Multi-wavelets from B-spline super-functions with approximation order

Approximation order is an important feature of all wavelets. It implies that polynomials up to degree p−1 are in the space spanned by the scaling function(s). In the scalar case, the scalar sum rules determine the approximation order or the left eigenvectors of the infinite down-sampled convolution matrix H determine the combinations of scaling functions required to produce the desired polynomial. For multi-wavelets the condition for approximation order is similar to the conditions in the scalar case. Generalized left eigenvectors of the matrix H_f; a finite portion of H determines the combinations of scaling functions that produce the desired superfunction from which polynomials of desired degree can be reproduced. The superfunctions in this work are taken to be B-splines. However, any refinable function can serve as the superfunction. The condition of approximation order is derived and new, symmetric, compactly supported and orthogonal multi-wavelets with approximation orders one, two, three and four are constructed.

Source-based filtering scheme against DDOS attacks

IP address spoofing is employed by a lot of DDoS attack tools. Most of the current research on DDoS attack packet filtering depends on cooperation among routers, which is hard to achieve in real campaigns. Therefore, in the paper, we propose a novel filtering scheme based on source information in this paper to defend against various source IP address spoofing. The proposed method works independently at the potential victim side, and accumulates the source information of its clients, for instance, source IP addresses, hops from the server during attacks free period. When a DDoS attack alarm is raised, we can filter out the attack packets based on the accumulated knowledge of the legitimate clients. We divide the source IP addresses into n(1 ≤ n ≤ 32) segments in our proposed algorithm; as a result, we can therefore release the challenge storage and speed up the procedure of information retrieval. The system which is proposed by us and the experiments indicated that the proposed method works effectively and efficiently.

OST : A transaction based online social trust model for social network and file sharing security

The continuous growth of the users pool of Social Networking web sites such as Facebook and MySpace, and their incessant augmentation of services and capabilities will in the future, meet and compare in contrast with today's Content distribution Networks (CDN) and Peer-to-Peer File sharing applications such as Kazaa and BitTorrent, but how can these two main streams applications, that already encounter their own security problems cope with the combined issues, trust for Social Networks, content and index poisoning in CDN? We will address the problems of Social Trust and File Sharing with an overlay level of trust model based on social activity and transactions, this can be an answer to enable users to increase the reliability of their online social life and also enhance the content distribution and create a better file sharing example. The aim of this research is to lower the risk of malicious activity on a given Social Network by applying a correlated trust model, to guarantee the validity of someone's identity, privacy and trustfulness in sharing content.

M-Dimension : multi-characteristics based routing protocol in human associated delay-tolerant networks with improved performance over one dimensional classic models

Human associated delay-tolerant network (HDTN) is a new delay-tolerant network where mobile devices are associated with humans. It can be viewed from both their geographic and social dimensions. The combination of these different dimensions can enable us to more accurately comprehend a delay-tolerant network and consequently use this multi-dimensional information to improve overall network efficiency. Alongside the geographic dimension of the network which is concerned with geographic topology of routing, social dimensions such as social hierarchy can be used to guide the routing message to improve not only the routing efficiency for individual nodes, but also efficiency for the entire network.

We propose a multi-dimensional routing protocol (M-Dimension) for the human associated delay-tolerant network which uses the local information derived from multiple dimensions to identify a mobile node more accurately. Each dimension has a weight factor and is organized by the Distance Function to select an intermediary and applies multi-cast routing. We compare M-Dimension to existing benchmark routing protocols using the MIT Reality Dataset, a well-known benchmark dataset based on a human associated mobile network trace file. The results of our simulations show that M-Dimension has a significant increase in the average success ratio and is very competitive when End-to-End Delay of packet delivery is used in comparison to other multi-cast DTN routing protocols.

T-OSN : a trust evaluation model in online social networks

Now days, the online social networks (OSN) have gained considerable popularity. More and more people use OSN to share their interests and make friends, also the OSN helps users overcome the geographical barriers. With the development of OSN, there is an important problem users have to face that is trust evaluation. Before user makes friends with a stranger, the user need to consider the following issues: Can a stranger be trusted? How much the stranger can be trusted? How to measure the trust of a stranger? In this paper, we take two factors, Degree and Contact Interval into consideration, which produce a new trust evaluation model (T-OSN). T-OSN is aimed to solve how to evaluate the trust value of an OSN user, also which is more efficient, more reliable and easy to implement. Base on our research, this model can be used in wide range, such as online social network (OSN) trust evaluation, mobile network message forwarding, ad hoc wireless networking, routing message on Internet and peer-to-peer file sharing network. The T-OSN model has following obvious advantages compare to other trust evaluate methods. First of all, it is not base on features of traditional social network, such as, distance and shortest path. We choose the special features of OSN to build up the model, that is including numbers of friends(Degree) and contact frequency(Contact Interval). These species features makes our model more suitable to evaluate OSN users trust value. Second, the formulations of our model are quite simple but effective. That means, to calculate the result by using our formulations will not cost too much resources. Last but not least, our model is easy to implement for an OSN website, because of the features that we used in our model, such as numbers of friends and contact frequency are easy to obtain. To sum up, our model is using a few resources to obtain a valuable trust value that can help OSN users to solve an important security problem, we believe that will be big step - or development of OSN.

Improving P2P IPTV random peers search through user similarity

With internet services to the end users becoming more homogenous, thus providing high bandwidth for all users, multimedia services such as IPTV to the public as a whole will finally become a reality, but even given the more abundant resources, IPTV architecture is far from being highly available due to technical limitations, we aim to provide a meaningful optimization in the P2P distribution model, which is currently based on a random structure bounded by high delays and low performance, by using channel probability, user's habits studies and users' similarity, in order to optimize one of the key aspects of IPTV which is the peers management, which directly reflects on resources and user's Quality of Experience.

Effects of social characters in viral propagation seeding strategies in online social networks

Online social networks have not only become a point of aggregation and exchange of information, they have so radically rooted into our everyday behaviors that they have become the target of important network attacks. We have seen an increasing trend in Sybil based activity, such as in personification, fake profiling and attempts to maliciously subvert the community stability in order to illegally create benefits for some individuals, such as online voting, and also from more classic informatics assaults using specifically mutated worms. Not only these attacks, in the latest months, we have seen an increase in spam activities on social networks such as Facebook and RenRen, and most importantly, the first attempts at propagating worms within these communities. What differentiates these attacks from normal network attacks, is that compared to anonymous and stealthy activities, or by commonly untrusted emails, social networks regain the ability to propagate within consentient users, who willingly accept to partake. In this paper, we will demonstrate the effects of influential nodes against non-influential nodes through in simulated scenarios and provide an overview and analysis of the outcomes.

AMDD : Exploring entropy based anonymous multi-dimensional data detection for network optimization in human associated DTNs

Human associated delay-tolerant networks (HDTNs) are new networks where mobile devices are associated with humans and demonstrate social-related communication characteristics. Most of recent works use real social trace file to analyse its social characteristics, however social-related data is sensitive and has concern of privacy issues. In this paper, we propose an anonymous method that anonymize the original data by coding to preserve individual's privacy. The Shannon entropy is applied to the anonymous data to keep rich useful social characteristics for network optimization, e.g. routing optimization. We use an existing MIT reality dataset and Infocom 06 dataset, which are human associated mobile network trace files, to simulate our method. The results of our simulations show that this method can make data anonymously while achieving network optimization.

Trust evaluation for indirectly connected nodes using link community information

Inspired by recent empirical research on link communities, we borrow some important ideas and concepts for our own research to provide a more reasonable computation model of transitive trust. The key advantages of using link community methodology is to reflect on the nature of the social network features of Hierarchy and Overlap. Our research mainly resolves the computation of trust transitivity in which two nodes do not have any direct links to any link community. In this research, we discover a new direction analyzing trust transitivity. By using a social network game, we found that the link community methodology is a natural way to analyze trust in social networks. We also discovered, even in a small social network, trust has certain community features.

Web malware that targets web applications

Web applications have steadily increased, making them very important in areas, such as financial sectors, e-commerce, e-government, social media network, medical data, e-business, academic an activities, e-banking, e-shopping, e-mail. However, web application pages support users interacting with the data stored in their website to insert, delete and modify content by making a web site their own space. Unfortunately, these activities attracted writers of malicious software for financial gain, and to take advantage of such activities to perform their malicious objectives. This chapter focuses on severe threats to web applications specifically on Structure Query Language Injection Attack (SQLIA) and Zeus threats. These threats could adopt new obfuscation techniques to evade and thwart countermeasures Intrusion Detection Systems (IDS). Furthermore, this work explores and discusses the techniques to detect and prevent web application malwar.

Models, services and security in modern online social networks

Modern online social networks have revolutionized the world the same way the radio and the plane did, crossing geographical and time boundaries, not without problems, more can be learned, they can still change our world and that their true worth is still a question for the future.

The more pieces, the better the puzzle: sperm concentration increases gametic compatibility

The genetic benefits individuals receive from mate choice have been the focus of numerous studies, with several showing support for both intrinsic genetic benefits and compatibility effects on fertilization success and offspring viability. However, the robustness of these effects have rarely been tested across an ecologically relevant environmental gradient. In particular, sperm environment is a crucial factor determining fertilization success in many species, especially those with external fertilization. Here, we test the importance of sperm environment in mediating compatibility-based selection on fertilization using a factorial breeding design. We detected a significant intrinsic male effect on fertilization success at only one of four sperm concentrations. Compatibility effects were significant at the two highest sperm concentrations and, interestingly, the magnitude of the compatibility effect consistently increased with sperm concentration. This suggests that females are able to modify the probability of sperm-egg fusion as the amount of sperm available increases.