SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis

In this paper, we propose a novel approach, called SENATUS, for joint traffic anomaly detection and root-cause analysis. Inspired from the concept of a senate, the key idea of the proposed approach is divided into three stages: election, voting and decision. At the election stage, a small number of \nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{, which are used} to represent approximately the total (usually huge) set of traffic flows. In the voting stage, anomaly detection is applied on the senator flows and the detected anomalies are correlated to identify the most possible anomalous time bins. Finally in the decision stage, a machine learning technique is applied to the senator flows of each anomalous time bin to find the root cause of the anomalies. We evaluate SENATUS using traffic traces collected from the Pan European network, GEANT, and compare against another approach which detects anomalies using lossless compression of traffic histograms. We show the effectiveness of SENATUS in diagnosing anomaly types: network scans and DoS/DDoS attacks

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

Flow-oriented anomaly-based detection of denial of service attacks with flow-control-assisted mitigation

Flooding-based distributed denial-of-service (DDoS) attacks present a serious and major threat to the targeted enterprises and hosts. Current protection technologies are still largely inadequate in mitigating such attacks, especially if they are large-scale. In this doctoral dissertation, the Computer Network Management and Control System (CNMCS) is proposed and investigated; it consists of the Flow-based Network Intrusion Detection System (FNIDS), the Flow-based Congestion Control (FCC) System, and the Server Bandwidth Management System (SBMS). These components form a composite defense system intended to protect against DDoS flooding attacks. The system as a whole adopts a flow-oriented and anomaly-based approach to the detection of these attacks, as well as a control-theoretic approach to adjust the flow rate of every link to sustain the high priority flow-rates at their desired level. The results showed that the misclassification rates of FNIDS are low, less than 0.1%, for the investigated DDOS attacks, while the fine-grained service differentiation and resource isolation provided within the FCC comprise a novel and powerful built-in protection mechanism that helps mitigate DDoS attacks

On the Robustness of Router-based Denial-of-Service (DoS) Defense Systems

This paper focuses on router-based defense mechanisms, and whether they can provide effective solutions to network Denial-of-Service (DoS) attacks. Router-based defenses operate either on traffic aggregates or on individual flows, and have been shown, either alone or in combination with other schemes, e.g., traceback, to be reasonably effective against certain types of basic attacks. Those attacks are, however, relatively brute-force, and usually accompanied by either significant increases in congestion, and/or traffic patterns that are easily identified. It is, therefore, unclear if router-based solutions are viable in the presence of more diverse or sophisticated attacks. As a result, even if incorporating defense mechanisms in the routers themselves has obvious advantages, such schemes have not seen wide deployments. Our ultimate goal is to determine whether it is possible to build router-based defense mechanisms that are effective against a wide range of attacks. This paper describes a first phase of this effort aimed at identifying weaknesses in existing systems. In particular, the paper demonstrates that aggregate defense systems can be readily circumvented, even by a single attacker, through minor modifications of its flooding patterns. Flow-based defenses fare slightly better, but can still be easily fooled by a small number of attackers generating transient flooding patterns. The findings of the paper provide insight into possible approaches for designing better and more robust router-based defense systems

Network Intrusion Detection and Mitigation Against Denial of Service Attack

The growing use of Internet service in the past few years have facilitated an increase in the denial of service (DoS) attacks. Despite the best preventative measures, DoS attacks have been successfully carried out against high-prole organizations and enterprises, including those that took down Chase, BOA, PNC and other major US banks in September 2009, which reveal the vulnerability of even well equipped networks. These widespread attacks have resulted in significant loss of service, money, and reputation for organizations, calling for a practical and ecient solution to DoS attack detection and mitigation. DoS attack detection and mitigation strengthens the robustness and security of network or computer system, by monitoring system activities for suspicious behaviors or policy violations, providing forensic information about the attack, and taking defensive measures to reduce the impact on the system. In general, attacks can be detected by (1) matching observed network trac with patterns of known attacks; (2) looking for deviation of trac behavior from the established prole; and (3) training a classier from labeled dataset of attacks to classify incoming trac. Once an attack is identied, the suspicious trac can be blocked or rate limited. In this presentation, we present a taxonomy of DoS attack detection and mitigation techniques, followed by a description of four representative systems (Snort, PHAD, MADAM, and MULTOPS). We conclude with a discussion of their pros/cons as well as challenges for future work

Addressing practical challenges for anomaly detection in backbone networks

Network monitoring has always been a topic of foremost importance for both network operators and researchers for multiple reasons ranging from anomaly detection to tra c classi cation or capacity planning. Nowadays, as networks become more and more complex, tra c increases and security threats reproduce, achieving a deeper understanding of what is happening in the network has become an essential necessity. In particular, due to the considerable growth of cybercrime, research on the eld of anomaly detection has drawn signi cant attention in recent years and tons of proposals have been made. All the same, when it comes to deploying solutions in real environments, some of them fail to meet some crucial requirements. Taking this into account, this thesis focuses on lling this gap between the research and the non-research world. Prior to the start of this work, we identify several problems. First, there is a clear lack of detailed and updated information on the most common anomalies and their characteristics. Second, unawareness of sampled data is still common although the performance of anomaly detection algorithms is severely a ected. Third, operators currently need to invest many work-hours to manually inspect and also classify detected anomalies to act accordingly and take the appropriate mitigation measures. This is further exacerbated due to the high number of false positives and false negatives and because anomaly detection systems are often perceived as extremely complex black boxes. Analysing an issue is essential to fully comprehend the problem space and to be able to tackle it properly. Accordingly, the rst block of this thesis seeks to obtain detailed and updated real-world information on the most frequent anomalies occurring in backbone networks. It rst reports on the performance of di erent commercial systems for anomaly detection and analyses the types of network nomalies detected. Afterwards, it focuses on further investigating the characteristics of the anomalies found in a backbone network using one of the tools for more than half a year. Among other results, this block con rms the need of applying sampling in an operational environment as well as the unacceptably high number of false positives and false negatives still reported by current commercial tools. On the whole, the presence of ampling in large networks for monitoring purposes has become almost mandatory and, therefore, all anomaly detection algorithms that do not take that into account might report incorrect results. In the second block of this thesis, the dramatic impact of sampling on the performance of well-known anomaly detection techniques is analysed and con rmed. However, we show that the results change signi cantly depending on the sampling technique used and also on the common metric selected to perform the comparison. In particular, we show that, Packet Sampling outperforms Flow Sampling unlike previously reported. Furthermore, we observe that Selective Sampling (SES), a sampling technique that focuses on small ows, obtains much better results than traditional sampling techniques for scan detection. Consequently, we propose Online Selective Sampling, a sampling technique that obtains the same good performance for scan detection than SES but works on a per-packet basis instead of keeping all ows in memory. We validate and evaluate our proposal and show that it can operate online and uses much less resources than SES. Although the literature is plenty of techniques for detecting anomalous events, research on anomaly classi cation and extraction (e.g., to further investigate what happened or to share evidence with third parties involved) is rather marginal. This makes it harder for network operators to analise reported anomalies because they depend solely on their experience to do the job. Furthermore, this task is an extremely time-consuming and error-prone process. The third block of this thesis targets this issue and brings it together with the knowledge acquired in the previous blocks. In particular, it presents a system for automatic anomaly detection, extraction and classi cation with high accuracy and very low false positives. We deploy the system in an operational environment and show its usefulness in practice. The fourth and last block of this thesis presents a generalisation of our system that focuses on analysing all the tra c, not only network anomalies. This new system seeks to further help network operators by summarising the most signi cant tra c patterns in their network. In particular, we generalise our system to deal with big network tra c data. In particular, it deals with src/dst IPs, src/dst ports, protocol, src/dst Autonomous Systems, layer 7 application and src/dst geolocation. We rst deploy a prototype in the European backbone network of G EANT and show that it can process large amounts of data quickly and build highly informative and compact reports that are very useful to help comprehending what is happening in the network. Second, we deploy it in a completely di erent scenario and show how it can also be successfully used in a real-world use case where we analyse the behaviour of highly distributed devices related with a critical infrastructure sector.La monitoritzaci o de xarxa sempre ha estat un tema de gran import ancia per operadors de xarxa i investigadors per m ultiples raons que van des de la detecci o d'anomalies fins a la classi caci o d'aplicacions. Avui en dia, a mesura que les xarxes es tornen m es i m es complexes, augmenta el tr ansit de dades i les amenaces de seguretat segueixen creixent, aconseguir una comprensi o m es profunda del que passa a la xarxa s'ha convertit en una necessitat essencial. Concretament, degut al considerable increment del ciberactivisme, la investigaci o en el camp de la detecci o d'anomalies ha crescut i en els darrers anys s'han fet moltes i diverses propostes. Tot i aix o, quan s'intenten desplegar aquestes solucions en entorns reals, algunes d'elles no compleixen alguns requisits fonamentals. Tenint aix o en compte, aquesta tesi se centra a omplir aquest buit entre la recerca i el m on real. Abans d'iniciar aquest treball es van identi car diversos problemes. En primer lloc, hi ha una clara manca d'informaci o detallada i actualitzada sobre les anomalies m es comuns i les seves caracter stiques. En segona inst ancia, no tenir en compte la possibilitat de treballar amb nom es part de les dades (mostreig de tr ansit) continua sent bastant est es tot i el sever efecte en el rendiment dels algorismes de detecci o d'anomalies. En tercer lloc, els operadors de xarxa actualment han d'invertir moltes hores de feina per classi car i inspeccionar manualment les anomalies detectades per actuar en conseqüencia i prendre les mesures apropiades de mitigaci o. Aquesta situaci o es veu agreujada per l'alt nombre de falsos positius i falsos negatius i perqu e els sistemes de detecci o d'anomalies s on sovint percebuts com caixes negres extremadament complexes. Analitzar un tema es essencial per comprendre plenament l'espai del problema i per poder-hi fer front de forma adequada. Per tant, el primer bloc d'aquesta tesi pret en proporcionar informaci o detallada i actualitzada del m on real sobre les anomalies m es freqüents en una xarxa troncal. Primer es comparen tres eines comercials per a la detecci o d'anomalies i se n'estudien els seus punts forts i febles, aix com els tipus d'anomalies de xarxa detectats. Posteriorment, s'investiguen les caracter stiques de les anomalies que es troben en la mateixa xarxa troncal utilitzant una de les eines durant m es de mig any. Entre d'altres resultats, aquest bloc con rma la necessitat de l'aplicaci o de mostreig de tr ansit en un entorn operacional, aix com el nombre inacceptablement elevat de falsos positius i falsos negatius en eines comercials actuals. En general, el mostreig de tr ansit de dades de xarxa ( es a dir, treballar nom es amb una part de les dades) en grans xarxes troncals s'ha convertit en gaireb e obligatori i, per tant, tots els algorismes de detecci o d'anomalies que no ho tenen en compte poden veure seriosament afectats els seus resultats. El segon bloc d'aquesta tesi analitza i confi rma el dram atic impacte de mostreig en el rendiment de t ecniques de detecci o d'anomalies plenament acceptades a l'estat de l'art. No obstant, es mostra que els resultats canvien signi cativament depenent de la t ecnica de mostreig utilitzada i tamb e en funci o de la m etrica usada per a fer la comparativa. Contr ariament als resultats reportats en estudis previs, es mostra que Packet Sampling supera Flow Sampling. A m es, a m es, s'observa que Selective Sampling (SES), una t ecnica de mostreig que se centra en mostrejar fluxes petits, obt e resultats molt millors per a la detecci o d'escanejos que no pas les t ecniques tradicionals de mostreig. En conseqü encia, proposem Online Selective Sampling, una t ecnica de mostreig que obt e el mateix bon rendiment per a la detecci o d'escanejos que SES, per o treballa paquet per paquet enlloc de mantenir tots els fluxes a mem oria. Despr es de validar i evaluar la nostra proposta, demostrem que es capa c de treballar online i utilitza molts menys recursos que SES. Tot i la gran quantitat de tècniques proposades a la literatura per a la detecci o d'esdeveniments an omals, la investigaci o per a la seva posterior classi caci o i extracci o (p.ex., per investigar m es a fons el que va passar o per compartir l'evid encia amb tercers involucrats) es m es aviat marginal. Aix o fa que sigui m es dif cil per als operadors de xarxa analalitzar les anomalies reportades, ja que depenen unicament de la seva experi encia per fer la feina. A m es a m es, aquesta tasca es un proc es extremadament lent i propens a errors. El tercer bloc d'aquesta tesi se centra en aquest tema tenint tamb e en compte els coneixements adquirits en els blocs anteriors. Concretament, presentem un sistema per a la detecci o extracci o i classi caci o autom atica d'anomalies amb una alta precisi o i molt pocs falsos positius. Adicionalment, despleguem el sistema en un entorn operatiu i demostrem la seva utilitat pr actica. El quart i ultim bloc d'aquesta tesi presenta una generalitzaci o del nostre sistema que se centra en l'an alisi de tot el tr ansit, no nom es en les anomalies. Aquest nou sistema pret en ajudar m es als operadors ja que resumeix els patrons de tr ansit m es importants de la seva xarxa. En particular, es generalitza el sistema per fer front al "big data" (una gran quantitat de dades). En particular, el sistema tracta IPs origen i dest i, ports origen i destí , protocol, Sistemes Aut onoms origen i dest , aplicaci o que ha generat el tr ansit i fi nalment, dades de geolocalitzaci o (tamb e per origen i dest ). Primer, despleguem un prototip a la xarxa europea per a la recerca i la investigaci o (G EANT) i demostrem que el sistema pot processar grans quantitats de dades r apidament aix com crear informes altament informatius i compactes que s on de gran utilitat per ajudar a comprendre el que est a succeint a la xarxa. En segon lloc, despleguem la nostra eina en un escenari completament diferent i mostrem com tamb e pot ser utilitzat amb exit en un cas d' us en el m on real en el qual s'analitza el comportament de dispositius altament distribuïts

Intrusion detection routers: Design, implementation and evaluation using an experimental testbed

In this paper, we present the design, the implementation details, and the evaluation results of an intrusion detection and defense system for distributed denial-of-service (DDoS) attack. The evaluation is conducted using an experimental testbed. The system, known as intrusion detection router (IDR), is deployed on network routers to perform online detection on any DDoS attack event, and then react with defense mechanisms to mitigate the attack. The testbed is built up by a cluster of sufficient number of Linux machines to mimic a portion of the Internet. Using the testbed, we conduct real experiments to evaluate the IDR system and demonstrate that IDR is effective in protecting the network from various DDoS attacks. © 2006 IEEE.published_or_final_versio

Optimization of BGP Convergence and Prefix Security in IP/MPLS Networks

Multi-Protocol Label Switching-based networks are the backbone of the operation of the Internet, that communicates through the use of the Border Gateway Protocol which connects distinct networks, referred to as Autonomous Systems, together. As the technology matures, so does the challenges caused by the extreme growth rate of the Internet. The amount of BGP prefixes required to facilitate such an increase in connectivity introduces multiple new critical issues, such as with the scalability and the security of the aforementioned Border Gateway Protocol. Illustration of an implementation of an IP/MPLS core transmission network is formed through the introduction of the four main pillars of an Autonomous System: Multi-Protocol Label Switching, Border Gateway Protocol, Open Shortest Path First and the Resource Reservation Protocol. The symbiosis of these technologies is used to introduce the practicalities of operating an IP/MPLS-based ISP network with traffic engineering and fault-resilience at heart. The first research objective of this thesis is to determine whether the deployment of a new BGP feature, which is referred to as BGP Prefix Independent Convergence (PIC), within AS16086 would be a worthwhile endeavour. This BGP extension aims to reduce the convergence delay of BGP Prefixes inside of an IP/MPLS Core Transmission Network, thus improving the networks resilience against faults. Simultaneously, the second research objective was to research the available mechanisms considering the protection of BGP Prefixes, such as with the implementation of the Resource Public Key Infrastructure and the Artemis BGP Monitor for proactive and reactive security of BGP prefixes within AS16086. The future prospective deployment of BGPsec is discussed to form an outlook to the future of IP/MPLS network design. As the trust-based nature of BGP as a protocol has become a distinct vulnerability, thus necessitating the use of various technologies to secure the communications between the Autonomous Systems that form the network to end all networks, the Internet

Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges