Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbours without any knowledge about the underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces a proximity-aware extension to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, which is based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In the MNBC protocol , physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for maintaining clusters based on physical Internet proximity. Furthermore, a reputation-based blockchain protocol is integrated with MNBC protocol in order to securely assign a master node for every cluster. We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay.

The development of Web 2.0 has remarkably increased in today's world.These development has also been a reason for the increment of online social networks (OSNs).Web 2.0 is the roof of the online social networks since online social networks are built on Web 2.0.Users are given an environment in which they can communicate with others without considering other users locations.The way of communication in OSNs is done via sharing various contents of data, such as photos, texts, and videos.Sharing data sometimes cause privacy problems in OSNs, especially in the case that the content involves different users information on itself.Users are notified after the content is shared and they are allowed to remove tags.The content is still available in OSNs platforms, users, therefore, find a way to punish other users with being unfriend, or they quit from OSNs.However, both cases are contradictory with the main of OSNs.By considering the above issues, we develop a framework in which users' opinions are taken on data sharing process and based on the final decision, which is taken by the user who posts the content, punishing or rewarding technique is used.We also evaluate the proposed work with users interactions.

This paper proposes a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to evaluate the security and performance of grouping nodes based on physical proximity. In MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for propagating based on physical internet proximity.

In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic in both the industrial and academic sectors. Software-defined networking offers numerous benefits against legacy networking systems by simplifying the process of network management through reducing the cost of network configurations. Currently, data plane fault management is limited to two mechanisms: proactive and reactive. These fault management and recovery techniques are activated only after a failure occurrence and hence packet loss is highly likely to occur. This is due to convergence time where new network paths will need to be allocated in order to forward the affected traffic rather than drop it. Such convergence leads to temporary service disruption and unavailability. Practically, not only the speed of recovery mechanisms affects the convergence, but also the delay caused by the process of failure detection. In this paper, we define a new approach for data plane fault management in software-defined networks where the goal is to eliminate the convergence process altogether rather than accelerate the failure detection and recovery. We propose a new framework, called Smart Routing, which allows the network controller to receive forewarning signs on failures and hence avoid risky paths before the failure incidents occur. The proposed approach aims to decrease service disruption, which in turn increases network service availability. We validate our framework through a set of experiments that demonstrate how the underlying model runs and its impact on improving service availability. We take as example of the applicability of the new framework three types of topologies covering real and simulated networks.

Online Social Networks (OSNs) have become one of the most popular implement for interacting with people all over the world and sharing data with them.These data sometimes may be a co-owned data which involves multiple users, sharing co-owned data can cause privacy violation if co-owners are not happy with the owner's sharing privacy settings.To tackle privacy issues on co-owned data, collaborative privacy management has become a popular research area in recent years.In this work, we provide a fuzzy logic decision based collaborative privacy management framework for OSNs.We use data sensitivity value and confidence value in targeted group as input variables of fuzzy system.We also use trust values between users since our framework needs to calculate trust loss and gains for reputation value.

Existing Side-channel attack techniques, such as meltdown attacks, show that attackers can exploit the microarchitecture and OS vulnerabilities to achieve their goals. In this paper, we present the development of our real-time system for detecting side-channel attacks. Unlike previous works, our proposed detection system does not rely on synchronisation between the attackers and victims. Instead, it uses processors' performance indicators to capture malicious Flush+ Reload activities with an accuracy of up to 99%. Moreover, the detection activities can be achieved with minimum time delay in both native and cloud systems with a low overhead performance of approximately less than 1% in the host system.

This paper explains how the commonly occurring DOS and Brute Force attacks on computer networks can be efficiently detected and network performance improved, which reduces costs and time. Therefore, network administrators attempt to instantly diagnose any network issues. The experimental work used the SNMP-MIB parameter datasets, which are collected via a specialised MIB dataset consisting of seven types of attack as noted in section three. To resolves such issues, this researched carried out several important contributions which are related to fault management concerns in computer network systems. A central task in the detection of the attacks relies on MIB feature behaviours using the suggested SFCM method. It was concluded that the DOS and Brute Force fault detection results for three different clustering methods demonstrated that the proposed SFCM detected every data point in the related group. Consequently, the FPC approached 1.0, its highest record, and an improved performance solution better than the EM methods and K-means are based on SNMP-MIB variables.

In the Bitcoin system, a peer-to-peer electronic currency system, the delay overhead in transaction verification prevents the Bitcoin from gaining increasing popularity nowadays as it makes the system vulnerable to double spend attacks. This paper introduces a proximity-aware extension to the current Bitcoin protocol, named Bitcoin Clustering Based Ping Time protocol (BCBPT). The ultimate purpose of the proposed protocol, that is based on how the clusters are formulated and the nodes define their membership, is to improve the transaction propagation delay in the Bitcoin network. In BCBPT, the proximity of connectivity in the Bitcoin network is increased by grouping Bitcoin nodes based on ping latencies between nodes. We show, through simulations, that the proximity base ping latency defines better clustering structures that optimize the performance of the transaction propagation delay. The reduction of the communication link cost measured by the information propagation time between nodes is mainly considered as a key reason for this improvement. Bitcoin Clustering Based Ping Time protocol is more effective at reducing the transaction propagation delay compared to the existing clustering protocol (LBC) that we proposed in our previous work.

The delay overhead of transaction verification in Bitcoin, a peer-to-peer electronic currency system, is a complicated issue which makes the system vulnerable to double spend attacks. In this paper, we propose a new approach, that is based on how the clusters are formulated and the nodes define their membership, to improve the transaction propagation delay in the Bitcoin network. In this approach, the locality of connectivity in the Bitcoin network is increased by grouping Bitcoin nodes based on their geographical location. We show, through simulations, that location based-distance better defines clustering structures that optimize the performance of the transaction propagation delay. A key reason behind this improvement is mainly due to the reduction of the communication link cost measured by the distance between nodes. Compared to the existing clustering protocol (BCBSN) that we proposed in our previous work, location based clustering is more effective at reducing the transaction propagation delay.

This paper deals with the prevention of security issues on mobile agents in IP Networks.We propose a new security computing model based on trusted server to avert Eavesdropping and Alternation attacks.The new protocol will be implemented using IBM mobile agent platform, Aglet.The new framework consists of components that provide support to the mobile agent while it is touring hosts in the agent space.It also protects the confidentiality and integrity of parts of the mobile agent.We conduct performance analysis over different types of mobile agents over a real IP Traces under malicious actions.

Fat tree topologies have been extensively used as interconnection networks for high performance computing, cluster and data center systems, with their most recent variants able to fairly extend and scale to accommodate higher processing power. While each progressive and evolved fat-tree topology includes some extra advancements, these networks do not fully address all the issues of large scale HPC. We propose a topology called Zoned-Fat tree (Z-Fat tree,) which is a further extension to the fat trees. The extension relates to the provision of extra degree of connectivity to utilize the extra ports per switches (routing nodes), that are, in some cases, not utilized by the architectural constraints of other variants of fat trees, and hence increases the bisection bandwidth, reduces the latency and supplies additional paths for fault tolerance. To support and profit from the extra links, we propose an adaptive low latency routing for up traffic which is based on a series of leading direction bits predefined at the source; furthermore we suggest a deterministic routing by implementing a dynamic round robin algorithm that overtakes D-mod-K in same cases and guarantees the utilization of all the extra links. We also propose a fault tolerance algorithm, named recoil-and-reroute which makes use of the extra links to ensure higher message delivery even in the presence of faulty links and switches.

The aim of classification in machine learning is to utilize knowledge gained from applying learning algorithms on a given data so as determine what class an unlabelled data having same pattern belongs to. However, algorithms do not learn properly when a massive difference in size between data classes exist. This classification problem exists in many real world application domains and has been a popular area of focus by machine learning and data mining researchers. The class imbalance problem is further made complex with the presence of associative data difficult factors. The duo have proven to greatly deteriorate classification performance. This paper introduces a two-phased data level approach for binary classes which entails the temporary re-labelling of classes. The proposed approach takes advantage of the local neighbourhood of the minority instances to identify and treat difficult examples belonging to both classes. Its outcome was satisfactory when compared against various data-level methods using datasets extracted from KEEL and UCI datasets repository.