Secure data over GSM based on algebraic codebooks

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Multi Rate 12.2Kbps voice channel is investigated and its maximum achievable data rate is calculated. Based on the vocoder channel properties, the method's ...
Secure Data over GSM based on Algebraic Codebooks M. Boloursaz, R. Kazemi, D. Nashtaali, M. Nasiri, and F. Behnia Department of Electrical Engineering Sharif University of Technology Tehran, Iran Email: [email protected] Abstract This paper considers the problem of secure data communication through the Global System for Mobile communications (GSM). The algebraic codebook method for data transmission through the Adaptive Multi Rate 12.2Kbps voice channel is investigated and its maximum achievable data rate is calculated. Based on the vocoder channel properties, the method's Bit Error Rate (BER) performance is improved by repetition coding and classification methods. Simulation results show that by simultaneous application of repetition coding and clustering methods, the decoder’s performance improves about 6.5% compared to the case of no clustering for 1Kbps data communication in AMR 4.75 voice codec.

1. Introduction Nowadays, with the spread of various voice communication systems such as PSTN, VOIP and Mobile Networks, voice channels are widely available. Previous research [1], [2] has shown that it is possible to use such channels for data communication in lowrate applications. Although high speed data dedicated channels are currently available, using compressed voice dedicated channels for data communication is still interesting due to the following reasons. First, the wide coverage of common voice channels makes the proposed low-rate data channel available everywhere, especially in areas where data dedicated networks have not yet become widespread. Second, in order to maintain the required speech quality, voice channels are designed for better QoS in comparison with data services. In general voice traffic is prioritized over data connections on networks where both exist. This makes the proposed low-rate data channel a reliable and more consistent real-time channel with better QoS especially on network congestion periods.

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Finally, the proposed data channel will be more secure by applying point to point encryption and avoiding the common network gateways. A data modem of the type described here has been suggested for transmission of encrypted end-to-end secure voice or data over the GSM voice channel in [1], [3] and [4]. This channel can also be used for realtime and secure transmission of Point of Sale (PoS) transaction information from the POS terminal to the financial host through the GSM voice channel [5]. Providing data connection over voice dedicated channels is a challenging problem because of several limitations. First, due to the low bandwidth of speech signal, voice dedicated channels are extremely narrow-band with maximum bandwidth of 4 KHz which inherently limits the achievable data rates. Second, as common voice channels use several techniques like Discontinuous Transmission (DTX), Voice Activity Detection (VAD) and Comfort Noise Generation (CNG) in order to reduce bandwidth usage during voice silence periods, the modulated signal should pass through the voice channel without raising any alarms such as VAD. Finally, the voice codecs used in such channels cause several unpredictable distortions to the modulated signal during the compression/decompression process. The previously proposed methods for the problem of data communication through compressed voice channels can be classified to three different groups due to the conceptual idea they are based on [6]. These three groups are referred as “Parameter Mapping”, “Codebook Optimization” and “Modulation Optimization” methods. Kondoz et al. [7] reported a method for data communication through codecs that utilize Algebraic Code Excited Linear Prediction (ACELP) speech coding technique. This method can be assumed as a “Parameter Mapping” technique in which the input data stream is modulated on Pulse Positions making a PPM signal. The pulses are then shaped for efficient

transmission through the Adaptive Multi Rate (AMR) [8] voice channel. In this research the algebraic codebook method (Kondoz et al. [7]) for secure data transmission through the GSM Adaptive Multi Rate voice channel is investigated. This method is introduced and its maximum achievable data rate is calculated. Based on AMR 12.2 channel properties, performance improvements are achieved by applying the idea of repetition coding. Finally, the decoder’s performance is further improved by application of classification and clustering methods on the output energies of the filter bank correlators. Section 2 outlines the algebraic data transmission scheme and the modulation/demodulation process. Section 3 presents the idea of repetition coding for further performance improvement of the method. Section 4 describes the performance and simulation result improvements by addition of the classification and clustering methods. Finally, section 5 concludes the paper.

2. Data Mapping on Algebraic Codebook Parameters The 12.2 Kbps table of the algebraic codebook of the AMR standard introduces a specific Pulse Position Modulation (PPM) for reliable data transmission over the GSM voice channel. This modulation scheme is used by Kondoz et al. [7] to transmit secure data over AMR 12.2 vocoder channel. The mentioned method is expected to be robust against the vocoder compression distortions because it maps the input bitstream on fixed algebraic codebook parameters that are directly extracted and transmitted in the speech coding process of AMR voice channel. In this modulation scheme, the modulator maps each 15 consecutive input bits on 5ms waveform symbols sampled at 8 KHz i.e., each symbol consists of 40 samples. Each such symbol is divided into five tracks of 8 sample positions. Each 3 consecutive bits of the input bitstream address the sample position of the pulse in the corresponding track. As noted by [7], the sign of consecutive pulses are changed alternately to guarantee less vocoder distortion and consequently, Table 1

better Bit Error Rate (BER) performance. Table 1 shows this bit pattern PPM modulation. As noted by [7], in order to make the modulator output signal to be classified as voice at the networks VAD block, it should resemble human’s voice. To do so, further spectral shaping in frequency domain is done on modulator’s output signal. At the receiver, a channel compensator is used to overcome the distortions caused by GSM AMR voice codec. Then the transmitted bits are extracted by applying matched filter over all possible PPM signals. The extracted bits at the receiver are then remodulated to provide the reference signal needed to update the filter coefficients of the adaptive equalizer that tries to compensate the channel’s response. Fig. 1 shows the mentioned receiver structure [7]. The coefficient adaptation process has 3 states. State P1 is for continuous adaptation. The adaptation process is suspended if the state is P2 and finally state P3 uses the previously stored training sequence to initialize the adaptive filter coefficients. The data transmission rate of this method is given by (1) = In which N denotes the symbol length in samples (e.g. in [7]), b is the number of bits transmitted by each pulse position and is the total number of transmitted pulses in a symbol. The data transmission rate for Kondoz et al. method is obtained as 3Kbps by placing and . Other data rates can be achieved by varying the number of pulses in a symbol and the number of bits modulated on each pulse position. Restricting our method to orthogonal symbols like PPM, the maximum data rate that is achievable by the mentioned method can be derived as half of the sampling frequency i.e. 4Kbps. From orthogonality principle it follows that . Replacing in (1), the maximum achievable rate is obtained by (2) The

equality

holds

for

and

AMR Algebraic Codebook-PPM with 5 tracks and 8 Sample Positions

Track 1: Track 2: Track 3: Track 4: Track 5: Data Bits:

0 1 2 3 4 000

5 6 7 8 9 001

10 11 12 13 14 010

15 16 17 18 19 011

20 21 22 23 24 100

25 26 27 28 29 101

30 31 32 33 34 110

35 36 37 38 39 111

Figure 1

The Algebraic Codebook Reciever Structure

.

3. Further Performance Improvement by Repetition Coding As noted by the AMR standard [8], the 20ms voice frames are divided into four subframes of 5ms (40 voice samples) each. The fundamental voice parameters are extracted for each subframe and then further compressed and combined to form the transmitted parameters for the whole speech frame. This compression process causes further loss of data about the original voice frame which in turn leads to more distortions and consequently higher error rates. Hence, if the data symbols are repeated, identical subframe parameters lead to less loss of data in this compression process and the output signal tracks the original transmitted signal more accurately. It is also observed that when repetition coding is used, detection by the last repeated subframe results in the minimum BER because this last subframe is the one that has the least distortion compared to the transmitted symbol. The Bit Error Rate (BER) for different number of symbol repetitions (redundancy factors) is reported in Table 2. The demodulating scheme of this three times repeated signal is presented in Fig. 2. In the next section, the BER performance of the introduced method is further improved by applying the classification techniques.

4. Classification Model As discussed in the previous section, Kondoz’s decoding scheme detects the symbol with the most correlation or the most similarity from possible symbols at the receiver (matched filter bank). But as the vocoder’s distortion is not additive and Gaussian, it seems that this detection policy does not lead to the optimum results, especially when repetition coding is used, because of neglecting some features of the previously repeated subframes. In this section, the decoding process is further improved by using features from the previously repeated subframes. The main idea is to utilize the information of previous subframes by using ensemble classification methods. The main input data of the proposed classification method are, “The first fifteen maximum correlations of Table 2

The BER Results for Repitition Factors of 1, 3 and 4

Data Rate (Kbps) 3 1 0.75

the decoder’s matched filter bank” denoted by , , … and . Simulation results show that the correct symbols lie between these fifteen symbols with a probability of more than 96 percent. Fig. 3 illustrates the proposed ensemble classification method. The input data of the proposed decoding scheme is an N × 15 matrix. N is the number of the transmitted symbols i.e. for the transmitted symbol its are arranged at the row. These outputs are clustered by K-means clustering method. Then, the binning block normalizes these data. Finally, the SVM algorithm is applied to extract the output data from or . Therefore, the method consists of the 4 steps described as follows. 1) Classification: The input data are classified into 4 groups, respectively. Simulation results show that the performance does not improve noticeably by using more than 4 groups. Different classification methods such as SVM, Neural Networks and Decision Trees [9] are examined on these data and the SVM method shows the best BER performance. 2) Classification and Binning: The Gaussian binning technique is used to categorize data to 3, 5 and 7 groups [10]. The Gaussian binning block determines the mean and variance and specifies some intervals for each column. For example, for 3 groups binning, data within one standard deviation of the mean are assigned a 0, data lower than this interval are assigned a −1 and otherwise a +1. The simulation results for categorizing data to 3 binned regions and classifying to four groups are presented in Table 3 for AMR 4.75 voice codec. The achieved result of shows a 9.6% performance improvement compared to the results reported in Table 2 for AMR4.75. In these simulations, 70 percent of data are used for training, 20 percent for testing, 10 percent for validating. Suppose that the probability of correct detection of is and the frequency of as a correct energy index is , then the total probability of correct detection is calculated by (3)

AMR 12.2Kbps (BER)

AMR 4.75Kbps (BER) 0.3 0.195 0.02

Correlator with all possible signals

Figure 2

Maximum Selection

Output bits

t=3T

Decoding Scheme for the Repitition Factor of 3

Classification and Clustering

Data

K-means Clustering

Classification ========== SVM

Binning

..., E1 (i),..., E15 (i) ,...

1

use E1 (i) for decoding

2

use E2 (i ) for decoding

3

use E3 (i) for decoding

4

use E4 (i ) for decoding

Figure 3 The Proposed Classification/Clustering Method

This measure is used for investigating the performance of each step and classification method. 3) Clustering, Classification and Binning: On this step, the data are clustered into 10 regions by K-means method. The other proceeding steps are similar to case 3. The corresponding simulation results are reported in Table 4 again for AMR 4.75. 4) Clustering, Binning, Classification and Repetition coding: Finally, the classification algorithm’s performance is improved by application of repetition coding and utilizing the data of the previously repeated subframes. Table 5 illustrates the simulation results of using the second subframe’s information when the 3Kbps signal constructed by kondoz method is repeated three times i.e., the data rate is 1Kbps. Again the rusults are reported for AMR 4.75 and show 6.5% performance improvement compared to BER=19.5% reported previously in Table 2.

5. Conclusion

In this paper the algebraic codebook method for secure data transmission through the GSM Adaptive Multi Rate voice channel is investigated. This method is introduced and its maximum achievable data rate is calculated. Based on AMR channel properties, performance improvements are achieved by applying the idea of repetition coding. Finally, the decoder’s performance is further improved by application of classification and clustering methods on the output energies of the filter bank correlators. It is shown that by simultaneous application of repetition and Table 3

Classification of Data to Four Groups by Binning (

Detected Sent 1 2 3 4 Table 4

, AMR4.75)

1

2

3

4

99.2% 56.4% 49% 51.8%

0.6% 39.5 0% 0%

0% 0.5% 51% 0.9%

0.2% 3.6% 0% 47.3%

Results of the K-means Method for Different Number of Regions with ( , AMR4.75)

Detected Sent 1

1

2

3

4

99.5%

0.3%

0%

0.2%

clustering methods, the decoder’s performance improves about 6.5% compared to the case of no clustering for 1Kbps data communication in AMR 4.75 voice codec.

6. References [1] N. Katugampala, S. Villette, A.M. Kondoz, ”Secure voice over GSM and other low bit rate systems,” IEEE Secure GSM and Beyond: End to End Security for Mobile Communications, pp. 3/1-3/4, Feb. 2003. [2] C. K. LaDue, V. V. Sapozhnykov, K. S. Fienberg, ”A data modem for GSM voice channel,” IEEE Transactions on Vehicular Technology, vol. 57, no. 4, pp. 2205-2218, July 2008. [3] N. Katugampala, K. T Al-Naimi, S. Villette, A. M. Kondoz, ”Real time data transmission over GSM voice channel for secure voice and data applications,” Secure Mobile Communications Forum: Exploring the Technical Challenges in Secure GSM and WLAN, pp. 7/1-7/4,Sept. 2004. [4] N. Katugampala, K. T. Al-Naimi, S. Villette, A. M. Kondoz, ”Real time end to end secure voice communications over gsm voice channel,” 13th European signal processing conference, Turkey, Sep. 2005. [5] B. Kotnik, Z. Mezgeca, J. Svecko, A. Chowdhury, ”Data transmission over GSM voice channel using digital modulation technique based on autoregressive modeling of speech production,” Digital Signal Processing, vol. 19, no. 4, pp. 612-627, July 2009. [6] V. Sapozhnykov, S. Fienberg, ”A Low-rate Data Transfer Technique for Compressed Voice Channels,” Journal of Signal Processing Systems, vol. 68, no. 2, pp. 151-170, 2012. [7] A. Kondoz, N. Katugampala, K. T. Al-Naimi, S. Villette, Data Transmission, WIPO Patent GB05/001729, November 17, 2005. [8] ETSI, Digital Cellular Telecommunications Systems (Phase 2+), Adaptive Multi-Rate (AMR) speech transcoding, (GSM 06.90 version 7.2.1 Release 1998). [9] W. Guosheng, ”A Survey on Training Algorithms for Support Vector Machine Classifiers,” Networked Computing and Advanced Information Management, pp. 123-128, 2008. [10] B. Minaei, M. Nasiri, D. Hasani, E. Shenasa, ”Data mining using clementine,” Saher Publications, 201

2 3 4

47.2% 43.1% 46.4%

50.2% 0% 0.9%

0.5% 54.9% 0%

2.1% 2% 52.7%

Table 5 Results of the K-means Method with Clustering when the Data of the Second and Third Subframes are also Used for Decoding ( , AMR4.75)

Detected Sent 1 2 3 4

1

2

3

4

99.4% 21.2% 21.6% 19.4%

0.4% 78.8% 2.7% 0%

0% 0% 75.7% 0%

0.1% 0% 0% 80.6%