Biblio

In the era of big data, many users and companies start to move their data to cloud storage to simplify data management and reduce data maintenance cost. However, security and privacy issues become major concerns because third-party cloud service providers are not always trusty. Although data contents can be protected by encryption, the access patterns that contain important information are still exposed to clouds or malicious attackers. In this paper, we apply the ORAM algorithm to enable privacy-preserving access to big data that are deployed in distributed file systems built upon hundreds or thousands of servers in a single or multiple geo-distributed cloud sites. Since the ORAM algorithm would lead to serious access load unbalance among storage servers, we study a data placement problem to achieve a load balanced storage system with improved availability and responsiveness. Due to the NP-hardness of this problem, we propose a low-complexity algorithm that can deal with large-scale problem size with respect to big data. Extensive simulations are conducted to show that our proposed algorithm finds results close to the optimal solution, and significantly outperforms a random data placement algorithm.
 

Computing systems today have a large number of security configuration settings that enforce security properties. However, vulnerabilities and incorrect configuration increase the potential for attacks. Provable verification and simulation tools have been introduced to eliminate configuration conflicts and weaknesses, which can increase system robustness against attacks. Most of these tools require special knowledge in formal methods and precise specification for requirements in special languages, in addition to their excessive need for computing resources. Video games have been utilized by researchers to make educational software more attractive and engaging. Publishing these games for crowdsourcing can also stimulate competition between players and increase the game educational value. In this paper we introduce a game interface, called NetMaze, that represents the network configuration verification problem as a video game and allows for attack analysis. We aim to make the security analysis and hardening usable and accurately achievable, using the power of video games and the wisdom of crowdsourcing. Players can easily discover weaknesses in network configuration and investigate new attack scenarios. In addition, the gameplay scenarios can also be used to analyze and learn attack attribution considering human factors. In this paper, we present a provable mapping from the network configuration to 3D game objects.
 

This paper propose a fast human detection algorithm of video surveillance in emergencies. Firstly through the background subtraction based on the single Guassian model and frame subtraction, we get the target mask which is optimized by Gaussian filter and dilation. Then the interest points of head is obtained from figures with target mask and edge detection. Finally according to detecting these pionts we can track the head and count the number of people with the frequence of moving target at the same place. Simulation results show that the algorithm can detect the moving object quickly and accurately.

We survey the state-of-the-art on the Internet-of-Things (IoT) from a wireless communications point of view, as a result of the European FP7 project BUTLER which has its focus on pervasiveness, context-awareness and security for IoT. In particular, we describe the efforts to develop so-called (wireless) enabling technologies, aimed at circumventing the many challenges involved in extending the current set of domains (“verticals”) of IoT applications towards a “horizontal” (i.e. integrated) vision of the IoT. We start by illustrating current research effort in machine-to-machine (M2M), which is mainly focused on vertical domains, and we discuss some of them in details, depicting then the necessary horizontal vision for the future intelligent daily routine (“Smart Life”). We then describe the technical features of the most relevant heterogeneous communications technologies on which the IoT relies, under the light of the on-going M2M service layer standardization. Finally we identify and present the key aspects, within three major cross-vertical categories, under which M2M technologies can function as enablers for the horizontal vision of the IoT.

In cloud data center, shared storage with good management is a main structure used for the storage of virtual machines (VM). In this paper, we proposed Hybrid VM storage (HVSTO), a privacy preserving shared storage system designed for the virtual machine storage in large-scale cloud data center. Unlike traditional shared storage, HVSTO adopts a distributed structure to preserve privacy of virtual machines, which are a threat in traditional centralized structure. To improve the performance of I/O latency in this distributed structure, we use a hybrid system to combine solid state disk and distributed storage. From the evaluation of our demonstration system, HVSTO provides a scalable and sufficient throughput for the platform as a service infrastructure.

In many Twitter applications, developers collect only a limited sample of tweets and a local portion of the Twitter network. Given such Twitter applications with limited data, how can we classify Twitter users as either bots or humans? We develop a collection of network-, linguistic-, and application-oriented variables that could be used as possible features, and identify specific features that distinguish well between humans and bots. In particular, by analyzing a large dataset relating to the 2014 Indian election, we show that a number of sentimentrelated factors are key to the identification of bots, significantly increasing the Area under the ROC Curve (AUROC). The same method may be used for other applications as well.

The notion of trust is considered to be the cornerstone on patient-psychiatrist relationship. Thus, a trustfully background is fundamental requirement for provision of effective Ubiquitous Healthcare (UH) service. In this paper, the issue of Trust Evaluation of UH Providers when register UH environment is addressed. For that purpose a novel trust evaluation method is proposed, based on cloud theory, exploiting User Profile attributes. This theory mimics human thinking, regarding trust evaluation and captures fuzziness and randomness of this uncertain reasoning. Two case studies are investigated through simulation in MATLAB software, in order to verify the effectiveness of this novel method.

An intrusion detection system (IDS) inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. A networkbased system, or NIDS, the individual packets flowing through a network are analyzed. In a host-based system, the IDS examines at the activity on each individual computer or host. IDS techniques are divided into two categories including misuse detection and anomaly detection. In recently years, Mobile Agent based technology has been used for distributed systems with having characteristic of mobility and autonomy. In this working we aimed to combine IDS with Mobile Agent concept for more scale, effective, knowledgeable system.

Distributed Denial of Service (DDoS) attacks are one of the most important threads in network systems. Due to the distributed nature, DDoS attacks are very hard to detect, while they also have the destructive potential of classical denial of service attacks. In this study, a novel 2-step system is proposed for the detection of DDoS attacks. In the first step an anomaly detection is performed on the destination IP traffic. If an anomaly is detected on the network, the system proceeds into the second step where a decision on every user is made due to the behaviour models. Hence, it is possible to detect attacks in the network that diverges from users' behavior model.