Biblio

In this paper, a novel method is proposed to assess the power system resilience considering the impacts of hurricanes. Firstly, the transmission line outage model correlated to wind speed is developed. Then, Probability Load Flow (PLF) considering the random outage of lines and the variation of loads is designed, and Latin Hypercube Sampling (LHS) is used to improve the efficiency of Monte Carlo Simulation (MCS) in solving PLF. Moreover, risk indices, including line overloading, node voltage exceeding limit, load shedding and system collapse, are established to assess the resilience of power systems during hurricanes. The method is tested with a modified IEEE 14-bus system, and simulation results indicate the effectiveness of the proposed approach.

Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). Due to no fixed infrastructure of MANETs, a detection node playing as a collector should discover available routes to a collection node for data collection. Notably, route discovery suffers from many attacks (e.g., wormhole attack), thus the detection node should also collect securityrelated data during route discovery and analyze these data for determining reliable routes. However, few literatures provide incentives for security-related data collection in MANETs, and thus the detection node might not collect sufficient data, which greatly impacts the accuracy of attack detection and security measurement. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of RREQ forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization at the same time. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.

Mobile AdHoc Networks (MANET) can be fast implemented, and it is very popular in many specific network requirements, such as UAV (Unmanned Aerial Unit), Disaster Recovery and IoT (Internet of Things) etc. However, MANET is also vulnerable. AODV (Ad hoc On-Demand Distance Vector Routing) protocol is one type of MANET routing protocol and many attacks can be implemented to break the connections on AODV based AdHoc networks. In this article, aim of protecting the MANET security, we modeled the AODV protocol with one type of Automata and analyzed the security vulnerabilities of it; then based on the analyzing results, we proposed an enhancement to AODV protocol to against the Black Hole Attacks. We also implemented the proposed enhancement in NS3 simulator and verified the correctness, usability and efficiency.

Mobile ad hoc network (MANET) is a kind of mobile multi-hop network which can transmit data through intermediate nodes, it has been widely used and become important since the growing of the market of Internet of Things (IoT). However, the transmissions on MANET are vulnerable, it usually suffered with many internal or external attacks, and the research on security topics of MANET are becoming more and more hot recently. Blackhole Attack is one of the most famous attacks to MANET. In this paper, we focus on the Blackhole Attack in AODV protocol, and use NS-3 network simulator to study the impact of Blackhole Attack on network performance parameters, such as the Throughput, End-to-End Delay and Packet Loss Rate. We further analyze the changes in network performance by adjusting the number of blackhole nodes and total nodes, and the movement speed of mobile nodes. The experimental results not only reflect the behaviors of the Blackhole Attack and its damage to the network, but also provide the characteristics of Blackhole Attacks clearly. This is helpful to the research of Blackhole Attack feature extraction and MANET security measurement.

H.264/SVC (Scalable Video Coding) codestreams, which consist of a single base layer and multiple enhancement layers, are designed for quality, spatial, and temporal scalabilities. They can be transmitted over networks of different bandwidths and seamlessly accessed by various terminal devices. With a huge amount of video surveillance and various devices becoming an integral part of the security infrastructure, the industry is currently starting to use the SVC standard to process digital video for surveillance applications such that clients with different network bandwidth connections and display capabilities can seamlessly access various SVC surveillance (sub)codestreams. In order to guarantee the trustworthiness and integrity of received SVC codestreams, engineers and researchers have proposed several authentication schemes to protect video data. However, existing algorithms cannot simultaneously satisfy both efficiency and robustness for SVC surveillance codestreams. Hence, in this article, a highly efficient and robust authentication scheme, named TrustSSV (Trust Scalable Surveillance Video), is proposed. Based on quality/spatial scalable characteristics of SVC codestreams, TrustSSV combines cryptographic and content-based authentication techniques to authenticate the base layer and enhancement layers, respectively. Based on temporal scalable characteristics of surveillance codestreams, TrustSSV extracts, updates, and authenticates foreground features for each access unit dynamically with background model support. Using SVC test sequences, our experimental results indicate that the scheme is able to distinguish between content-preserving and content-changing manipulations and to pinpoint tampered locations. Compared with existing schemes, the proposed scheme incurs very small computation and communication costs.

Mobile Ad Hoc Network (MANET) is a multi-hop temporary and autonomic network comprised of a set of mobile nodes. MANETs have the features of non-center, dynamically changing topology, multi-hop routing, mobile nodes, limited resources and so on, which make it face more threats. Trust evaluation is used to support nodes to cooperate in a secure and trustworthy way through evaluating the trust of participating nodes in MANETs. However, many trust evaluation models proposed for MANETs still have many problems and shortcomings. In this paper, we review the existing researches, then analyze and compare the proposed trust evaluation models by presenting and applying uniform criteria in order to point out a number of open issues and challenges and suggest future research trends.

Multimedia has been exponentially increasing as the biggest big data, which consist of video clips, images, and audio files. Processing and analyzing them on a cloud data center have become a preferred solution that can utilize the large pool of cloud resources to address the problems caused by the tremendous amount of unstructured multimedia data. However, there exist many challenges in processing multimedia big data on a cloud data center, such as multimedia data representation approach, an efficient networking model, and an estimation method for traffic patterns. The primary purpose of this article is to develop a novel tensor-based software-defined networking model on a cloud data center for multimedia big-data computation and communication. First, an overview of the proposed framework is provided, in which the functions of the representative modules are briefly illustrated. Then, three models,—forwarding tensor, control tensor, and transition tensor—are proposed for management of networking devices and prediction of network traffic patterns. Finally, two algorithms about single-mode and multimode tensor eigen-decomposition are developed, and the incremental method is employed for efficiently updating the generated eigen-vector and eigen-tensor. Experimental results reveal that the proposed framework is feasible and efficient to handle multimedia big data on a cloud data center.