Biblio
The development of Vehicular Ad-hoc NETwork (VANET) has brought many conveniences to human beings, but also brings a very prominent security problem. The traditional solution to the security problem is based on centralized approach which requires a trusted central entity which exists a single point of failure problem. Moreover, there is no approach of technical level to ensure security of data. Therefore, this paper proposes a security architecture of VANET based on blockchain and mobile edge computing. The architecture includes three layers, namely perception layer, edge computing layer and service layer. The perception layer ensures the security of VANET data in the transmission process through the blockchain technology. The edge computing layer provides computing resources and edge cloud services to the perception layer. The service layer uses the combination of traditional cloud storage and blockchain to ensure the security of data.
Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.
In spite of numerous advantages of biometrics-based personal authentication systems over traditional security systems based on token or knowledge, they are vulnerable to attacks that can decrease their security considerably. In this paper, we propose a new hardware solution to protect biometric templates such as fingerprint. The proposed scheme is based on chaotic N × N grid multi-scroll system and it is implemented on Xilinx FPGA. The hardware implementation is achieved by applying numerical solution methods in our study, we use EM (Euler Method). Simulation and experimental results show that the proposed scheme allows a low cost image encryption for embedded systems while still providing a good trade-off between performance and hardware resources. Indeed, security analysis performed to the our scheme, is strong against known different attacks, such as: brute force, statistical, differential, and entropy. Therefore, the proposed chaos-based multiscroll encryption algorithm is suitable for use in securing embedded biometric systems.
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.
Information Centric Networking (ICN) changed the communication model from host-based to content-based to cope with the high volume of traffic due to the rapidly increasing number of users, data objects, devices, and applications. ICN communication model requires new security solutions that will be integrated with ICN architectures. In this paper, we present a security framework to manage ICN traffic by detecting, preventing, and responding to ICN attacks. The framework consists of three components: availability, access control, and privacy. The availability component ensures that contents are available for legitimate users. The access control component allows only legitimate users to get restrictedaccess contents. The privacy component prevents attackers from knowing content popularities or user requests. We also show our specific solutions as examples of the framework components.
Cloud computing is the major paradigm in today's IT world with the capabilities of security management, high performance, flexibility, scalability. Customers valuing these features can better benefit if they use a cloud environment built using HPC fabric architecture. However, security is still a major concern, not only on the software side but also on the hardware side. There are multiple studies showing that the malicious users can affect the regular customers through the hardware if they are co-located on the same physical system. Therefore, solving possible security concerns on the HPC fabric architecture will clearly make the fabric industries leader in this area. In this paper, we propose an autonomic HPC fabric architecture that leverages both resilient computing capabilities and adaptive anomaly analysis for further security.
Design for Testability (DfT) techniques allow devices to be tested at various levels of the manufacturing process. Scan architecture is a dominantly used DfT technique, which supports a high level of fault coverage, observability and controllability. However, scan architecture can be used by hardware attackers to gain critical information stored within the device. The security threats due to an unrestricted access provided by scan architecture has to be addressed to ensure hardware security. In this work, a solution based on the Clock and Data Recovery (CDR) method has been presented to authenticate users and limit the access to the scan architecture to authorized users. As compared to the available solution the proposed method presents a robust performance and reduces the area overhead by more than 10%.
Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are Naive Bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.
Among the threats to information systems of state institutions, enterprises and financial organizations of particular importance are those originating from organized criminal groups that specialize in obtaining unauthorized access to the computer information protected by law. Criminal groups often possess a material base including financial, technical, human and other resources that allow to perform targeted attacks on information resources as secretly as possible. The principal features of such targeted attacks are the use of software created or modified specifically for use in illegal purposes with respect to specific organizations. Due to these circumstances, the detection of such attacks is quite difficult, and their prevention is even more complicated. In this regard, the task of identifying and analyzing such threats is very relevant. One effective way to solve it is to implement the Honeypot system, which allows to research the strategy and tactics of the attackers. In the present article, there is proposed the original architecture of the Honeypot system designed to study targeted attacks on information systems of criminogenic objects. The architectural design includes such basic elements as the functional component, the registrar of events occurring in the system and the protector. The key features of the proposed Honeypot system are considered, and the functional purpose of its main components is described. The proposed system can find its application in providing information security of institutions, organizations and enterprises, it can be used in the development of information security systems.
The need to process the verity, volume and velocity of data generated by today's Internet of Things (IoT) devices has pushed both academia and the industry to investigate new architectural alternatives to support the new challenges. As a result, Edge Computing (EC) has emerged to address these issues, by placing part of the cloud resources (e.g., computation, storage, logic) closer to the edge of the network, which allows faster and context dependent data analysis and storage. However, as EC infrastructures grow, different providers who do not necessarily trust each other need to collaborate in order serve different IoT devices. In this context, EC infrastructures, IoT devices and the data transiting the network all need to be subject to identity and provenance checks, in order to increase trust and accountability. Each device/data in the network needs to be identified and the provenance of its actions needs to be tracked. In this paper, we propose a blockchain container based architecture that implements the W3C-PROV Data Model, to track identities and provenance of all orchestration decisions of a business network. This architecture provides new forms of interaction between the different stakeholders, which supports trustworthy transactions and leads to a new decentralized interaction model for IoT based applications.
Ubiquitous Healthcare System (U-Healthcare) is a well-known application of wireless sensor networking (WSN). In this system, the sensors take less power for operating the function. As the data transfers between sensor and other stations is sensitive so there needs to provide a security scheme. Due to the low life of sensor nodes in Wireless Sensor Networks (WSN), asymmetric key based security (AKS) architecture is always considered as unsuitable for these types of networks. Several papers have been published in recent past years regarding how to incorporate AKS in WSN, Haque et al's Asymmetric key based Architecture (AKA) is one of them. But later it is found that this system has authentication problem and therefore prone to man-in-the-middle (MITM) attack, furthermore it is not a truly asymmetric based scheme. We address these issues in this paper and proposed a complete asymmetric approach using PEKS-PM (proposed by Pham in [8]) to remove impersonation attack. We also found some other vulnerabilities in the original AKA system and proposed solutions, therefore making it a better and enhanced asymmetric key based architecture.
Edge computing can potentially play a crucial role in enabling user authentication and monitoring through context-aware biometrics in military/battlefield applications. For example, in Internet of Military Things (IoMT) or Internet of Battlefield Things (IoBT),an increasing number of ubiquitous sensing and computing devices worn by military personnel and embedded within military equipment (combat suit, instrumented helmets, weapon systems, etc.) are capable of acquiring a variety of static and dynamic biometrics (e.g., face, iris, periocular, fingerprints, heart-rate, gait, gestures, and facial expressions). Such devices may also be capable of collecting operational context data. These data collectively can be used to perform context-adaptive authentication in-the-wild and continuous monitoring of soldier's psychophysical condition in a dedicated edge computing architecture.
Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by performing much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
Dual Connectivity(DC) is one of the key technologies standardized in Release 12 of the 3GPP specifications for the Long Term Evolution (LTE) network. It attempts to increase the per-user throughput by allowing the user equipment (UE) to maintain connections with the MeNB (master eNB) and SeNB (secondary eNB) simultaneously, which are inter-connected via non-ideal backhaul. In this paper, we focus on one of the use cases of DC whereby the downlink U-plane data is split at the MeNB and transmitted to the UE via the associated MeNB and SeNB concurrently. In this case, out-of-order packet delivery problem may occur at the UE due to the delay over the non-ideal backhaul link, as well as the dynamics of channel conditions over the MeNB-UE and SeNB-UE links, which will introduce extra delay for re-ordering the packets. As a solution, we propose to adopt the RaptorQ FEC code to encode the source data at the MeNB, and then the encoded symbols are separately transmitted through the MeNB and SeNB. The out-of-order problem can be effectively eliminated since the UE can decode the original data as long as it receives enough encoded symbols from either the MeNB or SeNB. We present detailed protocol design for the RaptorQ code based concurrent transmission scheme, and simulation results are provided to illustrate the performance of the proposed scheme.
In the near future, vehicular cloud will help to improve traffic safety and efficiency. Unfortunately, a computing of vehicular cloud and fog cloud faced a set of challenges in security, authentication, privacy, confidentiality and detection of misbehaving vehicles. In addition to, there is a need to recognize false messages from received messages in VANETs during moving on the road. In this work, the security issues and challenges for computing in the vehicular cloud over for computing is studied.
Fog computing is a new paradigm which extends cloud computing services into the edge of the network. Indeed, it aims to pool edge resources in order to deal with cloud's shortcomings such as latency problems. However, this proposal does not ensure the honesty and the good behavior of edge devices. Thus, security places itself as an important challenge in front of this new proposal. Authentication is the entry point of any security system, which makes it an important security service. Traditional authentication schemes endure latency issues and some of them do not satisfy fog-computing requirements such as mutual authentication between end devices and fog servers. Thus, new authentication protocols need to be implemented. In this paper, we propose a new efficient authentication scheme for fog computing architecture. Our scheme ensures mutual authentication and remedies to fog servers' misbehaviors. Moreover, fog servers need to hold only a couple of information to verify the authenticity of every user in the system. Thus, it provides a low overhead in terms of storage capacity. Finally, we show through experimentation the efficiency of our scheme.
As an extension of cloud computing, fog computing is proving itself more and more potentially useful nowadays. Fog computing is introduced to overcome the shortcomings of cloud computing paradigm in handling the massive amount of traffic caused by the enormous number of Internet of Things devices being increasingly connected to the Internet on daily basis. Despite its advantages, fog architecture introduces new security and privacy threats that need to be studied and solved as soon as possible. In this work, we explore two privacy issues posed by the fog computing architecture and we define privacy challenges according to them. The first challenge is related to the fog's design purposes of reducing the latency and improving the bandwidth, where the existing privacy-preserving methods violate these design purposed. The other challenge is related to the proximity of fog nodes to the end-users or IoT devices. We discuss the importance of addressing these challenges by putting them in the context of real-life scenarios. Finally, we propose a privacy-preserving fog computing paradigm that solves these challenges and we assess the security and efficiency of our solution.
To solve the problems associated with large data volume real-time processing, heterogeneous systems using various computing devices are increasingly used. The characteristic of solving this class of problems is related to the fact that there are two directions for improving methods of real-time data analysis: the first is the development of algorithms and approaches to analysis, and the second is the development of hardware and software. This article reviews the main approaches to the architecture of a hardware-software solution for traffic capture and deep packet inspection (DPI) in data transmission networks with a bandwidth of 80 Gbit/s and higher. At the moment there are software and hardware tools that allow designing the architecture of capture system and deep packet inspection: 1) Using only the central processing unit (CPU); 2) Using only the graphics processing unit (GPU); 3) Using the central processing unit and graphics processing unit simultaneously (CPU + GPU). In this paper, we consider these key approaches. Also attention is paid to both hardware and software requirements for the architecture of solutions. Pain points and remedies are described.
Current technologies to include cloud computing, social networking, mobile applications and crowd and synthetic intelligence, coupled with the explosion in storage and processing power, are evolving massive-scale marketplaces for a wide variety of resources and services. They are also enabling unprecedented forms and levels of collaborations among human and machine entities. In this new era, trust remains the keystone of success in any relationship between two or more parties. A primary challenge is to establish and manage trust in environments where massive numbers of consumers, providers and brokers are largely autonomous with vastly diverse requirements, capabilities, and trust profiles. Most contemporary trust management solutions are oblivious to diversities in trustors' requirements and contexts, utilize direct or indirect experiences as the only form of trust computations, employ hardcoded trust computations and marginally consider collaboration in trust management. We surmise the need for reference architecture for trust management to guide the development of a wide spectrum of trust management systems. In our previous work, we presented a preliminary reference architecture for trust management which provides customizable and reconfigurable trust management operations to accommodate varying levels of diversity and trust personalization. In this paper, we present a comprehensive taxonomy for trust management and extend our reference architecture to feature collaboration as a first-class object. Our goal is to promote the development of new collaborative trust management systems, where various trust management operations would involve collaborating entities. Using the proposed architecture, we implemented a collaborative personalized trust management system. Simulation results demonstrate the effectiveness and efficiency of our system.