Yongzhao Zhan

An Investigation of Security Trends in Personal Wireless Networks

Wireless networks are an integral part of day-to-day life for many people, with businesses and home users relying on them for connectivity and communication. This paper examines the problems relating to the topic of wireless security and the background literature. Following this, primary research has been undertaken that focuses on the current trend of wireless security. Previous work is used to create a timeline of encryption usage and helps to exhibit the differences between 2009 and 2012. Moreover, a novel 802.11 denial-of-service device has been created to demonstrate the way in which it is possible to design a new threat based on current technologies and equipment that is freely available. The findings are then used to produce recommendations that present the most appropriate countermeasures to the threats found.

A semi-supervised incremental learning method based on adaptive probabilistic hypergraph for video semantic detection

Semantic categorization for the complex videos is an ambiguous task. The semi-supervised learning method based on hypergraph model can achieve multi-semantics labels, but a hypergraph model is sensitive to the radius parameter when it is constructed and the number of vertices belonging to a hyperedge is fixed. In this paper, a semi-supervised incremental learning method based on adaptive probabilistic hypergraph for video semantic detection is presented. In the probabilistic hypergraph modeling, a formula is presented as a measurement to adaptively decide whether a vertex is belonged to a hyperedge or not. The model has high robustness and can overcome the defect of fixed number of vertices belonging to the same hyperedge in the traditional probabilistic hypergraph model. In the semi-supervised incremental learning process, a threshold is defined, which is used to judge whether unlabeled sample can be added into the modeling, in order that the model learning result for unlabeled samples has high certainty. The experimental results show that our method can improve the model generalization ability and utilize the unlabeled samples effectively. In the aspects of recall rate and precision rate for semantic video concept detection from complex videos, our proposed method outperforms the compared methods.

A Socioecological Model for Advanced Service Discovery in Machine-to-Machine Communication Networks

The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.

Achieving Green IT Using VDI in Cyber Physical Society

With rapid advances in Internet technologies and increasing popularity of cyber social networks, the physical world and cyber world are gradually merging to form a new cyber-socio-physical society known as the Cyber Physical Society (CPS). In contrast to the previous research studies in cyber physical society, we are focusing on a different case of CPS-green IT in this paper. The complex cyber physical systems of cloud bring unprecedented challenges in power resource managements. This paper looks at the literature behind virtualization and mainly virtual desktop infrastructure as the solution to these challenges. In this paper, we investigate how to use cutting-edge virtualization technologies to reduce power consumption of IT infrastructure in Cyber Physical Society. This research and the implementation of a testing virtual desktop environment using VMware and Wyse technologies portray the clear improvements that hypervisor and desktop virtualization can be brought to IT infrastructures drawing particular attention to power consumption and the green incentives.

An Investigation into the Evolution of Security Usage in Home Wireless Networks

Wireless networks are an integral part of life for many residential properties. The use of laptops and smartphones has lead to a large increase in the number of wireless networks. The research within this project revealed how residential users were securing their networks, this research mirrored a previous investigation into wireless security from 2009, as well as a comparison with other authors work dating back to 2006 and 2007. These findings were then analyzed and reasons behind the positive trend in encryption utilization were examined.

Moments in Time: A Forensic View of Twitter

Online social networks have become a ubiquitous and important part of the modern, developed society. Modifications in social conduct within these often forced closed spaces are noted, these side effects well documented. The potential for evidence gathering and research into changing human behaviour and social norms is undoubted, but the networks pose significant problems to digital forensic investigators that are not experienced offline. Data will reside on multiples of servers in multiple countries, across multiple jurisdictions. Capturing it before it is overwritten or deleted is a known problem, mirrored in other cloud based services. This paper will examine the background to social networking sites, potential behaviour sets experienced on-line and the application of current law and forensic methodologies. This paper will then take a view of the social networking site Twitter by adopting a formal approach to the examination, extraction and analysis of data. This paper will identify a known problem, status removal and offer a novel, yet simple method for determining what has been removed.

A Video Semantic Analysis Method Based on Kernel Discriminative Sparse Representation and Weighted KNN

To improve the classification performance of sparse representation features, a method of video semantic analysis based on kernel discriminative sparse representation and weighted KNN is proposed in this paper. A discriminative model is built by introducing kernel category function to KSVD dictionary optimization algorithm, mapping the sparse representation features into high-dimensional space. Then the optimal dictionary is generated and applied to compute the sparse representation coefficients of video features. Finally, the video semantic analysis is made by means of weighed KNN method based on optimization sparse representation. Before the video semantic analysis, genetic algorithm is used to get global optimal features and reduce the dimension. Furthermore, the kernel function is introduced to establish discrimination about sparse representation features and the classification vote result is weighed, the purpose of which is to improve the accuracy and rationality of video semantic analysis. The experimental results show that the proposed method significantly improves the discrimination of sparse representation features and is 22.33% higher in accuracy compared with the traditional SVM method based on KSVD. The method is suitable for the classification of video features with nonlinear relationship, tolerating not only the noise but also interference problems in video shot.

A Semi-supervised Incremental Learning Algorithm Based on Auto-adaptive Probabilistic Hypergraph and Its Application for Video Semantic Detection

Semantic categorization of complex videos is an ambiguous task. The semi-supervised learning method, which is based on hyper graph model, can achieve multi-semantics labels, but it is sensitive to the radius parameter when a hyper graph model is constructed and the number of vertices belonging to a hyper edge is fixed. A new method is proposed in this paper to construct an auto-adaptive probabilistic hyper graph (ada-PHGraph) model, where a formula is presented as a measurement to auto-adaptively decide whether a vertex is belonged to a hyper edge or not. Our proposed algorithm has high robustness and can overcome the defect of fixed number of vertices belonging to the same hyper edge in the traditional probabilistic hyper graph model. In addition, a pre-defined threshold is used to judge whether the model learning result for unlabeled samples has high certainty and can been included in the model. The auto-adaptive probabilistic hyper graph model can achieve the dynamic updates effectively when the number of samples increases by applying the incremental learning mechanism. Our experimental results have shown that the auto-adaptive probabilistic hyper graph model can improve the model generalization ability and utilize the unlabeled samples effectively.

Performance evaluation and simulation of peer-to-peer protocols for Massively Multiplayer Online Games

Massively Multiplayer Online Games are networked games that allow a large number of people to play together. Classically MMOG worlds are hosted on many powerful servers and players that move around the world are passed from server to server as they pass through the environment. Running a large number of servers can be challenging and there are many considerations for a developer who wants to create a game to enter the MMOG market. If it is possible to use a P2P network to host an MMOG successfully, the costs of running a server farm are taken out of the equation. This will allow for groups with small budgets to enter the MMOG market and help competition in the market place. In this paper, the methods for the design of P2P massively multiplayer game protocols have been presented. Performance bottlenecks have been evaluated and highlighted by using simulations. The business viability has also been discussed in this paper.

Distributed SVM Classification with Redundant Data Removing

The biggest challenge faced by the distributed classification in wireless sensor networks (WSNs) is how to reduce the energy consumption in sensors for improving their service capacities. In this paper, an incremental Support Vector Machine (SVM) training method based on redundant data removing is proposed. Applying this method, distributed clustering is firstly performed on the data of sensors. Then boundaries are obtained to discriminate between clustered data and scattered data in clusters through Fisher Discriminant Ratio (FDR). The clustered data are regarded as redundant data and removed. Thus the number of data samples for training SVM is greatly reduced and then the computation is speed up in WSNs. Simulation results showed that the proposed method achieved goals of reducing energy consumption and keeping classification accuracies by decreasing time of training Support Vectors (SVs).