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Burst ratio is a parameter that quantifies packet loss patterns in transmission networks. It has been defined for an end-to-end scenario, therefore burst ratio can be determined only if the characteristics of the whole transmission path are known. In this paper, the burst ratio parameter applicability to cases when the transmission path consists of a series of transmission channels with known packet loss rate and burst ratio values is extended. The paper also presents the results of simulations performed with NS2 software, demonstrating the validity of the burst ratio analysis. Consequently, the research makes it possible to determine the value of the burst ratio parameter in concatenated packet networks, which in turn supports delivering higher quality VoIP services. [ABSTRACT FROM AUTHOR]

The paper presents a practical implementation of the non-classified data hiding system (NDHS) understood as a military platform for information warfare that takes advantage of the hidden data transmission for voice connections in order to gain informational lead over a potential enemy. The NDHS performs here as a botnet network that is managed by the hidden transmission controller referred to as the master resident. Research studies are dedicated to investigation of various connections in heterogeneous links as well as functionalities of such components as hidden protocol bridges and the master resident

Voice over Internet Protocol (VoIP) and its underlying Session Initiation Protocol (SIP) are widely deployed technologies since they provide an efficient and fast means of both voice and data communication over a single network. However, in spite of their advantages, they also have their security threats due to the inherent vulnerabilities in the underlying Internet Protocol (IP) that can potentially be exploited by hackers. This study introduces a novel defense mechanism to effectively combat advanced attacks that exploit vulnerabilities identified in some less-known features of SIP. The SIP-DRDoS (SIP-based distributed reflection denial of service) attack, which can survive the existing security systems, is an advanced attack that can be performed on an SIP network through the multiplication of legitimate traffic. In this study, we propose a novel defense mechanism that consists of statistics, inspection, and action modules to mitigate the SIP-DRDoS attack. We implement the SIP-DRDoS attack by utilizing our SIP-based audit and attack software in our VoIP/SIP security lab environment that simulates an enterprise-grade SIP network. We then utilize our SIP-based defense tool to realize our novel defense mechanism against the SIP-DRDoS attack. Our experimental results prove that our defense approach can do a deep packet analysis for SIP traffic, detect SIP flood attacks, and mitigate them by dropping attack packets. While the SIP-DRDoS attack with around 1 Gbps of traffic dramatically escalates the CPU (central processing unit) usage of the SIP server by up to 74 % , our defense mechanism effectively reduces it down to 17 % within 6 min after the attack is initiated. Our approach represents a significant advancement over the existing defense mechanisms and demonstrates the potential to effectively protect VoIP systems against SIP-based DRDoS attacks. [ABSTRACT FROM AUTHOR]

In VoIP applications, such as Interactive Voice Response and VoIP-phone conversation transcription, speech signals are degraded not only by environmental noise but also by transmission network quality, and distortions induced by encoding and decoding algorithms. Therefore, there is a need for automatic speech recognition (ASR) systems to handle integrated noise-network distorted speech. In this study, we present a comparative analysis of a speech-to-text system trained on clean speech against one trained on integrated noise-network distorted speech. Training an ASR model on noise-network distorted speech dataset improves its robustness. Although the performance of an ASR model trained on clean speech depends on noise type, this is not the case when noise is further distorted by network transmission. The model trained on noise-network distorted speech exhibited a 60% improvement rate in the word error rate (WER), word match rate (MER), and word information lost (WIL) over the model trained on clean speech. Furthermore, the ASR model trained with noise-network distorted speech could tolerate a jitter of less than 20% and a packet loss of less than 15%, without a decrease in performance. However, WER, MER, and WIL increased in proportion to the jitter and packet loss as they exceeded 20% and 15%, respectively. Additionally, the model trained on noise-network distorted speech exhibited higher robustness compared to that trained on clean speech. The ASR model trained on noise-network distorted speech can also tolerate signal-to-noise (SNR) values of 5 dB and above, without the loss of performance, independent of noise type.

Voice over Internet Protocol (VoIP) and its underlying Session Initiation Protocol (SIP) are widely deployed technologies since they provide an efficient and fast means of both voice and data communication over a single network. However, in spite of their advantages, they also have their security threats due to the inherent vulnerabilities in the underlying Internet Protocol (IP) that can potentially be exploited by hackers. This study introduces a novel defense mechanism to effectively combat advanced attacks that exploit vulnerabilities identified in some less-known features of SIP. The SIP-DRDoS (SIP-based distributed reflection denial of service) attack, which can survive the existing security systems, is an advanced attack that can be performed on an SIP network through the multiplication of legitimate traffic. In this study, we propose a novel defense mechanism that consists of statistics, inspection, and action modules to mitigate the SIP-DRDoS attack. We implement the SIP-DRDoS attack by utilizing our SIP-based audit and attack software in our VoIP/SIP security lab environment that simulates an enterprise-grade SIP network. We then utilize our SIP-based defense tool to realize our novel defense mechanism against the SIP-DRDoS attack. Our experimental results prove that our defense approach can do a deep packet analysis for SIP traffic, detect SIP flood attacks, and mitigate them by dropping attack packets. While the SIP-DRDoS attack with around 1 Gbps of traffic dramatically escalates the CPU (central processing unit) usage of the SIP server by up to 74%, our defense mechanism effectively reduces it down to 17% within 6 min after the attack is initiated. Our approach represents a significant advancement over the existing defense mechanisms and demonstrates the potential to effectively protect VoIP systems against SIP-based DRDoS attacks.

In this paper, we introduce SPQER (pronounced speaker), a novel approach to evaluate the quality of experience for real-time Voice over IP (VoIP) communication in mobile and lossy networks. Traditional speech quality metrics, e.g., Perceptual Evaluation of Speech Quality (PESQ) or the Hearing-Aid Speech Quality Index (HASQI), directly compare frequencies and amplitudes to calculate the received signal distortions. SPQER instead uses machine learning classification to evaluate the percentage of recognizable words in conjunction with a time-based decay function to penalize delay and cross-talking. So instead of evaluating noise, SPQER directly answers the question: What percentage of words is the recipient able to understand? We presented a sensitivity analysis, which is based on testbed experiments for different packet loss rates and simulated delays, to asses the impact of challenging link conditions. A final correlation analysis to a short user study shows that SPQER can better evaluate the amount of understandable words than PESQ and HASQI, while still giving a more precise indication about the voice quality than the Word Error Rate (WER) metric.

Steganography in inactive Voice-over-IP frames is a new technique of information hiding, which can achieve large steganographic capacity while maintaining excellent imperceptibility. To prevent the illegitimate use of this technique, the entropy-based and poker test-based steganalysis methods have been presented. However, the detection performance of these two methods is not so good for the cases of having small quantity of inactive frames or low embedding rates. Thus, we present a new steganalysis method based on statistic characteristics of fundamental frequency. Specifically, we employ the statistics for zero-crossing count (ZCC), including the average ZCC of inactive frames, the ratio between the average ZCC of inactive frames and that of all frames, and the difference between the average ZCC of inactive frames and their calibrated versions, to characterize the frame-level dynamic characteristic of speech signals; we utilize the average values of Mel-frequency cepstral coefficients (MFCCs) to represent the invariant characteristic of inactive frames; further, using the feature set consisting of the zero-crossing statistics and average MFCCs, we propose a support-vector-machine based steganalysis for inactive speech frames. The proposed steganalysis method is evaluated with a large number of ITU-T G.723.1 encoded speech samples, and compared with the existing methods. The experimental results demonstrate that the proposed method significantly outperforms the previous ones on detection accuracy, false positive rate and false negative rate for any given embedding rates or using the same number of inactive frames. Particularly, the proposed method can provide accurate detecting results for the existing steganographic methods only using very small quantity of inactive frames, and thereby be employed to detecting potential inactive-frame steganography behaviors in real-time speech streams.

We introduce a novel SIP based attack, named as the SR-DRDoS attack, that exploits some less known SIP features by using the IP-spoofing technique, the reflection based attack logic and the DDoS attack logic. Furthermore, we develop a SIP-based DoS/DDoS attack simulator, named Mr. SIP, and use it to implement our SR-DRDoS attack. Our attack is shown to dramatically increase the CPU load of a SIP server from 0% up to 100% in only 4 minutes after the attack is initiated. Since our intelligent attack creates legitimate traffic on the SIP network by using reflection methods, it bypasses black-lists as well as IP, packet-count or session/transaction based rate limiting and automatic message generation detection systems which exist in state-of-the-art security perimeters such as firewalls, intrusion detection/prevention systems and anomaly detection systems. Moreover, we propose a novel defense mechanism that effectively mitigates our proposed DRDoS attack. Our defense mechanism is shown to successfully reduce the CPU load of a SIP server under attack from 71% down to 18% within 3 minutes after it is initiated.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.