Abstract Interpretation of Stateful Networks

Modern networks achieve robustness and scalability by maintaining states on their nodes. These nodes are referred to as middleboxes and are essential for network functionality. However, the presence of middleboxes drastically complicates the task of network verification. Previous work showed that the problem is undecidable in general and EXPSPACE-complete when abstracting away the order of packet arrival. We describe a new algorithm for conservatively checking isolation properties of stateful networks. The asymptotic complexity of the algorithm is polynomial in the size of the network, albeit being exponential in the maximal number of queries of the local state that a middlebox can do, which is often small. Our algorithm is sound, i.e., it can never miss a violation of safety but may fail to verify some properties. The algorithm performs on-the fly abstract interpretation by (1) abstracting away the order of packet processing and the number of times each packet arrives, (2) abstracting away correlations between states of different middleboxes and channel contents, and (3) representing middlebox states by their effect on each packet separately, rather than taking into account the entire state space. We show that the abstractions do not lose precision when middleboxes may reset in any state. This is encouraging since many real middleboxes reset, e.g., after some session timeout is reached or due to hardware failure

Design and Verification of Specialised Security Goals for Protocol Families

Communication Protocols form a fundamental backbone of our modern information networks. These protocols provide a framework to describe how agents - Computers, Smartphones, RFID Tags and more - should structure their communication. As a result, the security of these protocols is implicitly trusted to protect our personal data. In 1997, Lowe presented ‘A Hierarchy of Authentication Specifications’, formalising a set of security requirements that might be expected of communication protocols. The value of these requirements is that they can be formally tested and verified against a protocol specification. This allows a user to have confidence that their communications are protected in ways that are uniformly defined and universally agreed upon. Since that time, the range of objectives and applications of real-world protocols has grown. Novel requirements - such as checking the physical distance between participants, or evolving trust assumptions of intermediate nodes on the network - mean that new attack vectors are found on a frequent basis. The challenge, then, is to define security goals which will guarantee security, even when the nature of these attacks is not known. In this thesis, a methodology for the design of security goals is created. It is used to define a collection of specialised security goals for protocols in multiple different families, by considering tailor-made models for these specific scenarios. For complex requirements, theorems are proved that simplify analysis, allowing the verification of security goals to be efficiently modelled in automated prover tools

Verification and Configuration of Software-based Networks

The innovative trends of Network Function Virtualization (NFV) and Software Defined Networking (SDN) have posed never experienced opportunities in productive environments, like data centers. While NFV decouples software implementation of the network functions (e.g., DPI and NAT) from their physical counterparts, SDN is in charge of dynamically changing those functions to create network paths. One new opportunity of such Software-based networks is to make the network service-provisioning models more flexible, by enabling users to build their own service graphs: users can select the Virtual Network Functions (VNFs) to use and can specify how packets have to be processed and forwarded in their networks. In particular, this PhD thesis spans mostly topics related to the verification and configuration of service graphs. For what concerns the challenges of network verification, our aim is to explore strategies that overcome the limitations of traditional techniques, which generally exploit complex modelling approaches and takes considerable verification times. Thus we envision for verification techniques that are based on non-complex modelling approaches in order to be much more efficient than existing proposals. Under these conditions, such novel approaches may work at run-time and, in particular, may be performed before deploying the service graphs, in order to avoid unexpected network behaviours and detect errors as early as possible. Another requirement is that verification should take a reasonable amount of time from a VNF Orchestrator point of view, with fair processing resources (e.g. CPU, memory and so on). This is because we are in the context of flexible services, where the reconfiguration of network functions can be frequently triggered, both in case of user request and in case of management events. The first contribution of this thesis lays on the service graphs specification by means of forwarding policies (i.e, a high-level specification of how packet flows are forwarded). While the majority of the SDN verification tools operate on OpenFlow configurations, we have defined a formal model to detect a set of anomalies in forwarding policies (i.e., erroneous specifications that may cause misleading network conditions and states). The key factors that distinguish our work from existing approaches are both an early detection of policies anomalies (i.e., before translating such policies into OpenFlow entries), in order to speed up the fixing phase, without even starting service deployment, and a scalable approach that achieves verification times in the order of milliseconds for medium- large- sized networks. Another advancement in network verification has been the possibility to verify networks including stateful VNFs, which are functions that may dynamically change the forwarding path of a traffic flow according to their local algorithms and states (e.g., IDSs). Our second contribution is thus a verification approach that models the network and the involved (possibly stateful) VNFs as a set of FOL formulas. Those formulas are passed to the off-the-shelf SMT (Satisfiability Modulo Theory) solver Z3 in order to verify some reachability-based properties. In particular, the proposed solution has been implemented in a tool released under the AGPLv3 license, named VeriGraph, which takes the functional configurations of all deployed VNFs (e.g., filtering rules on firewalls) into account to check the network. The adopted approach achieves verification times in the order of milliseconds, which is compliant with the timing limitations needed by a VNF Orchestrator. Finally, for what concerns the configuration of VNFs, service graph deployment should include a strategy to deploy VNF configurations in order to fix bugs in case of verification failures. Here, we have to face several challenges like the different ways a network function may require for being configured (REST API, CLI, etc...) and the configuration semantic that depends on the function itself (e.g., router parameters are clearly different from firewall ones). We conclude this thesis by proposing a model-based configuration approach, which means defining a representation of the main configuration parameters of a VNF. This VNF model is then automatically processed by further software modules in the VNF architecture to translate the configuration parameters into a particular format required by a VNF and to deliver the produced configuration into the VNF following one of the configuration strategies (e.g., REST, configuration file, etc.) already supported by the function. The achieved results of this last work, w.r.t. the current state of the art, are the exploitation of a model-driven approach that achieves a higher flexibility and the insertion of non-VNF-specific software modules to avoid changes in the VNF implementation

On the placement of security-related Virtualised Network Functions over data center networks

Middleboxes are typically hardware-accelerated appliances such as firewalls, proxies, WAN optimizers, and NATs that play an important role in service provisioning over today's data centers. Reports show that the number of middleboxes is on par with the number of routers, and consequently represent a significant commitment from an operator's capital and operational expenditure budgets. Over the past few years, software middleboxes known as Virtual Network Functions (VNFs) are replacing the hardware appliances to reduce cost, improve the flexibility of deployment, and allow for extending network functionality in short timescales. This dissertation aims at identifying the unique characteristics of security modules implementation as VNFs in virtualised environments. We focus on the placement of the security VNFs to minimise resource usage without violating the security imposed constraints as a challenge faced by operators today who want to increase the usable capacity of their infrastructures. The work presented here, focuses on the multi-tenant environment where customised security services are provided to tenants. The services are implemented as a software module deployed as a VNF collocated with network switches to reduce overhead. Furthermore, the thesis presents a formalisation for the resource-aware placement of security VNFs and provides a constraint programming solution along with examining heuristic, meta-heuristic and near-optimal/subset-sum solutions to solve larger size problems in reduced time. The results of this work identify the unique and vital constraints of the placement of security functions. They demonstrate that the granularity of the traffic required by the security functions imposes traffic constraints that increase the resource overhead of the deployment. The work identifies the north-south traffic in data centers as the traffic designed for processing for security functions rather than east-west traffic. It asserts that the non-sharing strategy of security modules will reduce the complexity in case of the multi-tenant environment. Furthermore, the work adopts on-path deployment of security VNF traffic strategy, which is shown to reduce resources overhead compared to previous approaches

Deep learning : enhancing the security of software-defined networks

Software-defined networking (SDN) is a communication paradigm that promotes network flexibility and programmability by separating the control plane from the data plane. SDN consolidates the logic of network devices into a single entity known as the controller. SDN raises significant security challenges related to its architecture and associated characteristics such as programmability and centralisation. Notably, security flaws pose a risk to controller integrity, confidentiality and availability. The SDN model introduces separation of the forwarding and control planes. It detaches the control logic from switching and routing devices, forming a central plane or network controller that facilitates communications between applications and devices. The architecture enhances network resilience, simplifies management procedures and supports network policy enforcement. However, it is vulnerable to new attack vectors that can target the controller. Current security solutions rely on traditional measures such as firewalls or intrusion detection systems (IDS). An IDS can use two different approaches: signature-based or anomaly-based detection. The signature-based approach is incapable of detecting zero-day attacks, while anomaly-based detection has high false-positive and false-negative alarm rates. Inaccuracies related to false-positive attacks may have significant consequences, specifically from threats that target the controller. Thus, improving the accuracy of the IDS will enhance controller security and, subsequently, SDN security. A centralised network entity that controls the entire network is a primary target for intruders. The controller is located at a central point between the applications and the data plane and has two interfaces for plane communications, known as northbound and southbound, respectively. Communications between the controller, the application and data planes are prone to various types of attacks, such as eavesdropping and tampering. The controller software is vulnerable to attacks such as buffer and stack overflow, which enable remote code execution that can result in attackers taking control of the entire network. Additionally, traditional network attacks are more destructive. This thesis introduces a threat detection approach aimed at improving the accuracy and efficiency of the IDS, which is essential for controller security. To evaluate the effectiveness of the proposed framework, an empirical study of SDN controller security was conducted to identify, formalise and quantify security concerns related to SDN architecture. The study explored the threats related to SDN architecture, specifically threats originating from the existence of the control plane. The framework comprises two stages, involving the use of deep learning (DL) algorithms and clustering algorithms, respectively. DL algorithms were used to reduce the dimensionality of inputs, which were forwarded to clustering algorithms in the second stage. Features were compressed to a single value, simplifying and improving the performance of the clustering algorithm. Rather than using the output of the neural network, the framework presented a unique technique for dimensionality reduction that used a single value—reconstruction error—for the entire input record. The use of a DL algorithm in the pre-training stage contributed to solving the problem of dimensionality related to k-means clustering. Using unsupervised algorithms facilitated the discovery of new attacks. Further, this study compares generative energy-based models (restricted Boltzmann machines) with non-probabilistic models (autoencoders). The study implements TensorFlow in four scenarios. Simulation results were statistically analysed using a confusion matrix, which was evaluated and compared with similar related works. The proposed framework, which was adapted from existing similar approaches, resulted in promising outcomes and may provide a robust prospect for deployment in modern threat detection systems in SDN. The framework was implemented using TensorFlow and was benchmarked to the KDD99 dataset. Simulation results showed that the use of the DL algorithm to reduce dimensionality significantly improved detection accuracy and reduced false-positive and false-negative alarm rates. Extensive simulation studies on benchmark tasks demonstrated that the proposed framework consistently outperforms all competing approaches. This improvement is a further step towards the development of a reliable IDS to enhance the security of SDN controllers

Dynamic service chain composition in virtualised environment

Network Function Virtualisation (NFV) has contributed to improving the flexibility of network service provisioning and reducing the time to market of new services. NFV leverages the virtualisation technology to decouple the software implementation of network appliances from the physical devices on which they run. However, with the emergence of this paradigm, providing data centre applications with an adequate network performance becomes challenging. For instance, virtualised environments cause network congestion, decrease the throughput and hurt the end user experience. Moreover, applications usually communicate through multiple sequences of virtual network functions (VNFs), aka service chains, for policy enforcement and performance and security enhancement, which increases the management complexity at to the network level. To address this problematic situation, existing studies have proposed high-level approaches of VNFs chaining and placement that improve service chain performance. They consider the VNFs as homogenous entities regardless of their specific characteristics. They have overlooked their distinct behaviour toward the traffic load and how their underpinning implementation can intervene in defining resource usage. Our research aims at filling this gap by finding out particular patterns on production and widely used VNFs. And proposing a categorisation that helps in reducing network latency at the chains. Based on experimental evaluation, we have classified firewalls, NAT, IDS/IPS, Flow monitors into I/O- and CPU-bound functions. The former category is mainly sensitive to the throughput, in packets per second, while the performance of the latter is primarily affected by the network bandwidth, in bits per second. By doing so, we correlate the VNF category with the traversing traffic characteristics and this will dictate how the service chains would be composed. We propose a heuristic called Natif, for a VNF-Aware VNF insTantIation and traFfic distribution scheme, to reconcile the discrepancy in VNF requirements based on the category they belong to and to eventually reduce network latency. We have deployed Natif in an OpenStack-based environment and have compared it to a network-aware VNF composition approach. Our results show a decrease in latency by around 188% on average without sacrificing the throughput