Biblio

Existing Lipschitz-based provable defences to adversarial examples only cover the L2 threat model. We introduce the first bound that makes use of Lipschitz continuity to provide a more general guarantee for threat models based on any Lp norm. Additionally, a new strategy is proposed for designing network architectures that exhibit superior provable adversarial robustness over conventional convolutional neural networks. Experiments are conducted to validate our theoretical contributions, show that the assumptions made during the design of our novel architecture hold in practice, and quantify the empirical robustness of several Lipschitz-based adversarial defence methods.

With the improvement of people's living standards, more and more network users access the network, including a large number of infrastructure, these devices constitute the Internet of things(IoT). With the rapid expansion of devices in the IoT, the data transmission between the IoT has become more complex, and the security issues are facing greater challenges. SDN as a mature network architecture, its security has been affirmed by the industry, it separates the data layer from the control layer, thus greatly improving the security of the network. In this paper, we apply the SDN to the IoT, and propose a IoT network architecture based on SDN. In this architecture, we not only make use of the security features of SDN, but also deploy different security modules in each layer of SDN to integrate, analyze and plan various data through the IoT, which undoubtedly improves the security performance of the network. In the end, we give a comprehensive introduction to the system and verify its performance.

Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom. The sampled architectures together with the accuracies they achieve provide a rich basis for our study. Our ''robust architecture Odyssey'' reveals several valuable observations: 1) densely connected patterns result in improved robustness; 2) under computational budget, adding convolution operations to direct connection edge is effective; 3) flow of solution procedure (FSP) matrix is a good indicator of network robustness. Based on these observations, we discover a family of robust architectures (RobNets). On various datasets, including CIFAR, SVHN, Tiny-ImageNet, and ImageNet, RobNets exhibit superior robustness performance to other widely used architectures. Notably, RobNets substantially improve the robust accuracy ( 5% absolute gains) under both white-box and black-box attacks, even with fewer parameter numbers. Code is available at https://github.com/gmh14/RobNets.

In the current generation, road accidents and security problems increase dramatically worldwide in our day to day life. In order to overcome this, Vehicular Ad-hoc Network (VANETs) is considered as a key element of future Intelligent Transportation Systems (ITS). With the advancement in vehicular communications, the attacks have also increased, and such architecture is still exposed to many weaknesses which led to numerous security threats that must be addressed before VANET technology is practically and safely adopted. Distributed Denial of Service (DDoS) attack, replay attacks and Sybil attacks are the significant security threats that affect the communication and privacy in VANET. An algorithm to detect and prevent various kinds of security attacks in VANET communication has been designed and proposed in this work. An analysis has also been done by applying four protocols on an existing scenario of real traffic simulator using OpenStreetMap and the best suitable protocol has been selected for further application. The evaluation has been done using SUMO, NS3 and Java simulation environment. Simulation results and extensive performance analysis shows that our proposed Algorithm performs well in detecting and preventing the attacks in VANET communication.

With the increasing diversity of application needs (datacenters, IoT, content retrieval, industrial automation, etc.), new network architectures are continually being proposed to address specific and particular requirements. From a network management perspective, it is both important and challenging to enable evolution towards such new architectures. Given the ubiquity of the Internet, a clean-slate change of the entire infrastructure to a new architecture is impractical. It is believed that we will see new network architectures coming into existence with support for interoperability between separate architectural islands. We may have servers, and more importantly, content, residing in domains having different architectures. This paper presents COIN, a content-oriented interoperability framework for current and future Internet architectures. We seek to provide seamless connectivity and content accessibility across multiple of these network architectures, including the current Internet. COIN preserves each domain's key architectural features and mechanisms, while allowing flexibility for evolvability and extensibility. We focus on Information-Centric Networks (ICN), the prominent class of Future Internet architectures. COIN avoids expanding domain-specific protocols or namespaces. Instead, it uses an application-layer Object Resolution Service to deliver the right "foreign" names to consumers. COIN uses translation gateways that retain essential interoperability state, leverages encryption for confidentiality, and relies on domain-specific signatures to guarantee provenance and data integrity. Using NDN and MobilityFirst as important candidate solutions of ICN, and IP, we evaluate COIN. Measurements from an implementation of the gateways show that the overhead is manageable and scales well.

In modern high-performance computing (HPC) systems, network congestion is an important factor that contributes to performance degradation. However, how network congestion impacts application performance is not fully understood. As Aries network, a recent HPC network architecture featuring a dragonfly topology, is equipped with network counters measuring packet transmission statistics on each router, these network metrics can potentially be utilized to understand network performance. In this work, by experiments on a large HPC system, we quantify the impact of network congestion on various applications' performance in terms of execution time, and we correlate application performance with network metrics. Our results demonstrate diverse impacts of network congestion: while applications with intensive MPI operations (such as HACC and MILC) suffer from more than 40% extension in their execution times under network congestion, applications with less intensive MPI operations (such as Graph500 and HPCG) are mostly not affected. We also demonstrate that a stall-to-flit ratio metric derived from Aries network counters is positively correlated with performance degradation and, thus, this metric can serve as an indicator of network congestion in HPC systems.

Software Defined Networking (SDN) is a promising network architecture that aims at providing high flexibility through the separation between network logic (control plane) and forwarding functions (data plane). This separation provides logical centralization of controllers, global network overview, ease of programmability, and a range of new SDN-compliant services. In recent years, the adoption of SDN in enterprise networks has been constantly increasing. In the meantime, new challenges arise in different levels such as scalability, management, and security. In this paper, we elaborate on complex security issues in the current SDN architecture. Especially, reconnaissance attack where attackers generate traffic for the goal of exploring existing services, assets, and overall network topology. To eliminate reconnaissance attack in SDN environment, we propose SDN-based solution by utilizing distributed firewall application, security policy, and OpenFlow counters. Distributed firewall application is capable of tracking the flow based on pre-defined states that would monitor the connection to sensitive nodes toward malicious activity. We utilize Mininet to simulate the testing environment. We are able to detect and mitigate this type of attack at early stage and in average around 7 second.

Due to the harsh environment and energy limitation, maintaining efficient communication is crucial to the lifetime of Underwater Sensor Networks (UWSN). Named Data Networking (NDN), one of future network architectures, begins to be applied to UWSN. Although Underwater Named Data Networking (UNDN) performs well in data transmission, it still faces some security threats, such as the Denial-of-Service (DoS) attacks caused by Interest Flooding Attacks (IFAs). In this paper, we present a new type of DoS attacks, named as Synergetic Denial-of-Service (SDoS). Attackers synergize with each other, taking turns to reply to malicious interests as late as possible. SDoS attacks will damage the Pending Interest Table, Content Store, and Forwarding Information Base in routers with high concealment. Simulation results demonstrate that the SDoS attacks quadruple the increased network traffic compared with normal IFAs and the existing IFA detection algorithm in UNDN is completely invalid to SDoS attacks. In addition, we analyze the infection problem in UNDN and propose a defense method Trident based on carefully designed adaptive threshold, burst traffic detection, and attacker identification. Experiment results illustrate that Trident can effectively detect and resist both SDoS attacks and normal IFAs. Meanwhile, Trident can robustly undertake burst traffic and congestion.

Software defined network (SDN) is an emerging network architecture. Its control logic and forwarding logic are separated. SDN has the characteristics of centralized management, which makes it easier for malicious attackers to use the security vulnerabilities of SDN networks to implement distributed denial Service (DDoS) attack. Information entropy is a kind of lightweight DDoS early detection method. This paper proposes a DDoS attack detection method in SDN networks based on φ-entropy. φ-entropy can adjust related parameters according to network conditions and enlarge feature differences between normal and abnormal traffic, which can make it easier to detect attacks in the early stages of DDoS traffic formation. Firstly, this article demonstrates the basic properties of φ-entropy, mathematically illustrates the feasibility of φ-entropy in DDoS detection, and then we use Mini-net to conduct simulation experiments to compare the detection effects of DDoS with Shannon entropy.

Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area.

Cyber-physical systems are an integral component of weapons, sensors and autonomous vehicles, as well as cyber assets directly supporting tactical forces. Mission resilience of tactical networks affects command and control, which is important for successful military operations. Traditional engineering methods for mission assurance will not scale during battlefield operations. Commanders need useful mission resilience metrics to help them evaluate the ability of cyber assets to recover from incidents to fulfill mission essential functions. We develop 6 cyber resilience metrics for tactical network architectures. We also illuminate how psychometric modeling is necessary for future research to identify resilience metrics that are both applicable to the dynamic mission state and meaningful to commanders and planners.

At present, the network architecture is based on the TCP/IP protocol and node communications are achieved by the IP address and identifier of the node. The IP address in the network remains basically unchanged, so it is more likely to be attacked by network intruder. To this end, it is important to make periodic dynamic hopping in a specific address space possible, so that an intruder fails to obtain the internal network address and grid topological structure in real time and to continue to perform infiltration by the building of a new address space layout randomization system on the basis of SDN from the perspective of an attacker.

With the gradual progress of cloud computing, big data, network virtualization and other network technology. The traditional network architecture can no longer support this huge business. At this time, the clean slate team defined a new network architecture, SDN (Software Defined Network). It has brought about tremendous changes in the development of today's networks. The controller sends the flow table down to the switch, and the data flow is forwarded through matching flow table items. However, the current flow table resources of the SDN switch are very limited. Therefore, this paper studies the technology of the latest SDN Flow table optimization at home and abroad, proposes an efficient optimization scheme of Flow table item on the betweenness centrality through the main road selection algorithm, and realizes related applications by setting up experimental topology. Experiments show that this scheme can greatly reduce the number of flow table items of switches, especially the more hosts there are in the topology, the more obvious the experimental effect is. And the experiment proves that the optimization success rate is over 80%.

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.

UAANET (UAV Ad hoc Network) is defined as an autonomous system made of swarm of UAVs (Unmanned Aerial Vehicle) and GCS (Ground Control Station). Compared to other types of MANET (Mobile Ad hoc network), UAANET have some unique features and bring several challenges. One of them is the design of routing protocol. It must be efficient for creating routes between nodes and dynamically adjusting to the rapidly changing topology. It must also be secure to protect the integrity of the network against malicious attackers. In this paper, we will present the architecture and the performance evaluation (based on both real-life experimental and emulation studies) of a secure routing protocol called SUAP (Secure UAV Ad hoc routing Protocol). SUAP ensures routing services between nodes to exchange real-time traffic and also guarantees message authentication and integrity to protect the network integrity. Additional security mechanisms were added to detect Wormhole attacks. Wormhole attacks represent a high level of risk for UAV ad hoc network and this is the reason why we choose to focus on this specific multi node attack. Through performance evaluation campaign, our results show that SUAP ensures the expected security services against different types of attacks while providing an acceptable quality of service for real-time data exchanges.

SDN is new networking concept which has revolutionized the network architecture in recent years. It decouples control plane from data plane. Architectural change provides re-programmability and centralized control management of the network. At the same time it also increases the complexity of underlying physical infrastructure of the network. Unfortunately, the centralized control of the network introduces new vulnerabilities and attacks. Attackers can exploit the limitation of centralized control by DDoS attack on control plane. The entire network can be compromised by DDoS attack. Based on packet entropy, a solution for mitigation of DDoS attack provided in the proposed scheme.

Named data network (NDN) is one of the most promising information-centric networking architectures, where the core concept is to focus on the named data (or contents) themselves. Users in NDN can easily send a request packet to get the desired content regardless of its address. The routers in NDN have cache functionality to make the users instantly retrieve the desired file. Thus, the user can immediately get the desired file from the nearby nodes instead of the remote host. Nevertheless, NDN is a novel proposal and there are still some open issues to be resolved. In view of previous research, it is a challenge to achieve access control on a specific user and support potential receivers simultaneously. In order to solve it, we present a fine-grained access control mechanism tailored for NDN, supporting data confidentiality, potential receivers, and mobility. Compared to previous works, this is the first to support fine-grained access control and potential receivers. Furthermore, the proposed scheme achieves provable security under the DBDH assumption.

The defense-in-depth approach has been widely recommended for designing critical information infrastructure, however, the lack of holistic design guidelines makes it difficult for many organizations to adopt the concept. Therefore, this paper proposes a holistic architectural framework and guidelines based on ring-based nested network zones for designing such highly secured information systems. This novel security architectural framework and guidelines offer the overall structural design and implementation options for holistically designing the N-tier/shared nothing system architectures. The implementation options, e.g. for the zone's perimeters, are recommended to achieve different capability levels of security or to trade off among different required security attributes. This framework enables the adaptive capability suitable for different real-world contexts. This paper also proposes an attack-hops verification approach as a tool to evaluate the architectural design.

Exponential growth of data produced and consumed by consumer electronic systems will strain data connectivity technologies beyond the next ten years. A private universal data platform is therefore required to connect CE Hardware for improved security, reliability and energy use. A novel Push-Pull data network architecture is hereto presented, employing multiple bridged peripheral links to create an ultra-fast, ultra-secure, private and low power data network to connect nearly any system. Bridging standard USB 3.0 technologies, we demonstrate a universally secure, ultra-low power and scalable switchable data platform offering the highest level of data privacy, security and performance. Delivering up to 12 times the throughput speeds of existing USB 3.0 data transfer cables, the presented solution builds on the reliability of universal peripheral communications links using proven ports, protocols and low-power components. A “Software Constructed” ad-hoc circuit network, the presented digital architecture delivers frictionless adoption and exceptional price-performance measures connecting both existing and future CE hardware.

Vehicular Ad-hoc Network (VANET) can provide vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications for efficient and safe transportation. The vehicles features high mobility, thus undergoing frequent handovers when they are moving, which introduces the significant overload on the network entities. To address the problem, the distributed mobility management (DMM) protocol for next generation mobile network has been proposed, which can be well combined with VANETs. Although the existing DMM solutions can guarantee the smooth handovers of vehicles, the security has not been fully considered in the mobility management. Moreover, the most of existing schemes cannot support group communication scenario. In this paper, we propose an efficient and secure group mobility management scheme based on the blockchain. Specifically, to reduce the handover latency and signaling cost during authentication, aggregate message authentication code (AMAC) and one-time password (OTP) are adopted. The security analysis and the performance evaluation results show that the proposed scheme can not only enhance the security functionalities but also support fast handover authentication.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

The internet paradigm was designed to forward packets from host-to-host. But nowadays the focal point has moved to data. The Internet Centric Network (ICN) provides architectures to meet this requirement. The Content Centric Network (CCN) is the most widely used ICN architecture. Information Centric Network's ability to perform in-network caching lead to faster retrieval of data on subsequent request. Although latency is solved, caching in a router makes it vulnerable to attacks that focus on the cache. One such attack is content poisoning, that will fill the router with poisoned content making the end user difficult to retrieve original valid data. In this paper, we propose a solution to mitigate content poisoning attack that will consume minimum time and require minimal storage overhead during the verification process.

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.

Mobile ad hoc networks (MANET) is a type of networks that consists of autonomous nodes connecting directly without a top-down network architecture or central controller. Absence of base stations in MANET force the nodes to rely on their adjacent nodes in transmitting messages. The dynamic nature of MANET makes the relationship between nodes untrusted due to mobility of nodes. A malicious node may start denial of service attack at network layer to discard the packets instead of forwarding them to destination which is known as black hole attack. In this paper a secure and trust based approach based on ad hoc on demand distance vector (STAODV) has been proposed to improve the security of AODV routing protocol. The approach isolates the malicious nodes that try to attack the network depending on their previous information. A trust level is attached to each participating node to detect the level of trust of that node. Each incoming packet will be examined to prevent the black hole attack.

SDN is a new network architecture for control and data forwarding logic separation, able to provide a high degree of openness and programmability, with many advantages not available by traditional networks. But there are still some problems unsolved, for example, it is easy to cause the controller to be attacked due to the lack of verifying the source of the packet, and the limited range of match fields cannot meet the requirement of the precise control of network services etc. Aiming at the above problems, this paper proposes a SDN network security control forwarding mechanism based on cipher identification, when packets flow into and out of the network, the forwarding device must verify their source to ensure the user's non-repudiation and the authenticity of packets. Besides administrators control the data forwarding based on cipher identification, able to form network management and control capabilities based on human, material, business flow, and provide a new method and means for the future of Internet security.