A Machine Learning Approach for Prediction of Signaling SIP Dialogs

POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095 UIDB/EEA/50008/2020In this paper, we propose a machine learning methodology for prediction of signaling sessions established with the Session Initiation Protocol (SIP). Given the increasing importance of predicting and detecting abnormal sequences of SIP messages to avoid SIP signaling-based attacks, we first propose a Bayesian inference method capable of representing the statistical relation between a SIP message, observed by a SIP user agent or a SIP server, and prior trustworthy SIP dialogs. The Bayesian inference method, a Hidden Markov Model (HMM) enriched with $n-$ gram Markov observations, is updated over time, so the inference can be used in real-time. The HMM is then used for predicting and detecting SIP dialogs through a lightweight implementation of Viterbi algorithm for sparse state spaces. Experimental results are also reported, where a SIP dataset representing prior information collected by a SIP user agent and/or a SIP server is used to predict or detect if a received sequence of SIP messages is legitimate according to similar SIP dialogs already observed. Finally, we discuss the results obtained for a dataset of abnormal SIP sequences, not observed during the inference stage, showing the effective utility of the proposed methodology to detect abnormal SIP sequences in a short period of time.publishersversionpublishe

Autonomic Overload Management For Large-Scale Virtualized Network Functions

The explosion of data traffic in telecommunication networks has been impressive in the last few years. To keep up with the high demand and staying profitable, Telcos are embracing the Network Function Virtualization (NFV) paradigm by shifting from hardware network appliances to software virtual network functions, which are expected to support extremely large scale architectures, providing both high performance and high reliability. The main objective of this dissertation is to provide frameworks and techniques to enable proper overload detection and mitigation for the emerging virtualized software-based network services. The thesis contribution is threefold. First, it proposes a novel approach to quickly detect performance anomalies in complex and large-scale VNF services. Second, it presents NFV-Throttle, an autonomic overload control framework to protect NFV services from overload within a short period of time, allowing to preserve the QoS of traffic flows admitted by network services in response to both traffic spikes (up to 10x the available capacity) and capacity reduction due to infrastructure problems (such as CPU contention). Third, it proposes DRACO, to manage overload problems arising in novel large-scale multi-tier applications, such as complex stateful network functions in which the state is spread across modern key-value stores to achieve both scalability and performance. DRACO performs a fine-grained admission control, by tuning the amount and type of traffic according to datastore node dependencies among the tiers (which are dynamically discovered at run-time), and to the current capacity of individual nodes, in order to mitigate overloads and preventing hot-spots. This thesis presents the implementation details and an extensive experimental evaluation for all the above overload management solutions, by means of a virtualized IP Multimedia Subsystem (IMS), which provides modern multimedia services for Telco operators, such as Videoconferencing and VoLTE, and which is one of the top use-cases of the NFV technology

A Comprehensive Survey of Voice over IP Security Research

We present a comprehensive survey of Voice over IP security academic research, using a set of 245 publications forming a closed cross-citation set. We classify these papers according to an extended version of the VoIP Security Alliance (VoIPSA) Threat Taxonomy. Our goal is to provide a roadmap for researchers seeking to understand existing capabilities and to identify gaps in addressing the numerous threats and vulnerabilities present in VoIP systems. We discuss the implications of our findings with respect to vulnerabilities reported in a variety of VoIP products. We identify two specific problem areas (denial of service, and service abuse) as requiring significant more attention from the research community. We also find that the overwhelming majority of the surveyed work takes a black box view of VoIP systems that avoids examining their internal structure and implementation. Such an approach may miss the mark in terms of addressing the main sources of vulnerabilities, i.e., implementation bugs and misconfigurations. Finally, we argue for further work on understanding cross-protocol and cross-mechanism vulnerabilities (emergent properties), which are the byproduct of a highly complex system-of-systems and an indication of the issues in future large-scale systems

Secure Service Provisioning (SSP) Framework for IP Multimedia Subsystem (IMS)

Mit dem Erscheinen mobiler Multimediadienste, wie z. B. Unified Messaging, Click-to-Dial-Applikationen, netzwerkübergeifende Multimedia-Konferenzen und nahtlose Multimedia-Streming-Dienste, begann die Konvergenz von mobilen Kommunikationsetzen und Festnetzen, begleitet von der Integration von Sprach- und Datenkommunikations-Übertragungstechnik Diese Entwicklungen bilden die Voraussetzung für die Verschmelzung des modernen Internet auf der einen Seite mit der Telekommunikation im klassischen Sinne auf der anderen. Das IP Multimedia-Subsystem (IMS) darf hierbei als die entscheidende Next-Generation-Service-Delivery-Plattform in einer vereinheitlichten Kommunikationswelt angesehen werden. Seine Architektur basiert auf einem modularen Design mit offenen Schnittstellen und bietet dedizierte Voraussetzungen zur Unterstützung von Multimedia-Diensten auf der Grundlage der Internet-Protokolle. Einhergehend mit dieser aufkommenden offenen Technologie stellen sich neue Sicherheits-Herausforderungen in einer vielschichtigen Kommunikationsinfrastruktur, im Wesentlichen bestehend aus dem Internet Protokoll (IP), dem SIP-Protokoll (Session Initiation Protocol) und dem Real-time Transport Protokoll (RTP). Die Zielsetzung des Secure Service Provisioning-Systems (SSP) ist, mögliche Angriffsszenarien und Sicherheitslücken in Verbindung mit dem IP Multimedia Subsystem zu erforschen und Sicherheitslösungen, wie sie von IETF, 3GPP und TISPAN vorgeschlagen werden, zu evaluieren. Im Rahmen dieser Forschungsarbeit werden die Lösungen als Teil des SSP-Systems berücksichtigt, mit dem Ziel, dem IMS und der Next-Generation-SDP einen hinreichenden Schutz zu garantieren. Dieser Teil, der als Sicherheitsschutzstufe 1 bezeichnet wird, beinhaltet unter anderem Maßnahmen zur Nutzer- und Netzwerk-Authentifizierung, die Autorisierung der Nutzung von Multimediadiensten und Vorkehrungen zur Gewährleistung der Geheimhaltung und Integrität von Daten im Zusammenhang mit dem Schutz vor Lauschangriffen, Session-Hijacking- und Man-in-the-Middle-Angriffen. Im nächsten Schritt werden die Beschränkungen untersucht, die für die Sicherheitsschutzstufe 1 charakteristisch sind und Maßnahmen zu Verbesserung des Sicherheitsschutzes entwickelt. Die entsprechenden Erweiterungen der Sicherheitsschutzstufe 1 führen zu einem Intrusion Detection and Prevention-System (IDP), das Schutz vor Denial-of-Service- (DoS) / Distributed-Denial-of-Service (DDoS)-Angriffen, missbräuchlicher Nutzung und Täuschungsversuchen in IMS-basierten Netzwerken bietet. Weder 3GPP noch TISPAN haben bisher Lösungen für diesen Bereich spezifiziert. In diesem Zusammenhang können die beschriebenen Forschungs- und Entwicklungsarbeiten einen Beitrag zur Standardisierung von Lösungen zum Schutz vor DoS- und DDoS-Angriffen in IMS-Netzwerken leisten. Der hier beschriebene Ansatz basiert auf der Entwicklung eines (stateful / stateless) Systems zur Erkennung und Verhinderung von Einbruchsversuchen (Intrusion Detection and Prevention System). Aus Entwicklungssicht wurde das IDP in zwei Module aufgeteilt: Das erste Modul beinhaltet die Basisfunktionen des IDP, die sich auf Flooding-Angriffe auf das IMS und ihre Kompensation richten. Ihr Ziel ist es, das IMS-Core-Netzwerk und die IMS-Ressourcen vor DoS- und DDoS-Angriffen zu schützen. Das entsprechende Modul basiert auf einer Online Stateless-Detection-Methodologie und wird aktiv, sobald die CPU-Auslastung der P-CSCF (Proxy-Call State Control Function) einen vordefinierten Grenzwert erreicht oder überschreitet. Das zweite Modul (IDP-AS) hat die Aufgabe, Angriffe, die sich gegen IMS Application Server (AS) richten abzufangen. Hierbei konzentrieren sich die Maßnahmen auf den Schutz des ISC-Interfaces zwischen IMS Core und Application Servern. Das betreffende Modul realisiert eine Stateful Detection Methodologie zur Erkennung missbräuchlicher Nutzungsaktivitäten. Während der Nutzer mit dem Application Server kommuniziert, werden dabei nutzerspezifische Zustandsdaten aufgezeichnet, die zur Prüfung der Legitimität herangezogen werden. Das IDP-AS prüft alle eingehenden Requests und alle abgehenden Responses, die von IMS Application Servern stammen oder die an IMS Application Server gerichtet sind, auf ihre Zulässigkeit im Hinblick auf die definierten Attack Rules. Mit Hilfe der Kriterien Fehlerfreiheit und Processing Delay bei der Identifikation potenzieller Angriffe wird die Leistungsfähigkeit der IDP-Module bewertet. Für die entsprechenden Referenzwerte werden hierbei die Zustände Nomallast und Überlast verglichen. Falls die Leistungsfähigkeit des IDP nicht unter den Erwartungen zurückbleibt, wird ein IDP-Prototyp zur Evaluation im Open IMS Playground des Fokus Fraunhofer 3Gb-Testbeds eingesetzt, um unter realen Einsatzbedingungen z. B. in VoIP-, Videokonferenz- , IPTV-, Presence- und Push-to-Talk-Szenarien getestet werden zu können.With the emergence of mobile multimedia services, such as unified messaging, click to dial, cross network multiparty conferencing and seamless multimedia streaming services, the fixed–mobile convergence and voice–data integration has started, leading to an overall Internet–Telecommunications merger. The IP Multimedia Subsystem (IMS) is considered as the next generation service delivery platform in the converged communication world. It consists of modular design with open interfaces and enables the flexibility for providing multimedia services over IP technology. In parallel this open based emerging technology has security challenges from multiple communication platforms and protocols like IP, Session Initiation Protocol (SIP) and Real-time Transport Protocol (RTP). The objective of Secure Service Provisioning (SSP) Framework is to cram the potential attacks and security threats to IP Multimedia Subsystem (IMS) and to explore security solutions developed by IETF, 3GPP and TISPAN. This research work incorporates these solutions into SSP Framework to secure IMS and next generation Service Delivery Platform (SDP). We define this part as level 1 security protection which includes user and network authentication, authorization to access multimedia services, providing confidentiality and integrity protection etc. against eavesdropping, session hijacking and man-in-the middle attacks etc. In the next step, we have investigated the limitations and improvements to level 1 security and proposed the enhancement and extension as level 2 security by developing Intrusion Detection and Prevention (IDP) system against Denial-of-Service (DoS)/Distributed DoS (DDoS) flooding attacks, misuses and frauds in IMS-based networks. These security threats recently have been identified by 3GPP and TISPAN but no solution is recommended and developed. Therefore our solution may be considered as recommendation in future. Our approach based on developing both stateless and stateful intrusion detection and prevention system. From development point of view, we have divided the work into two modules: the first module is IDP-Core; addressing and mitigating the flooding attacks in IMS core. Its objective is to protect the IMS resources and IMS-core entities from DoS/DDoS flooding attacks. This module based on online stateless detection methodology and activates when CPU processing load of P-CSCF (Proxy-Call State Control Function) reaches or crosses the defined threshold limit. The second module is IDP-AS; addressing and mitigating the misuse attacks facing to IMS Application Servers (AS). Its focus is to secure the ISC interface between IMS Core and Application Servers. This module is based on stateful misuse detection methodology by creating and comparing user state (partner) when he/she is communicating with application server to check whether user is performing legitimate or illegitimate action with attacks rules. The IDP-AS also compared the incoming request and outgoing response to and from IMS Application Servers with the defined attacks rules. In the performance analysis, the processing delay and attacks detection accuracy of both Intrusion Detection and Prevention (IDP) modules have been measured at Fraunhofer FOKUS IMS Testbed which is developed for research purpose. The performance evaluation based on normal and overload conditions scenarios. The results showed that the processing delay introduced by both IDP modules satisfied the standard requirements and did not cause retransmission of SIP REGISTER and INVITE requests. The developed prototype is under testing phase at Fraunhofer FOKUS 3Gb Testbed for evaluation in real world communication scenarios like VoIP, video conferencing, IPTV, presence, push-to-talk etc

Classification of Abnormal Signaling SIP Dialogs through Deep Learning

POCI-01-0145-FEDER-030433 UIDB/50008/2020 PRT/BD/152200/2021Due to the high utilization of the Session Initiation Protocol (SIP) in the signaling of cellular networks and voice over IP multimedia systems, the avoidance of security vulnerabilities in SIP systems is a major aspect to assure that the operators can reach satisfactory readiness levels of service. This work is focused on the detection and prediction of abnormal signaling SIP dialogs as they evolve. Abnormal dialogs include two classes: the ones observed so far and thus labeled as abnormal and already known, but also the unknown ones, i.e., specific sequences of SIP messages never observed before. Taking advantage of recent advances in deep learning, we use Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) to detect and predict dialogs already observed. Additionally, and based on the outputs of the LSTM neural network, we propose two different classifiers capable of identifying unknown SIP dialogs, given the high level of vulnerability they may represent for the SIP operation. The proposed approaches achieve higher SIP dialogs detection scores in a shorter time when compared to a reference probabilistic-based approach. Moreover, the proposed detectors of unknown SIP dialogs achieve a detection probability above 94%, indicating its capability to detect a significant number of unknown SIP dialogs in a short amount of time.publishersversionpublishe

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii