Voice Over Internet Protocol Network Research Articles

Involved Security Solution in Voice over IP Networks

In this paper, the work involves setting up a secure VoIP solution. At first, we presented some attacks scenarios affecting confidentiality, authenticity and integrity in the VoIP network. In the second part, we focused on VOIP security themes, so our work has focused on areas where enhancing VoIP security is essential. The convergence towards everything of IP is now a reality. The efforts made by infrastructure providers, whether fixed, mobile, and cabled or wireless. This is also evident in operators who now only design the services they offer in the context of everything IP. But with the increasing use of Voice over IP (VoIP) technology, services and protocols are becoming increasingly vulnerable. In this context work several organizations that promote the research and development of network security, warn users of the attacks risks that may affect the communications confidentiality. This work proposed to explore the security vulnerabilities associated with VoIP networks and to present some solutions to avoid and minimize these threats.

A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems

Voice over Internet Protocol (VoIP) is a recent voice communication technology and due to its variety of calling capabilities, the system is expected to fuel the market value even further in the next five years. However, there are serious concerns since VoIP systems are frequently been attacked. According to recent security alliance reports, malicious activities have increased largely during the current pandemic against VoIP and other vulnerable networks. This hence implies that existing models are not sufficiently reliable since most of them do not have a hundred percent detection rate. In this paper, a review of our most recent Intrusion Detection & Prevention Systems (IDPS) developed is proposed together with a comparative analysis. The final work consisted of ten models which addressed flood intentional attacks to mitigate VoIP attacks. The methodological approaches of the studies included the quantitative and scientific paradigms, for which several instruments (comparative analysis and experiments) were used. Six prevention models were developed using three sorting methods combined with either a modified galloping algorithm or an extended quadratic algorithm. The seventh IDPS was designed by improving an existing genetic algorithm (e-GAP) and the eighth model is a novel deep learning method known as the Closest Adjacent Neighbour (CAN). Finally, for a better comparative analysis of AI-based algorithms, a Deep Analysis of the Intruder Tracing (DAIT) model using a bottom-up approach was developed to address the issues of processing time, effectiveness, and efficiency which were challenges when addressing very large datasets of incoming messages. This novel method prevented intruders to access a system without authorization and avoided any anomaly filtering at the firewall with a minimum processing time. Results revealed that the DAIT and the e-GAP models are very efficient and gave better results when benchmarking with models. These two models obtained an F-score of 98.83%, a detection rate of 100%, a false rate of 0%, an accuracy of 98.7%, and finally a processing time per message of 0.092 ms and 0.094 ms respectively. When comparing with previous models in the literature from which it is specified that detection rates obtained are 95.5% and false-positive alarm of around 1.8%, except for one recent machine learning-based model having a detection rate of 100% and a processing time of 0.53 ms, the DAIT and the e-GAP models give better results.

Implementasi Mos dan E-Model Untuk Mengetahui Kualitas Komunikasi Jaringan VoIP

Voice Over Internet Protocol (VoIP) is a voice communication technology (voice) that uses IP which can be done electronically and in realtime. In building VoIP devices used such as IPPBX Server, IPPBX Gateway IP and other supporting devices such as mikrotik. To design VoIP generally implemented using a local area network (LAN) or a wireless local area network (WLAN). In this study, an analysis of the quality of VoIP network communication is to determine the problems caused by communication using VoIP, so that VoIP can be used to maximize the existing internet network. This VoIP network can save costs when communicating such as video communications or video conferencing.

Optimizing VoIP server resources using linear programming model and autoscaling technique: An SDN approach

SummaryNowadays, the Voice Over IP (VoIP) technology is an important component of the communications industry as well as a low‐cost alternative to Public Switched Telephone Networks. Communication in VoIP networks consists of two main phases, for example, signaling and media exchange. VoIP servers are responsible for signaling exchange using the Session Initiation Protocol (SIP) as the signaling protocol. The saturation of SIP server resources is one of the issues with the VoIP network, which causes problems such as overload or loss of energy. Resource saturation occurs mainly due to a lack of integrated server resource management. In the traditional VoIP networks, management and routing are distributed among all equipment, including servers. These servers are overloaded during peak times and face energy loss during idle times. Given the importance of this issue, this paper introduces a framework based on Software‐Defined Networking technology for SIP server resource management. The advantage of this framework is to have a global view of all the server resources. In this framework, the resource allocation optimization problem and resource autoscaling are presented to deal with the problems posed. The goal is to maximize total throughput and minimize energy consumption. In this regard, we seek to strike a balance between efficiency and energy. The proposed framework is implemented in the actual testbed. The results show that the proposed framework has succeeded in achieving these goals.

Security and Challenges in Voice over Internet Protocols: A Survey

Voice over Internet Protocol (VoIP) an emerging new technologies is based on realtime communication of data by sending and receiving digital signals using Internet Protocols (IP). VoIP has become popular in business organization and scientific communities. Voice coder (Codec) named as coder and decoder, play an important role in packet switch network for transfer of audio and video data. A signalling protocol known as Session Initiation Protocol (SIP) is used for data communication between nodes in VoIP network. In this paper we survey on security related issues in VoIP communication system and covered several security attacks. We proceed with survey on all the problems that have big effects on VoIP security and challenges such as data manipulation, substitution attack, power backups, jitter, latency and delay. Finally, we conclude the paper with further research on security of VoIP communication system.

Fast Detection of Distributed Denial of Service Attacks in VoIP Networks Using Convolutional Neural Networks

Voice over Internet Protocol (VoIP) is a recent technology used to transfer media and voice over Internet Protocol (IP). Many organizations moved to VoIP services instead of the traditional telephone systems because of its low cost and variety of introduced services. The Session Initiation Protocol (SIP) is the most used protocol for signaling functions in VoIP networks. It has simple implantation but suffers from less protection against attacks. The Distributed Denial of Service (DDoS) attack is a dangerous attack that preventing legitimate users from using VoIP services and draining their resources. In this paper, we proposed an approach that utilizes deep learning to detect DDoS attacks. The proposed approach uses token embedding to improve the extracted features of SIP messages. Then, Convolutional Neural Network (CNN) was used to detect DDoS attacks with different intensities. Furthermore, a real VoIP dataset that contains different scenarios of attacks was used to evaluate the proposed approach. Our experiments find that the CNN model achieved a high F1 score (99-100%) as another deep learning approach that utilizes Recurrent Neural Network (RNN) but with less detection time. Also, it outperforms another system that depends on classical machine learning in case of low-rate DDoS attacks.

A Diagnosis and Hardening Platform for an Asterisk VoIP PBX

Voice over IP (VoIP) is a set of software and hardware technologies used for making voice calls over the Internet. VoIP has been massively deployed in corporative environments since voice and data network convergence enables unified communication services while reducing costs. The main component of a VoIP network infrastructure is the private branch exchange (PBX). Nowadays, Asterisk is the most widespread PBX deployed within corporations due to its open access technology, along with its modular and flexible design. The configuration of PBX systems usually relies on multiple configuration files composed of a vast number of parameters that may have an impact on the security of the system. Therefore, the setup of such systems tends to be complicated and prone to errors and usually requires highly specialized human intervention. In this research, a diagnosis platform for discovering vulnerabilities and security breaches in the configuration of an Asterisk PBX is presented. The proposed platform performs both reactive and proactive actions in order to reconfigure and harden an Asterisk PBX. Firstly, the platform reacts after certain events by modifying the configuration of the Asterisk PBX in order to mitigate risks. Secondly, the platform performs several on-demand assessments that also reconfigure the Asterisk PBX to improve overall security. Finally, the functionality of the platform is easily extensible and highly customizable. Extensive tests have been carried out to assess the security and performance of the Asterisk PBX when facing attacks. Results show that the security of the platform increases, avoiding performance degradation when using the proposed platform.

Survey of Countering DoS/DDoS Attacks on SIP Based VoIP Networks

Voice over IP (VoIP) services hold promise because of their offered features and low cost. Most VoIP networks depend on the Session Initiation Protocol (SIP) to handle signaling functions. The SIP is a text-based protocol that is vulnerable to many attacks. Denial of Service (DoS) and distributed denial of service (DDoS) attacks are the most harmful types of attacks, because they drain VoIP resources and render SIP service unavailable to legitimate users. In this paper, we present recently introduced approaches to detect DoS and DDoS attacks, and classify them based on various factors. We then analyze these approaches according to various characteristics; furthermore, we investigate the main strengths and weaknesses of these approaches. Finally, we provide some remarks for enhancing the surveyed approaches and highlight directions for future research to build effective detection solutions.

Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks.

Many companies have transformed their telephone systems into Voice over IP (VoIP) systems. Although implementation is simple, VoIP is vulnerable to different types of attacks. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions. SIP is unprotected against attacks because it is a text-based protocol and lacks defense against the growing security threats. The Distributed Denial of Service (DDoS) attack is a harmful attack, because it drains resources, and prevents legitimate users from using the available services. In this paper, we formulate detection of DDoS attacks as a classification problem and propose an approach using token embedding to enhance extracted features from SIP messages. We discuss a deep learning model based on Recurrent Neural Networks (RNNs) developed to detect DDoS attacks with low and high-rate intensity. For validation, a balanced real traffic dataset was built containing three attack scenarios with different attack durations and intensities. Experiments show that the system has a high detection accuracy and low detection time. The detection accuracy was higher for low-rate attacks than that of traditional machine learning.

Green Cloud Multimedia Networking: NFV/SDN Based Energy-Efficient Resource Allocation

The rapid growth of communications and multimedia network services such as Voice over Internet Protocol (VoIP) have caused these networks to face a crisis in resources from two perspectives: 1. Lack of resources and, as a result, overload; 2. Redundancy of resources and, as a result, energy loss. Cloud computing allows the scale of resources to be reduced or increased on demand. Many of the gains obtained from the cloud computing come from resource sharing and virtualization technology. On the other hand, the emerging concept of Software-Defined Networking (SDN) can provide a global view of the entire network for integrated resource management. Network Function Virtualization (NFV) can also be used to virtually implement a variety of network devices and functions. In this paper, we present an energy-efficient framework called GreenVoIP to manage the resources of virtualized cloud VoIP centers. By managing the number of VoIP servers and network equipment, such as switches, this framework not only prevents overload but also supports green computing by saving energy. Finally, GreenVoIP is implemented and evaluated on real platforms, including Floodlight, Open vSwitch, and Kamailio. The results suggest that the proposed framework can minimize the number of active devices, prevent overloading, and provide service quality requirements.

Enhancing VoIP BW Utilization over ITTP Protocol

The revolution of Voice over Internet Protocol (VoIP) technology has propagated everywhere and replaced the conventional telecommunication technology (e.g. landline). Nevertheless, several enhancements need to be done on VoIP technology to improve its performance. One of the main issues is to improve the VoIP network bandwidth (BW) utilization. VoIP packet payload compression is one of the key approaches to do that. This paper proposes a new method to compress VoIP packet payload. The suggested method works over internet telephony transport protocol (ITTP) and named Delta-ITTP method. The core idea of the Delta-ITTP method is to find and transmit the delta between the successive VoIP packet payloads, which is typically smaller than the original VoIP packet payload. The suggested Delta-ITTP method implements VoIP packet payload compression at the sender side and decompression at the receiver side. During the compression process, the Delta-ITTP method needs to keep some values to restore the original VoIP packet payload at the receiver side. For this, the Delta-ITTP method utilizes some of the IP protocol fields and no additional header is needed. The Delta-ITTP method has been deployed and compared with the traditional ITTP protocol without compression. The result showed that up to 19% BW saving was achieved in the tested cases leading to the desired enhancement in the VoIP network BW utilization.

Evaluating QoE in VoIP networks with QoS mapping and machine learning algorithms

The quality of experience (QoE) of the end-users is a critical criterion of measurement in VoIP (Voice over Internet Protocol) systems for technical and commercial purposes. We investigate how quality of service (QoS) influences QoE and assesses the QoE in VoIP communication. Our contributions are three-fold. First, the impacts of QoS on QoE are comprehensively analyzed by experimental means and an association test method, instead of independently studying each parameter. Second, an algorithm is proposed to integrate the effects of QoS parameters with spatial or temporal characteristics on QoE. Third, we apply machine learning regression algorithms with QoS impairments, noise and echo impairments to nonintrusive voice quality prediction in different network environments. The results from numerous experiments show that fairly accurate prediction can be obtained from these models. Our work will achieve a more accurate evaluation of the QoE in VoIP by using QoS parameters, clarify the influence of IP network environments, noise and echo impairments on the quality and reliability of VoIP traffic, and provide QoS parameter requirements for the VoIP application that runs at the desired QoE level.

Perfect forward secrecy via an ECC-based authentication scheme for SIP in VoIP

With the advent of the internet, Voice over Internet Protocol (VoIP) has obtained a considerable amount of attention due to its low cost, and ease of implementation. Similar to other emerging technologies, VoIP faces several challenges, including security in terms of confidentiality, integrity, and authenticity. Specifically, there is a need for secure and efficient authentication and key agreement scheme to address the security requirements of communications over VoIP networks. Recently, Ravanbakhsh et al. have presented an authentication and key agreement protocol for VoIP networks. Here, in this article, we first prove that Ravanbakhsh et al.’s scheme cannot provide perfect forward secrecy. Next, we present an elliptic curve cryptography-based secure two-factor authentication and key agreement scheme. We analyze the security of the proposed scheme informally, and demonstrate that the proposed scheme can provide different security features, including perfect forward secrecy, and is robust against security attacks such as the impersonation attack, the replay attack, and the stolen-verifier attack. Furthermore, we simulate the protocol and analyze its security formally using Scyther tool. The results show its robustness against different attacks, and its ability to provide perfect forward secrecy. We compare the computation cost of the proposed scheme with the related schemes. Results show that the proposed scheme achieves a satisfiable performance comparable to other ECC-based methods.

NIML: non‐intrusive machine learning‐based speech quality prediction on VoIP networks

Voice over Internet Protocol (VoIP) networks have recently emerged as a promising telecommunication medium for transmitting voice signal. One of the essential aspects that interests researchers is how to estimate the quality of transmitted voice over VoIP for several purposes such as design and technical issues. Two methodologies are used to evaluate the voice, which are subjective and objective methods. In this study, the authors propose a non-intrusive machine learning-based (NIML) objective method to estimate the quality of voice. In particular, they build a training set of parameters – from the network and the voice itself – along with the quality of voices as labels. The voice quality is estimated using the perceptual evaluation of speech quality (PESQ) method as an intrusive algorithm. Then, the authors use a set of classifiers to build models for estimating the quality of the transmitted voice from the training set. The experimental results show that the classifier models have a valuable performance where Random Forest model has superior results compared to other models of precision 94.1%, recall 94.2%, and receiver operating characteristic area 99.2% as evaluation metrics.

A Comprehensive Study of DSCP Markings’ Impact on VOIP QoS in HFC Networks

Various factors can have a significant degrading impact on the residential Voice over Internet Protocol (VoIP) phone services’ quality. Hybrid fibre- coaxial (HFC) networks typically carry three types of traffic that include voice, data, and video. Unlike data and video, some delays or packet loss can result in a noticeable degraded impact on a VoIP’s phone conversation. This paper will analyze and assess VoIP traffic prioritization and its impact on VoIP’s quality of service (QoS) based on the concept of differentiated services code point (DSCP) markings. Call testing examines two types of calls. The first set of tests focus on calls that originate from a VoIP network and terminate on a signalling system 7 (SS7) network. The second experiment focuses on calls that originate from SS7 network and terminate on a VoIP network. The research results provide DSCP markings configurations that can improve phone conversations’ quality.

A Comprehensive Study of Dscp Markings’ Impact on Voip Qos in Hfc Networks

Various factors can have a significant degrading impact on the residential Voice over Internet Protocol (VoIP) phone services’ quality. Hybrid fibre- coaxial (HFC) networks typically carry three types of traffic that include voice, data, and video. Unlike data and video, some delays or packet loss can result in a noticeable degraded impact on a VoIP’s phone conversation. This paper will analyze and assess VoIP traffic prioritization and its impact on VoIP’s quality of service (QoS) based on the concept of differentiated services code point (DSCP) markings. Call testing examines two types of calls. The first set of tests focus on calls that originate from a VoIP network and terminate on a signalling system 7 (SS7) network. The second experiment focuses on calls that originate from SS7 network and terminate on a VoIP network. The research results provide DSCP markings configurations that can improve phone conversations’ quality.

Efficient Detection of Attacks in SIP Based VoIP Networks Using Linear l1-SVM Classifier

The Session Initiation Protocol (SIP) is one of the most common protocols that are used for signaling function in Voice over IP (VoIP) networks. The SIP protocol is very popular because of its flexibility, simplicity, and easy implementation, so it is a target of many attacks. In this paper, we propose a new system to detect the Denial of Service (DoS) attacks (i.e. malformed message and invite flooding) and Spam over Internet Telephony (SPIT) attack in the SIP based VoIP networks using a linear Support Vector Machine with l1 regularization (i.e. l1-SVM) classifier. In our approach, we project the SIP messages into a very high dimensional space using string based n-gram features. Hence, a linear classifier is trained on the top of these features. Our experimental results show that the proposed system detects malformed message, invite flooding, and SPIT attacks with a high accuracy. In addition, the proposed system outperformed other systems significantly in the detection speed.

Optimizing Branch Telephone Networks for Campus VoIP with Mobile Clients

During working hours, employees on campus are not always in their room because of other activities in other rooms both in one building and in different buildings. In this study, the development of a branch telephone communication network or private branch exchange (PBX) uses voice over internet protocol (VoIP) technology with a mobile-based client device and utilizes the internet as a media. For system design, network development life cycle (NDLC) method is used where VoIP communication network is monitored with paessler router traffic grapher (PRTG) software to see quality of service (QoS) values with packet loss, delay, and jitter parameters. The results of the monitoring of each parameter are of perfect average which can be seen from the amount of packet loss value of 0%; delay below 150 msec for sending data packets from the transmitter to the receiver; and jitter in a proper category with a time below 75 msec. However, for better development, the installation and configuration of servers that have been created virtually can be implemented on the actual server system; IP usage which is devoted to VoIP communication networks; bandwidth management to provide a separate traffic line for the VoIP communication network that is created.

Perfect forward secrecy in VoIP networks through design a lightweight and secure authenticated communication scheme

With the growth of the internet, development of IP based services has increased. Voice over IP (VoIP) technology is one of the services which works based on the internet and packet switching networks and uses this structure to transfer the multimedia data e.g. voices and images. Recently, Chaudhry et al., Zhang et al. and Nikooghadam et al. have presented three authentication and key agreement protocols, separately. However, in this paper, it is proved that the presented protocols by Chaudhry et al. and also Nikooghadam et al. do not provide the perfect forward secrecy, and the presented protocol by Zhang et al. not only is vulnerable to replay attack, and known session-specific temporary information attack, but also does not provide user anonymity, re-registration and revocation, and violation of fast error detection. Therefore, a secure and efficient two-factor authentication and key agreement protocol is presented. The security analysis proves that our proposed protocol is secure against various attacks. Furthermore, security of proposed scheme is formally analyzed using BAN logic and simulated by means of the AVISPA tool. The simulation results demonstrate security of presented protocol against active and passive attacks. The communication and computation cost of the proposed scheme is compared with previously proposed authentication schemes and results confirm superiority of the proposed scheme.

Dynamic voice spammers detection using Hidden Markov Model for Voice over Internet Protocol network

Voice over Internet Protocol (VoIP) provides flexible and cost effective services. These services are used by voice spammers to generate unsolicited voice calls. Earlier research declared that the VoIP services are misused for making prank calls, product promotion calls and credit card services. This results in customer dissatisfaction and the financial losses in the bank and telecommunication sectors. Thus, detection of spammers is an essential task to enhance the quality of services in VoIP network. Spammers occur in the form of either human spammer or computer spammer and mimic as the legitimate caller. Voice spammer's states in successive time period are dynamic and dependent, particularly the human voice spammer exhibit high degree of dynamism. This poses challenge for traditional spam detection algorithms. In this paper, a Dynamic Voice Spammer Detection Model (DVSDM) based on the Hidden Markov Model (HMM) is proposed. This model estimates voice spammer's states by using various behaviour variables and detects the voice spammers before reaching the victim. The performance of this detection model is experimentally evaluated with two scenarios (mild and heavy distribution of voice spam calls). The proposed model achieves a False Positive Rate (FPR) of less than 2% and 5% for heavy and mild distribution of voice spam calls respectively. Moreover, the DVSDM model achieves a True Positive Rate (TPR) of 95% for heavy and 92% for mild distribution of voice spam calls.