Biblio

In this paper, a dynamic cybersecurity protection method based on software-defined networking (SDN) is proposed, according to the protection requirement analysis for industrial control systems (ICSs). This method can execute security response measures by SDN, such as isolation, redirection etc., based on the real-time intrusion detection results, forming a detecting-responding closed-loop security control. In addition, moving target defense (MTD) concept is introduced to the protection for ICSs, where topology transformation and IP/port hopping are realized by SDN, which can confuse and deceive the attackers and prevent attacks at the beginning, protection ICSs in an active manner. The simulation results verify the feasibility of the proposed method.

The Internet of Things (IoT) is growing rapidly controlling and connecting thousands of devices every day. The increased number of interconnected devices increase the network traffic leading to energy and Quality of Service efficiency problems of the IoT network. Therefore, IoT platforms and networks are susceptible to failures and attacks that have significant economic and security consequences. In this regard, implementing effective secure IoT platforms and networks are valuable for both the industry and society. In this paper, we propose two frameworks that aim to verify a number of security policies related to runtime information of the network and dynamic flow routing paths, respectively. The underlying rationale is to allow the operator of an IoT network in order to have an overall control of the network and to define different policies based on the demands of the network and the use cases (e.g., achieving more secure or faster network).

Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it is challenging to choose a proper degree of traffic flow handling granularity while proactively protecting forwarding devices from getting overloaded. In this paper, we propose a novel traffic flow matching control framework called Q-DATA that applies reinforcement learning in order to enhance the traffic flow monitoring performance in SDN based networks and prevent traffic forwarding performance degradation. We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity. It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm. Next, we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require. Our novel approach is realized as a REST SDN application and evaluated in an SDN environment. Through comprehensive experiments, the results show that-compared to the default behavior of common SDN controllers and to our previous DATA mechanism-the new Q-DATA framework yields a remarkable improvement in terms of traffic forwarding performance degradation protection of SDN switches while still providing the most detailed traffic flow information on demand.

Despite the wide of range of research and technologies that deal with the problem of routing in computer networks, there remains a gap between the level of network hardware administration and the level of business requirements and constraints. Not much has been accomplished in literature in order to have a direct enforcement of such requirements on the network. This paper presents a new solution in specifying and directly enforcing security policies to control the routing configuration in a software-defined network by using Row-Level Security checks which enable fine-grained security policies on individual rows in database tables. We show, as a first step, how a specific class of such policies, namely multilevel security policies, can be enforced on a database-defined network, which presents an abstraction of a network's configuration as a set of database tables. We show that such policies can be used to control the flow of data in the network either in an upward or downward manner.

Quantum computing is posing new threats on our security infrastructure. This has triggered a new research field on quantum-safe methods, and those that rely on the application of quantum principles are commonly referred as quantum cryptography. The most mature development in the field of quantum cryptography is called Quantum Key Distribution (QKD). QKD is a key exchange primitive that can replace existing mechanisms that can become obsolete in the near future. Although QKD has reached a high level of maturity, there is still a long path for a mass market implementation. QKD shall overcome issues such as miniaturization, network integration and the reduction of production costs to make the technology affordable. In this direction, we foresee that QKD systems will evolve following the same path as other networking technologies, where systems will run on specific network cards, integrable in commodity chassis. This work describes part of our activity in the EU H2020 project CiViQ in which quantum technologies, as QKD systems or quantum random number generators (QRNG), will become a single network element that we define as Quantum Switch. This allows for quantum resources (keys or random numbers) to be provided as a service, while the different components are integrated to cooperate for providing the most random and secure bit streams. Furthermore, with the purpose of making our proposal closer to current networking technology, this work also proposes an abstraction logic for making our Quantum Switch suitable to become part of software-defined networking (SDN) architectures. The model fits in the architecture of the SDN quantum node architecture, that is being under standardization by the European Telecommunications Standards Institute. It permits to operate an entire quantum network using a logically centralized SDN controller, and quantum switches to generate and to forward key material and random numbers across the entire network. This scheme, demonstrated for the first time at the Madrid Quantum Network, will allow for a faster and seamless integration of quantum technologies in the telecommunications infrastructure.

Many modern business models of the manufacturing industry use the possibilities of digitization. In particular, the idea of connecting machines to networks and communication infrastructure is gaining momentum. However, in addition to the considerable economic advantages, this development also brings decisive disadvantages. By connecting previously encapsulated industrial networks with untrustworthy external networks such as the Internet, machines and systems are suddenly exposed to the same threats as conventional IT systems. A key problem today is the typical network paradigm with static routers and switches that cannot meet the dynamic requirements of a modern industrial network. Current security solutions often only threat symptoms instead of tackling the cause. In this paper we will therefore analyze the weaknesses of current networks and security solutions using the example of industrial remote maintenance. We will then present a novel concept of how Software-Defined Networking (SDN) in combination with a policy framework that supports attribute-based access control can be used to meet current and future security requirements in dynamic industrial networks. Furthermore, we will introduce an examplary implementation of this novel security framework for the use case of industrial remote maintenance and evaluate the solution. Our results show that SDN in combination with an Attribute-based Access Control (ABAC) policy framework is perfectly suited to increase flexibility and security of modern industrial networks at the same time.

Software Defined Networking (SDN) has introduced both innovative opportunities and additional risks in the computer networking. Among disadvantages of SDNs one can mention their susceptibility to vulnerabilities associated with both virtualization and the traditional networking. Selecting a proper controller for an organization may not be a trivial task as there is a variety of SDN controllers on the market and each of them may come with its own pros and cons from the security point of view. This research proposes a comprehensive methodology for organizations to evaluate security-related features available in SDN controllers. The methodology can serve as a guideline in the decisions related to SDN choice. The proposed security assessment follows a structured approach to evaluate each layer of the SDN architecture and each metrics defined in presented research has been matched with the security controls defined in NIST 800-53. Through the tests on actual controllers the paper provides an example on how the proposed methodology can be used to evaluate existing SDN solutions.

Compared to ordinary Internet applications, the transfer of scientific data flows often has higher requirements for network performance. The network security devices and systems often affect the efficiency of scientific data transfer. As a new type of network architecture, Software-defined Networking (SDN) decouples the data plane from the control plane. Its programmability allows users to customize the network transfer path and makes the network more intelligent. The Science DMZ model is a private network for scientific data flow transfer, which can improve performance under the premise of ensuring network security. This paper combines SDN with Science DMZ, designs and implements an SDN-based traffic scheduling algorithm considering the load of link. In addition to distinguishing scientific data flow from common data flow, the algorithm further distinguishes the scientific data flows of different applications and performs different traffic scheduling of scientific data for specific link states. Experiments results proved that the algorithm can effectively improve the transmission performance of scientific data flow.

Present networks within safety-critical systems rely on complex and inflexible network configurations. New technologies such as software-defined networking are more dynamic and offer more flexibility, but due care needs to be exercised to ensure that safety and security are not compromised by incorrect configurations. To this end, this paper proposes the use of pre-generated and optimized configuration templates. These provide alternate routes for traffic considering availability, resilience and timing constraints where network components fail due to attacks or faults.To obtain these templates, two heuristics based on Dijkstra's algorithm and an optimization algorithm providing the maximum resilience were investigated. While the configurations obtained through optimization yield appropriate templates, the heuristics investigated are not suitable to obtain configuration templates, since they cannot fulfill all requirements.

Large-scale failures in communication networks due to natural disasters or malicious attacks can severely affect critical communications and threaten lives of people in the affected area. In the absence of a proper communication infrastructure, rescue operation becomes extremely difficult. Progressive and timely network recovery is, therefore, a key to minimizing losses and facilitating rescue missions. To this end, we focus on network recovery assuming partial and uncertain knowledge of the failure locations. We proposed a progressive multi-stage recovery approach that uses the incomplete knowledge of failure to find a feasible recovery schedule. Next, we focused on failure recovery of multiple interconnected networks. In particular, we focused on the interaction between a power grid and a communication network. Then, we focused on network monitoring techniques that can be used for diagnosing the performance of individual links for localizing soft failures (e.g. highly congested links) in a communication network. We studied the optimal selection of the monitoring paths to balance identifiability and probing cost. Finally, we addressed, a minimum disruptive routing framework in software defined networks. Extensive experimental and simulation results show that our proposed recovery approaches have a lower disruption cost compared to the state-of-the-art while we can configure our choice of trade-off between the identifiability, execution time, the repair/probing cost, congestion and the demand loss.

The growing trend toward information technology increases the amount of data travelling over the network links. The problem of detecting anomalies in data streams has increased with the growth of internet connectivity. Software-Defined Networking (SDN) is a new concept of computer networking that can adapt and support these growing trends. However, the centralized nature of the SDN design is challenged by the need for an efficient method for traffic monitoring against traffic anomalies caused by misconfigured devices or ongoing attacks. In this paper, we propose a new model for traffic behavior monitoring that aims to ensure trusted communication links between the network devices. The main objective of this model is to confirm that the behavior of the traffic streams matches the instructions provided by the SDN controller, which can help to increase the trust between the SDN controller and its covered infrastructure components. According to our preliminary implementation, the behavior monitoring unit is able to read all traffic information and perform a validation process that reports any mismatching traffic to the controller.

Software-defined networking (SDN) allows the smart grid to be centrally controlled and managed by decoupling the control plane from the data plane, but it also expands attack surface for attackers. Existing studies about the security of SDN-enabled smart grid (SDSG) mainly focused on static methods such as access control and identity authentication, which is vulnerable to attackers that carefully probe the system. As the attacks become more variable and complex, there is an urgent need for dynamic defense methods. In this paper, we propose a security function virtualization (SFV) based moving target defense of SDSG which makes the attack surface constantly changing. First, we design a dynamic defense mechanism by migrating virtual security function (VSF) instances as the traffic state changes. The centralized SDN controller is re-designed for global status monitoring and migration management. Moreover, we formalize the VSF instances migration problem as an integer nonlinear programming problem with multiple constraints and design a pre-migration algorithm to prevent VSF instances' resources from being exhausted. Simulation results indicate the feasibility of the proposed scheme.

Distributed Denial of Service attack is very harmful to software-defined networking. Effective defense measures are the key to ensure SDN security. An adaptive moving target defense scheme based on end information hopping for SDN is proposed. The source address entropy value and the flow rate method are used to detect the network condition. According to the detection result, the end information is adjusted by time adaptive or space adaptive. A model of active network defense is constructed. The experimental results show that the proposed scheme enhances the anti-attack capability and serviceability compared with other methods, and has greater dynamics and flexibility.

Network virtualization is a fundamental technology for datacenters and upcoming wireless communications (e.g., 5G). It takes advantage of software-defined networking (SDN) that provides efficient network management by converting networking fabrics into SDN-capable devices. Moreover, white-box switches, which provide flexible and fast packet processing, are broadly deployed in commercial datacenters. A white-box switch requires a specific and restricted packet processing pipeline; however, to date, there has been no SDN-based network hypervisor that can support the pipeline of white-box switches. Therefore, in this paper, we propose WhiteVisor: a network hypervisor which can support the physical network composed of white-box switches. WhiteVisor converts a flow rule from the virtual network into a packet processing pipeline compatible with the white-box switch. We implement the prototype herein and show its feasibility and effectiveness with pipeline conversion and overhead.

The requirements regarding network management defined by the continuously rising amount of interconnected devices in the industrial landscape turns it into an increasingly complex task. Associated by the fusion of technologies up to Cyber-Physical Production Systems (CPPS) and the Industrial Internet of Things (IIoT) with its multitude of communicating sensors and actuators new demands arise. In particular, the driving forces of this development, mobility and flexibility, are affecting today's networks. However, it is precisely these wireless solutions, as enabler for this advancement, that create new attack vectors and cyber-security threats. Furthermore, many cryptographic procedures, intended to secure the networks, require additional overhead, which is limiting the transmission bandwidth and speed as well. For this reason, new and efficient solutions must be developed and applied, in order to secure the existing, as well as the future, industrial communication networks. This work proposes a conceptual approach, consisting of a combination of Software-Defined Networking (SDN) and Physical Layer Security (PhySec) to satisfy the network security requirements. Use cases are explained that demonstrate the appropriateness of the approach and it is shown that this is a easy to use and resource efficient, but nevertheless sound and secure approach.

The growth of malware poses a major threat to internet users, governments, and businesses around the world. One of the major types of malware, ransomware, encrypts a user's sensitive information and only returns the original files to the user after a ransom is paid. As malware developers shift the delivery of their product from HTTP to HTTPS to protect themselves from payload inspection, we can no longer rely on deep packet inspection to extract features for malware identification. Toward this goal, we propose a solution leveraging a recent trend in networking hardware, that is programmable forwarding engines (PFEs). PFEs allow collection of per-packet, network monitoring data at high rates. We use this data to monitor the network traffic between an infected computer and the command and control (C&C) server. We extract high-level flow features from this traffic and use this data for ransomware classification. We write a stream processor and use a random forest, binary classifier to utilizes these rich flow records in fingerprinting malicious, network activity without the requirement of deep packet inspection. Our classification model achieves a detection rate in excess of 0.86, while maintaining a false negative rate under 0.11. Our results suggest that a flow-based fingerprinting method is feasible and accurate enough to catch ransomware before encryption.

In the last decade, crypto-ransomware evolved from a family of malicious software with scarce repercussion in the research community, to a sophisticated and highly effective intrusion method positioned in the spotlight of the main organizations for cyberdefense. Its modus operandi is characterized by fetching the assets to be blocked, their encryption, and triggering an extortion process that leads the victim to pay for the key that allows their recovery. This paper reviews the evolution of crypto-ransomware focusing on the implication of the different advances in communication technologies that empowered its popularization. In addition, a novel defensive approach based on the Self-Organizing Network paradigm and the emergent communication technologies (e.g. Software-Defined Networking, Network Function Virtualization, Cloud Computing, etc.) is proposed. They enhance the orchestration of smart defensive deployments that adapt to the status of the monitoring environment and facilitate the adoption of previously defined risk management policies. In this way it is possible to efficiently coordinate the efforts of sensors and actuators distributed throughout the protected environment without supervision by human operators, resulting in greater protection with increased viability

Conventional SDN-based MTD techniques have been mainly developed with a single SDN controller which exposes a single point of failure as well as raises a scalability issue for large-scale networks in achieving both security and performance. The use of multiple SDN controllers has been proposed to ensure both performance and security of SDN-based MTD systems for large-scale networks; however, the effect of using multiple SDN controllers has not been investigated in the state-of-the-art research. In this paper, we propose the SDN based MTD architecture using multiple SDN controllers and validate their security effect (i.e., attack success probability) by implementing an IP shuffling MTD in a testbed using ONOS SDN controllers.

Software-defined networking (SDN) is a recently developed approach to computer networking that brings a centralized orientation to network control, thereby improving network architecture and management. However, as with any communication environment that involves message transmission among users, SDN is confronted by the ongoing challenge of protecting user privacy. In this “Work in Progress (WIP)” research, we propose an SDN security model that applies the moving target defense (MTD) technique to protect communication links from sensitive data leakages. MTD is a security solution aimed at increasing complexity and uncertainty for attackers by concealing sensitive information that may serve as a gateway from which to launch different types of attacks. The proposed MTD-based security model is intended to protect user identities contained in transmitted messages in a way that prevents network intruders from identifying the real identities of senders and receivers. According to the results from preliminary experiments, the proposed MTD model has potential to protect the identities contained in transmitted messages within communication links. This work will be extended to protect sensitive data if an attacker gets access to the network device.

Introduction of SDN (Software-Defined Networking) into the network management turns the formerly quite rigid networks to programmatically reconfigurable, dynamic and high-performing entities, which are managed remotely. At the same time, introduction of the new interfaces evidently widens the attack surface, and new kind of attack vectors are introduced threatening the QoS even critically. Thus, there is need for a security architecture, drawing from the SDN management and monitoring capabilities, and eventually covering the threats posed by the SDN evolution. For efficient security-architecture implementation, we analyze the security risks of SDN and based on that propose heuristic security objectives. Further, we decompose the objectives for effective security control implementation and security metrics definition to support informed security decision-making and continuous security improvement.

In Software-Defined Networks (SDN), so called SDN controllers are responsible for managing the network devices building such a network. Once such a core component of the network has been infected with malicious software (e.g., by a malicious SDN application), an attacker typically has a strong interest in remaining undetected while compromising other devices in the network. Thus, hiding a malicious network state and corresponding network manipulations are important objectives for an adversary. To achieve this, rootkit techniques can be applied in order to manipulate the SDN controller's view of a network. As a consequence, monitoring capabilities of SDN controllers as well as SDN applications with a security focus can be fooled by hiding adverse network manipulations. To tackle this problem, we propose a novel approach capable of detecting and preventing hidden network manipulations before they can attack a network. In particular, our method is able to drop adverse network manipulations before they are applied on a network. We achieve this by comparing the actual network state, which includes both malicious and benign configurations, with the network state which is provided by a potentially compromised SDN controller. In case of an attack, the result of this comparison reveals network manipulations which are adversely removed from an SDN controller's view of a network. To demonstrate the capabilities of this approach, we implement a prototype and evaluate effectiveness as well as efficiency. The evaluation results indicate scalability and high performance of our system, while being able to protect major SDN controller platforms.

Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision of a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.

This paper presents a review on how to benefit from software-defined networking (SDN) to enhance smart grid security. For this purpose, the attacks threatening traditional smart grid systems are classified according to availability, integrity, and confidentiality, which are the main cyber-security objectives. The traditional smart grid architecture is redefined with SDN and a conceptual model for SDN-based smart grid systems is proposed. SDN based solutions to the mentioned security threats are also classified and evaluated. Our conclusions suggest that SDN helps to improve smart grid security by providing real-time monitoring, programmability, wide-area security management, fast recovery from failures, distributed security and smart decision making based on big data analytics.

Vehicular ad hoc networks (VANETs) have become a promising technology in intelligent transportation systems (ITS) with rising interest of expedient, safe, and high-efficient transportation. VANETs are vulnerable to malicious nodes and result in performance degradation because of dynamicity and infrastructure-less. In this paper, we propose a trust based dueling deep reinforcement learning approach (T-DDRL) for communication of connected vehicles, we deploy a dueling network architecture into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the most trusted routing path by deep neural network (DNN) in VANETs, where the trust model is designed to evaluate neighbors' behaviour of forwarding routing information. Simulation results are presented to show the effectiveness of the proposed T-DDRL framework.

The software defined networking framework facilitates flexible and reliable internet of things networks by moving the network intelligence to a centralized location while enabling low power wireless network in the edge. In this paper, we present SD-WSN6Lo, a novel software-defined wireless management solution for 6LoWPAN networks that aims to reduce the management complexity in WSN's. As an example of the technique, a simulation of controlling the power consumption of sensor nodes is presented. The results demonstrate improved energy consumption of approximately 15% on average per node compared to the baseline condition.