A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner

Security analysis of network neighbors

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010O presente trabalho aborda um problema comum a muitos dos actuais fornecedores de serviços Internet (ISPs): mitigação eficiente de tráfego malicioso na sua rede. Este tráfego indesejado impõe um desperdício de recursos de rede o que leva a uma consequente degradação da qualidade de serviço. Cria também um ambiente inseguro para os clientes, minando o potencial oferecido pela Internet e abrindo caminho para actividades criminosas graves. Algumas das principais condicionantes na criação de sistemas capazes de resolver estes problemas são: a enorme quantidade de tráfego a ser analisado, o facto da Internet ser inerentemente anónima e a falta de incentivo para os operadores de redes de trânsito em bloquear este tipo de tráfego. No âmbito de um ISP de média escala, este trabalho concentra-se em três áreas principais: origens de tráfego malicioso, classificação de segurança de redes vizinhas ao ISP e políticas de intervenção. Foram colectados dados de rede considerando, determinados tipos de tráfego malicioso: varrimento de endereços e inundação de fluxos de ligações; assim como informação de acessibilidades rede: mensagens de actualização de BGP disponibilizadas pelo RIPE Routing Information Service. Analisámos o tráfego malicioso em busca de padrões de rede, o que nos permitiu compreender que é maioritariamente originário de um subconjunto muito pequeno de ASes na Internet. No âmbito de um ISP e de acordo com um conjunto de métricas de segurança, definimos uma expressão de correlação para quantificar os riscos de segurança associados a conexões com redes vizinhas, a qual denominámos Risk Score. Finalmente, propusemos técnicas para concretização das tarefas de rede necessárias à redução de tráfego malicioso de forma eficiente, se possível em cooperação com redes vizinhas / ASes. Não temos conhecimento de qualquer publicação existente que correlacione as características de tráfego malicioso de varrimento de endereços e inundação de fluxos de ligações, com informação de acessibilidades de rede no âmbito de um ISP, de forma a classificar a segurança das vizinhanças de rede, com o propósito de decidir filtrar o tráfego de prefixos específicos de um AS ou bloquear todo o tráfego proveniente de um AS. Acreditamos que os resultados apresentados neste trabalho podem ser aplicados imediatamente em cenários reais, permitindo criar ambientes de rede mais seguros e escaláveis, desta forma melhorando as condições de rede necessárias ao desenvolvimento de novos serviços.This thesis addresses a common issue to many of current Internet Service Providers (ISPs): efficient mitigation of malicious traffic flowing through their network. This unwanted traffic imposes a waste of network resources, leading to a degradation of quality of service. It also creates an unsafe environment for users, therefore mining the Internet potential and opening way for severe criminal activity. Some of the main constraints of creating systems that may tackle these problems are the enormous amount of traffic to be analyzed, the fact that the Internet is inherently untraceable and the lack of incentive for transit networks to block this type of traffic. Under the scope of a mid scale ISP, this thesis focuses on three main areas: the origins of malicious traffic, security classification of ISP neighbors and intervention policies. We collected network data from particular types of malicious traffic: address scans and flow floods; and network reachability information: BGP update messages from RIPE Routing Information Service (RIS). We analyzed the malicious traffic looking for network patterns, which allowed us to understand that most of it originates from a very small subset of Internet ASes. We defined a correlation expression to quantify the security risks of neighbor connections within an ISP scope according to a set of security metrics that we named Risk Score. We finally proposed techniques to implement the network tasks required to mitigate malicious traffic efficiently, if possible in cooperation with other neighbors/ASes. We are not aware of any work been done that correlates the malicious traffic characteristics of address scans and flow flood attacks, with network reachability information of an ISP network, to classify the security of neighbor connections in order to decide to filter traffic from specific prefixes of an AS, or to block all traffic from an AS. It is our belief, the findings presented in this thesis can be immediately applied to real world scenarios, enabling more secure and scalable network environments, therefore opening way for better deployment environments of new services

Stateful Anycast for DDoS Mitigation

MEng thesisDistributed denial-of-service (DDoS) attacks can easily cripple victim hosts or networks, yet effective defenses remain elusive. Normal anycast can be used to force the diffusion of attack traffic over a group of several hosts to increase the difficulty of saturating resources at or near any one of the hosts. However, because a packet sent to the anycast group may be delivered to any member, anycast does not support protocols that require a group member to maintain state (such as TCP). This makes anycast impractical for most applications of interest.This document describes the design of Stateful Anycast, a conceptual anycast-like network service based on IP anycast. Stateful Anycast is designed to support stateful sessions without losing anycasts ability to defend against DDoS attacks. Stateful Anycast employs a set of anycasted proxies to direct packets to the proper stateholder. These proxies provide DDoS protection by dropping a sessions packets upon group member request. Stateful Anycast is incrementally deployable and can scale to support many groups

Modeling, analysis and defense strategies against Internet attacks.

Third, we have analyzed the tradeoff between delay caused by filtering of worms at routers, and the delay due to worms' excessive amount of network traffic. We have used the optimal control problem, to determine the appropriate tradeoffs between these two delays for a given rate of a worm spreading. Using our technique we can minimize the overall network delay by finding the number of routers that should perform filtering and the time at which they should start the filtering process.Many early Internet protocols were designed without a fundamentally secure infrastructure and hence vulnerable to attacks such as denial of service (DoS) attacks and worms. DoS attacks attempt to consume the resources of a remote host or network, thereby denying or degrading service to legitimate users. Network forensics is an emerging area wherein the source or the cause of the attacker is determined using IDS tools. The problem of finding the source(s) of attack(s) is called the "trace back problem". Lately, Internet worms have become a major problem for the security of computer networks, causing considerable amount of resources and time to be spent recovering from the disruption of systems. In addition to breaking down victims, these worms create large amounts of unnecessary network data traffic that results in network congestion, thereby affecting the entire network.In this dissertation, first we solve the trace back problem more efficiently in terms of the number of routers needed to complete the track back. We provide an efficient algorithm to decompose a network into connected components and construct a terminal network. We show that for a terminal network with n routers, the trace back can be completed in O(log n) steps.Second, we apply two classical epidemic SIS and SIR models to study the spread of Internet Worm. The analytical models that we provide are useful in determining the rate of spread and time required to infect a majority of the nodes in the network. Our simulation results on large Internet like topologies show that in a fairly small amount of time, 80% of the network nodes is infected

Deteção de ataques de negação de serviços distribuídos na origem

From year to year new records of the amount of traffic in an attack are established, which demonstrate not only the constant presence of distributed denialof-service attacks, but also its evolution, demarcating itself from the other network threats. The increasing importance of resource availability alongside the security debate on network devices and infrastructures is continuous, given the preponderant role in both the home and corporate domains. In the face of the constant threat, the latest network security systems have been applying pattern recognition techniques to infer, detect, and react more quickly and assertively. This dissertation proposes methodologies to infer network activities patterns, based on their traffic: follows a behavior previously defined as normal, or if there are deviations that raise suspicions about the normality of the action in the network. It seems that the future of network defense systems continues in this direction, not only by increasing amount of traffic, but also by the diversity of actions, services and entities that reflect different patterns, thus contributing to the detection of anomalous activities on the network. The methodologies propose the collection of metadata, up to the transport layer of the osi model, which will then be processed by the machien learning algorithms in order to classify the underlying action. Intending to contribute beyond denial-of-service attacks and the network domain, the methodologies were described in a generic way, in order to be applied in other scenarios of greater or less complexity. The third chapter presents a proof of concept with attack vectors that marked the history and a few evaluation metrics that allows to compare the different classifiers as to their success rate, given the various activities in the network and inherent dynamics. The various tests show flexibility, speed and accuracy of the various classification algorithms, setting the bar between 90 and 99 percent.De ano para ano são estabelecidos novos recordes de quantidade de tráfego num ataque, que demonstram não só a presença constante de ataques de negação de serviço distribuídos, como também a sua evolução, demarcando-se das outras ameaças de rede. A crescente importância da disponibilidade de recursos a par do debate sobre a segurança nos dispositivos e infraestruturas de rede é contínuo, dado o papel preponderante tanto no dominio doméstico como no corporativo. Face à constante ameaça, os sistemas de segurança de rede mais recentes têm vindo a aplicar técnicas de reconhecimento de padrões para inferir, detetar e reagir de forma mais rápida e assertiva. Esta dissertação propõe metodologias para inferir padrões de atividades na rede, tendo por base o seu tráfego: se segue um comportamento previamente definido como normal, ou se existem desvios que levantam suspeitas sobre normalidade da ação na rede. Tudo indica que o futuro dos sistemas de defesa de rede continuará neste sentido, servindo-se não só do crescente aumento da quantidade de tráfego, como também da diversidade de ações, serviços e entidades que refletem padrões distintos contribuindo assim para a deteção de atividades anómalas na rede. As metodologias propõem a recolha de metadados, até á camada de transporte, que seguidamente serão processados pelos algoritmos de aprendizagem automática com o objectivo de classificar a ação subjacente. Pretendendo que o contributo fosse além dos ataques de negação de serviço e do dominio de rede, as metodologias foram descritas de forma tendencialmente genérica, de forma a serem aplicadas noutros cenários de maior ou menos complexidade. No quarto capítulo é apresentada uma prova de conceito com vetores de ataques que marcaram a história e, algumas métricas de avaliação que permitem comparar os diferentes classificadores quanto à sua taxa de sucesso, face às várias atividades na rede e inerentes dinâmicas. Os vários testes mostram flexibilidade, rapidez e precisão dos vários algoritmos de classificação, estabelecendo a fasquia entre os 90 e os 99 por cento.Mestrado em Engenharia de Computadores e Telemátic

Enhanching Security in the Future Cyber Physical Systems

Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adopt Kalman Filter (KF) and χ2 detector. The χ2 -detector can detect myriad of system fault- s/attacks such as Denial of Service (DoS) attack, short term and long term random attacks. However, the study shows that the χ2 -detector is unable to detect the intelligent False Data Injection attack (FDI). To overcome this limitation, I present a Euclidean detector for smart grid which can effectively detect such injection attacks. Along with detecting attack/faults I also present the isolation of the attacked/faulty nodes for smart grid. For isolation the Gen- eralized Observer Scheme (GOS) implementing Kalman Filter is used. As GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. To address this issue, an Iterative Observer Scheme (IOS) is presented which is able to detect attack on multiple sensors. Since network is an integral part of the future CPSs, I also present a scheme for pre- serving privacy in the future Internet architecture, namely MobilityFirst architecture. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered ap- proach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users

ROVER: a DNS-based method to detect and prevent IP hijacks

2013 Fall.Includes bibliographical references.The Border Gateway Protocol (BGP) is critical to the global internet infrastructure. Unfortunately BGP routing was designed with limited regard for security. As a result, IP route hijacking has been observed for more than 16 years. Well known incidents include a 2008 hijack of YouTube, loss of connectivity for Australia in February 2012, and an event that partially crippled Google in November 2012. Concern has been escalating as critical national infrastructure is reliant on a secure foundation for the Internet. Disruptions to military, banking, utilities, industry, and commerce can be catastrophic. In this dissertation we propose ROVER (Route Origin VERification System), a novel and practical solution for detecting and preventing origin and sub-prefix hijacks. ROVER exploits the reverse DNS for storing route origin data and provides a fail-safe, best effort approach to authentication. This approach can be used with a variety of operational models including fully dynamic in-line BGP filtering, periodically updated authenticated route filters, and real-time notifications for network operators. Our thesis is that ROVER systems can be deployed by a small number of institutions in an incremental fashion and still effectively thwart origin and sub-prefix IP hijacking despite non-participation by the majority of Autonomous System owners. We then present research results supporting this statement. We evaluate the effectiveness of ROVER using simulations on an Internet scale topology as well as with tests on real operational systems. Analyses include a study of IP hijack propagation patterns, effectiveness of various deployment models, critical mass requirements, and an examination of ROVER resilience and scalability

Towards IP traceback based defense against DDoS attacks.

Lau Nga Sin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 101-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Research Motivation --- p.2Chapter 1.2 --- Problem Statement --- p.3Chapter 1.3 --- Research Objectives --- p.4Chapter 1.4 --- Structure of the Thesis --- p.6Chapter 2 --- Background Study on DDoS Attacks --- p.8Chapter 2.1 --- Distributed Denial of Service Attacks --- p.8Chapter 2.1.1 --- DDoS Attack Architecture --- p.9Chapter 2.1.2 --- DDoS Attack Taxonomy --- p.11Chapter 2.1.3 --- DDoS Tools --- p.19Chapter 2.1.4 --- DDoS Detection --- p.21Chapter 2.2 --- DDoS Countermeasure: Attack Source Traceback --- p.23Chapter 2.2.1 --- Link Testing --- p.23Chapter 2.2.2 --- Logging --- p.24Chapter 2.2.3 --- ICMP-based traceback --- p.26Chapter 2.2.4 --- Packet marking --- p.28Chapter 2.2.5 --- Comparison of various IP Traceback Schemes --- p.31Chapter 2.3 --- DDoS Countermeasure: Packet Filtering --- p.33Chapter 2.3.1 --- Ingress Filtering --- p.33Chapter 2.3.2 --- Egress Filtering --- p.34Chapter 2.3.3 --- Route-based Packet Filtering --- p.35Chapter 2.3.4 --- IP Traceback-based Packet Filtering --- p.36Chapter 2.3.5 --- Router-based Pushback --- p.37Chapter 3 --- Domain-based IP Traceback Scheme --- p.40Chapter 3.1 --- Overview of our IP Traceback Scheme --- p.41Chapter 3.2 --- Assumptions --- p.44Chapter 3.3 --- Proposed Packet Marking Scheme --- p.45Chapter 3.3.1 --- IP Markings with Edge Sampling --- p.46Chapter 3.3.2 --- Domain-based Design Motivation --- p.48Chapter 3.3.3 --- Mathematical Principle --- p.49Chapter 3.3.4 --- Marking Mechanism --- p.51Chapter 3.3.5 --- Storage Space of the Marking Fields --- p.56Chapter 3.3.6 --- Packet Marking Integrity --- p.57Chapter 3.3.7 --- Path Reconstruction --- p.58Chapter 4 --- Route-based Packet Filtering Scheme --- p.62Chapter 4.1 --- Placement of Filters --- p.63Chapter 4.1.1 --- At Sources' Networks --- p.64Chapter 4.1.2 --- At Victim's Network --- p.64Chapter 4.2 --- Proposed Packet Filtering Scheme --- p.65Chapter 4.2.1 --- Classification of Packets --- p.66Chapter 4.2.2 --- Filtering Mechanism --- p.67Chapter 5 --- Performance Evaluation --- p.70Chapter 5.1 --- Simulation Setup --- p.70Chapter 5.2 --- Experiments on IP Traceback Scheme --- p.72Chapter 5.2.1 --- Performance Metrics --- p.72Chapter 5.2.2 --- Choice of Marking Probabilities --- p.73Chapter 5.2.3 --- Experimental Results --- p.75Chapter 5.3 --- Experiments on Packet Filtering Scheme --- p.82Chapter 5.3.1 --- Performance Metrics --- p.82Chapter 5.3.2 --- Choices of Filtering Probabilities --- p.84Chapter 5.3.3 --- Experimental Results --- p.85Chapter 5.4 --- Deployment Issues --- p.91Chapter 5.4.1 --- Backward Compatibility --- p.91Chapter 5.4.2 --- Processing Overheads to the Routers and Network --- p.93Chapter 5.5 --- Evaluations --- p.95Chapter 6 --- Conclusion --- p.96Chapter 6.1 --- Contributions --- p.96Chapter 6.2 --- Discussions and future work --- p.99Bibliography --- p.11

A Machine Learning Approach For Enhancing Security And Quality Of Service Of Optical Burst Switching Networks

The Optical Bust Switching (OBS) network has become one of the most promising switching technologies for building the next-generation of internet backbone infrastructure. However, OBS networks still face a number of security and Quality of Service (QoS) challenges, particularly from Burst Header Packet (BHP) flooding attacks. In OBS, a core switch handles requests, reserving one of the unoccupied channels for incoming data bursts (DB) through BHP. An attacker can exploit this fact and send malicious BHP without the corresponding DB. If unresolved, threats such as BHP flooding attacks can result in low bandwidth utilization, limited network performance, high burst loss rate, and eventually, denial of service (DoS). In this dissertation, we focus our investigations on the network security and QoS in the presence of BHP flooding attacks. First, we proposed and developed a new security model that can be embedded into OBS core switch architecture to prevent BHP flooding attacks. The countermeasure security model allows the OBS core switch to classify the ingress nodes based on their behavior and the amount of reserved resources not being utilized. A malicious node causing a BHP flooding attack will be blocked by the developed model until the risk disappears or the malicious node redeems itself. Using our security model, we can effectively and preemptively prevent a BHP flooding attack regardless of the strength of the attacker. In the second part of this dissertation, we investigated the potential use of machine learning (ML) in countering the risk of the BHP flood attack problem. In particular, we proposed and developed a new series of rules, using the decision tree method to prevent the risk of a BHP flooding attack. The proposed classification rule models were evaluated using different metrics to measure the overall performance of this approach. The experiments showed that using rules derived from the decision trees did indeed counter BHP flooding attacks, and enabled the automatic classification of edge nodes at an early stage. In the third part of this dissertation, we performed a comparative study, evaluating a number of ML techniques in classifying edge nodes, to determine the most suitable ML method to prevent this type of attack. The experimental results from a preprocessed dataset related to BHP flooding attacks showed that rule-based classifiers, in particular decision trees (C4.5), Bagging, and RIDOR, consistently derive classifiers that are more predictive, compared to alternate ML algorithms, including AdaBoost, Logistic Regression, Naïve Bayes, SVM-SMO and ANN-MultilayerPerceptron. Moreover, the harmonic mean, recall and precision results of the rule-based and tree classifiers were more competitive than those of the remaining ML algorithms. Lastly, the runtime results in ms showed that decision tree classifiers are not only more predictive, but are also more efficient than other algorithms. Thus, our findings show that decision tree identifier is the most appropriate technique for classifying ingress nodes to combat the BHP flooding attack problem