No Arabic abstract
Networks are vulnerable to disruptions caused by malicious forwarding devices. The situation is likely to worsen in Software Defined Networks (SDNs) with the incompatibility of existing solutions, use of programmable soft switches and the potential of bringing down an entire network through compromised forwarding devices. In this paper, we present WedgeTail, an Intrusion Prevention System (IPS) designed to secure the SDN data plane. WedgeTail regards forwarding devices as points within a geometric space and stores the path packets take when traversing the network as trajectories. To be efficient, it prioritizes forwarding devices before inspection using an unsupervised trajectory-based sampling mechanism. For each of the forwarding device, WedgeTail computes the expected and actual trajectories of packets and `hunts for any forwarding device not processing packets as expected. Compared to related work, WedgeTail is also capable of distinguishing between malicious actions such as packet drop and generation. Moreover, WedgeTail employs a radically different methodology that enables detecting threats autonomously. In fact, it has no reliance on pre-defined rules by an administrator and may be easily imported to protect SDN networks with different setups, forwarding devices, and controllers. We have evaluated WedgeTail in simulated environments, and it has been capable of detecting and responding to all implanted malicious forwarding devices within a reasonable time-frame. We report on the design, implementation, and evaluation of WedgeTail in this manuscript.
Software-Defined Network (SDN) radically changes the network architecture by decoupling the network logic from the underlying forwarding devices. This architectural change rejuvenates the network-layer granting centralized management and re-programmability of the networks. From a security perspective, SDN separates security concerns into control and data plane, and this architectural recomposition brings up exciting opportunities and challenges. The overall perception is that SDN capabilities will ultimately result in improved security. However, in its raw form, SDN could potentially make networks more vulnerable to attacks and harder to protect. In this paper, we focus on identifying challenges faced in securing the data plane of SDN - one of the least explored but most critical components of this technology. We formalize this problem space, identify potential attack scenarios while highlighting possible vulnerabilities and establish a set of requirements and challenges to protect the data plane of SDNs. Moreover, we undertake a survey of existing solutions with respect to the identified threats, identifying their limitations and offer future research directions.
As networks expand in size and complexity, they pose greater administrative and management challenges. Software Defined Networks (SDN) offer a promising approach to meeting some of these challenges. In this paper, we propose a policy driven security architecture for securing end to end services across multiple SDN domains. We develop a language based approach to design security policies that are relevant for securing SDN services and communications. We describe the policy language and its use in specifying security policies to control the flow of information in a multi-domain SDN. We demonstrate the specification of fine grained security policies based on a variety of attributes such as parameters associated with users and devices/switches, context information such as location and routing information, and services accessed in SDN as well as security attributes associated with the switches and Controllers in different domains. An important feature of our architecture is its ability to specify path and flow based security policies, which are significant for securing end to end services in SDNs. We describe the design and the implementation of our proposed policy based security architecture and demonstrate its use in scenarios involving both intra and inter-domain communications with multiple SDN Controllers. We analyse the performance characteristics of our architecture as well as discuss how our architecture is able to counteract various security attacks. The dynamic security policy based approach and the distribution of corresponding security capabilities intelligently as a service layer that enable flow based security enforcement and protection of multitude of network devices against attacks are important contributions of this paper.
We propose PAIO, the first general-purpose framework that enables system designers to build custom-made Software-Defined Storage (SDS) data plane stages. It provides the means to implement storage optimizations adaptable to different workflows and user-defined policies, and allows straightforward integration with existing applications and I/O layers. PAIO allows stages to be integrated with modern SDS control planes to ensure holistic control and system-wide optimal performance. We demonstrate the performance and applicability of PAIO with two use cases. The first improves 99th percentile latency by 4x in industry-standard LSM-based key-value stores. The second ensures dynamic per-application bandwidth guarantees under shared storage environments.
The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.
Software Defined Networking (SDN) promises greater flexibility for directing packet flows, and Network Function Virtualization promises to enable dynamic management of software-based network functions. However, the current divide between an intelligent control plane and an overly simple, stateless data plane results in the inability to exploit the flexibility of a software based network. In this paper we propose SDNFV, a framework that expands the capabilities of network processing-and-forwarding elements to flexibly manage packet flows, while retaining both a high performance data plane and an easily managed control plane. SDNFV proposes a hierarchical control framework where decisions are made across the SDN controller, a host-level manager, and individual VMs to best exploit state available at each level. This increases the networks flexibility compared to existing SDNs where controllers often make decisions solely based on the first packet header of a flow. SDNFV intelligently places network services across hosts and connects them in sequential and parallel chains, giving both the SDN controller and individual network functions the ability to enhance and update flow rules to adapt to changing conditions. Our prototype demonstrates how to efficiently and flexibly reroute flows based on data plane state such as packet payloads and traffic characteristics.