denoted as A p . Suppose (23,56,12) are initial values of chaotic sequence o; then, the first three rows of A d will be positioned at (23,56,12) rows of A p . e permuted value A p is acquired by row and column permutation on A d .
To enhance the resiliency of the proposed scheme, no active S-boxes can be chosen through the chaotic sequence t.
e results obtained through this substitution are denoted as A s . e above steps are now repeated in a cycle of four rounds till a subsequent encrypted matrix is acquired. e size of the resultant encrypted matrix is identical to the original input and is further rescaled in the range [−1, 1] to obtain an encrypted audio file. Now, in order to decrypt the audio signal via the proposed scheme, the steps must be followed in reverse order as shown in Figure 3. In the case of decryption, the encrypted matrix of size d * e * f is fed to the algorithm as input, whereas the same set of keys are used for decryption and operations of substitution, permutation, and cyclic shift are performed inversely.
In the first step, inverse S-box is used to substitute encrypted matrix C. Inverse substitution on matrix C is given as e inverse of the S-box, that is, Inv-S 8 MEC S-box, takes two parameters and performs inverse substitution. Similarly, an inverse permutation is applied on permuted matrix A i8 with reference to chaotic sequence o. TD-ERCS map generates chaotic sequence r and matrix A ip is obtained by the action of inverse permutation.
A up1 (:, u) � A u8 (:, y(u)), ∀ u ∈ d, Finally, during the last step, values A ip are transformed from decimal to 8-bit binary denoted as A ip (i,j). In this case, a right cyclic shift of x q bits is executed with an outcome denoted as A ic (i,j), where q � 1, 2, . . ., a * b * c and x is chaotic sequence obtained from Chebyshev chaotic map having identical initial values. e same steps are followed for all four rounds to get the final input matrix. e matrix is again rescaled and transformed into an original audio file denoted as D.

Results and Discussion
In order to perform simulation analyses, an "audio1.wav" is nourished as a carrier input to the proposed scheme. e secret keys deployed in the algorithm are generated via chaotic maps and outlined in Table 1. is can also be visually elaborated through waveform plotting of original and encrypted audio files. e resiliency of the proposed scheme is further verified using "audio2.wav" as shown in Figure 5. e input and decrypted output audio are mapped together in Figure 5(d) and identical output proves that audio is recovered successfully. e outcomes divulge that the encrypted scheme is resilient and alters output in few rounds. e results are also substantiated from the outcomes of the next section.

Cryptographic Security and Performance Analysis.
ese analyses gage the resiliency, performance, and strength of our audio encryption scheme. Security and performance analysis is not appropriately performed on audio encryption schemes presented in recent literature. erefore, it is extremely difficult to evaluate and comment on the practical applications of these algorithms. e statistical analysis is also presented in this section to sort the working mechanism and determine the strength of the proposed scheme. ese analyses are executed and a comparison of results is done with the current state-of-the-art literature.
is analysis aids superior performance of the proposed scheme.

Correlation.
is analysis is a statistical metric to determine the strength of an encryption scheme. Correlation is tremendously beneficial for multimedia applications and Mathematical Problems in Engineering  audio; this analysis is done in the horizontal direction. Correlation computes the mutual relationship between identical segments in original and encryption audio samples.
A resilient cryptosystem alters audio samples into a random noise signal with a low correlation coefficient. e equation presents the correlation coefficient: Here, N s represent the total number of samples and i x and j x are sample values of encrypted audio. E(i), E(j), σ i , and σ j represent the mean and standard deviation of an original and encrypted audio file. Cov(i,j) is the covariance among both files. e analysis ascertains perfect result as shown in Table 2, and correlation coefficient values are edging towards zero. is means no similarity exist among two files and shows randomness of the encrypted sample.

Entropy.
It is a metric to evaluate the degree of uncertainty in encrypted audio data. e entropy analysis determines the randomness as shown in Table 3 and its higher value depicts randomness.

Contrast.
e contrast level determines dissimilarity in the audio signals. It is a metric to verify the effectiveness of encryption and determines that encryption creates required diffusion.
where i and j represent the position of a specific element of audio data. P(i, j) indicates the order of the cooccurrence matrix. Mathematical Problems in Engineering characteristics of distribution, homogeneity analysis is applied to measure the spread of components in gray-level cooccurrence matrix (GLCM). Mathematically, it is expressed as where i and j represent the position of a specific element of audio data. P(i,j) indicates the order of the cooccurrence matrix.

Energy.
where i and j represent positions of a particular element of audio data. is analysis computes the energy and determines the sum of squared elements of gray values in GLCM. It is mathematically given as All these four analyses are performed on an audio encryption scheme using different data samples and compared with relevant approaches as given in Table 3. e outcomes point towards the substantial performance of the algorithm.

Keyspace Analysis.
e keyspace represents all possible values that can be deployed as a secret key in the encryption algorithm. e proposed algorithm incorporates three chaotic maps to yield eight secret keys derived from their initial values. eir ranges are considerably large to resist brute force attacks. Assume the average range of keys is 10 10 and for all the eight secret keys, there are about 10 80 possible keys. It is almost equivalent to 256 binary bits. A modern computer would require around 10 15 years to check the keyspace of this size.

Key Sensitivity Analysis.
Keyspace on its own is not sufficient to ensure stability, and a resilient cryptosystem should therefore have to attain key sensitivity. Key sensitivity is related to the decryption and a tiny change in key-value results in failure of decryption and yields different output. In this scheme, the input audio is encrypted via secret keys presented in Table 1. ree separate scenarios are considered in order to illustrate the key sensitivity of the proposed scheme.
Firstly, we alter k 1 � q 0 � 0.5 to k 1 ' � q 0 � 0.500000001 and the remaining keys remain the same. In Figure 6(a), the difference with blue color represents decrypted audio and red color shows the original file. Now, for the second case, key k 5 is changed from k 5 � µ � 0.4 to k 5 ' � µ � 0.4000000001 and similarly k 8 e decrypted audio outputs are represented in Figures 6(b) and 6(c). It is clear that decryption of audio is failed with only a slight change in the encryption key and the algorithm shows formidable results.

Number of Sample Change Rates (NSCR) and Unified
Average Changing Intensity (UACI) Analysis. NSCR and UACI are robustness tests for investigating the performance of the encryption scheme. NSCR and UACI are mathematically represented as where x i and x i ′ are the representation of encrypted audio samples at ith position. N s is the length of the audio segment. D i can be defined as Table 4 shows the result of NSCR and UACI and comparisons have been done with modern schemes that depicted excellent performance and better resistance against differential attacks.

Encryption Quality.
ese metrics are applied to estimate the performance of the audio encryption framework. e analyses performed are mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). e MSE measures the similarity between the original and the encryption file. SSIM gages the difference between the two audio samples. PSNR is a measure of the ratio between the original and encrypted audio file and Reference [32] Reference [34] Reference [33] Reference [50] Reference [51] Proposed (Audio 1) PSNR � 20 log 10 255 where N is total samples and x i and y i are sample values of plain and encrypted audio.
Here, A, C is the mean and σ A and σ C are the standard deviation. Further, σ AC indicates cross correlation, B 1 � (K 1 Q) 2 , and B 2 � (K 2 Q) 2 , where Q is dynamic range and K 1 � 0.02 and K 2 � 0.03. It is apparent from the results as shown via Table 5 that the proposed scheme coheres excellent encryption effect.

Crest Factor (CF) and Root Mean Square (RMS)
Analysis. CF is a metric that determines the ratio of the peak value and its effectiveness in a waveform. If its value is zero, this means no peak and a higher value means a higher peak. RMS is calculated via the average amplitude level of an audio signal. ese are mathematically expressed as With reference to results obtained from Table 6, no statistical relation was found among original and audio files.

Spectrogram Analysis.
A spectrogram is a two-dimensional illustration of an audio signal. e third dimension is represented with different colors and can be obtained via Fourier transform.
e colors indicate the loudness of a specific frequency at a certain time. Figure 7 represents the spectrogram of original and encrypted audio files. It is clearly evident from results that encrypted audios are uniform and no similarities exist between original and encrypted audio files.

Computational Time.
Modern encryption schemes necessitate low computational requirements to synchronize with modern real-time audio applications. e robust design and SPN framework ensure superior performance of the encryption scheme. One elementary reason is four encryption rounds to attain desired security in contrast to other schemes that require a minimum of 12 rounds.

Noise-Resistant Analysis.
e forefront characteristic of the proposed audio encryption scheme is its ability to withstand noise. It is a proven fact that audio or voice conversations possibly suffer the effect of channel noise. e noise is purposely added by an unauthorized user or as a result of channel disturbance. All foremost encryptions schemes are inherently designed in such a way that slight modifications in cipher data fail successful decryption. Error detection and error correction measures are taken into account to address these issues but at the cost of additional computational load, whereas the real-time low-profile audio applications demand swift processing in contrast to security. Currently, no major approach is available to cater noise resistance in audio encryption applications. e proposed audio scheme successfully decrypts the noisy cipher audio. Several experiments have been conducted by adding noise to a large number of audio files. e audio files of varying sizes, formats, and frequencies are fed to the proposed scheme. "Salt and pepper" noise of density 0.1, 0.2, and so on is further induced in cipher audio. It is evident through experimental results that decrypted files are audible and proves noise tolerance of the proposed encryption scheme. e outcomes depict that the proposed scheme addresses our target problem of military warfare communication with high effectiveness.

Cryptanalysis
Linear and differential cryptanalysis are the major approaches to determine the strength of the cryptosystem. e proposed audio encryption scheme is secure against both attacks. e linear approximation probability LProb max � 2 −4.21 tests the imbalance of an event. e proposed approach employs 256 S-boxes in four rounds of the encryption scheme. erefore, LinearProb. 4r max � 2 −4.21 × 256 � 2 −1077..76 and shows that differentiating a random permutation for cryptanalyst is highly challenging. Similarly, the differential probability is DifferentialProb. 4r max � 2 −4.05 × 256 � 2 −1036.8 and S-boxes have a uniform mapping and audio encryption is resistant to differential cryptanalysis. e proposed scheme should also avoid several other attacks model of cryptanalysis. e most prominent of these are cipher-text only and known-plaintext and chosen-plaintext attacks. e proposed audio encryption algorithm has an exceptional confusion and diffusion framework. Moreover, the results of key sensitivity, keyspace, and other security analyses make algorithms prone to these attacks. e security and strength of the proposed audio encryption scheme are evaluated through a variety of security and performance metrics. e outcomes are further equated with the recent state-of-the-art approaches and yield exemplary performance.
e results indicate that the audio encryption scheme is resilient and feasible for real-time applications.
e performance metrics prove that the scheme has moderate computational requirements and noise-resistant characteristics. e robust design and inherent characteristics of the scheme make it practically feasible to use it in modern military communications. e proposed scheme as a whole tends to

Conclusion
In the present research, an encryption framework is proposed to ensure the security of real-time multimedia applications. e design framework encrypts audio applications and is primarily developed to ensure the integrity of military voice communications. ree chaotic maps are further incorporated in the proposed scheme to enhance resiliency. e prescribed approach incorporates the action of three recurring steps cyclic shift, permutation, and substitution of bits repeated for four rounds. e proposed design exploits MEC and S 8 symmetric group to synthesize confusion components. Each component of the algorithm has an input yielded by a distinctive chaotic sequence. e security and performance analysis depicted that the proposed scheme resists all modern attacks. Moreover, the simulation results manifest that the encryption scheme is robust and resistant to noise perturbation. e unique characteristics of noise resistance make the algorithm a unique choice in critical applications. Moreover, the algorithm is flexible and adaptive towards changes. e modular design adopted via the SPN framework along with the fusion of chaotic maps renders the scheme feasible to a variety of low-profile modern applications. e inherent characteristics and evaluation results make the scheme an ideal candidate for modern  warfare communication. e most sublime feature of the proposed scheme is its noise-resistant characteristic that suits ideally to target problem area of real-time audio communication in warzones. e resiliency of the scheme is enhanced by employing additional S-boxes and increasing rounds of encryption. is will surely be subjected to the target application and also increases computational requirements. e proposed paradigm is ideally suited to real-time audio applications and also incorporates digital video with minor alterations. In forthcoming work, we intend to develop a cryptosystem for the security of remote sensing big data. e current scheme will also be fused with a machine learning approach. As a new proposal, it is suggested to analyze this framework before deployment.

Data Availability
e data used to support the findings of this study are included within the article.

Conflicts of Interest
e authors declare that they have no conflicts of interest.