Peer-to-Peer Networking and Applications

Software-defined networking (SDN) is earning popularity in enterprise network for simplifying network management service and reducing operational cost. However, security enhancement is required for concerns. In this paper, we analyze the network attack patterns of governments and enterprises, whose networking paradigm are constructed in SDN. In detail, methods of time series data mining including clustering and forecasting are proposed to discover hidden information in temporal network attack data. To start with, hierarchical clustering with modified dynamic time warping distance measure was developed to classify time series data of nine departments of China, which is aimed to identify patterns of network attack. We then explored autoregressive integrated moving average to build a model describing relationships and behavior of network attack as well as forecast the frequency of the future network attack, which is targeted to prevent extensive exposure of attack events. Experiments demonstrated that our models have the ability to distinguish the complex phenomena of temporal network attack and realize statistically accurate predication of network attack under SDN architecture. Our work provides the foundation for decision-making when dealing with issues of network safety.

Recently, a new peer-assisted architecture to build content delivery systems has been presented. This architecture is based on the use of the storage capacity of end-users’ set-top boxes (STBs), connected in a peer-to-peer (P2P) manner in order to help the content servers in the delivery process. In these systems, the contents are usually split into a set of smaller pieces, called sub-streams, which are randomly injected at the STBs. The present paper is focused on Video on Demand (VoD) streaming and it is assumed that the STB-based content delivery system is deployed over the global Internet, where the clients are distributed over different ISP networks. In this scenario, three different strategies are studied for increasing the percentage of data uploaded by peers, in order to offload the content servers as much as possible. First of all, a new mechanism is presented which determines which sub-stream has to be placed at which STB by a Non-Linear Programming (NLP) formulation. A different strategy for reducing the content server load is to take advantage of the available bandwidth in the different ISP networks. In this sense, two new mechanisms for forwarding the VoD requests to different ISP networks are presented. Finally, the present paper also shows that in some situations the available uplink bandwidth is associated with STBs that do not have the required sub-streams. Regarding this concern, a new mechanism has been designed that dynamically re-allocates some streams, which are being transmitted from specific STBs, to different STBs, in order to find the necessary resources to start new streaming sessions.

These days Internet of Things (IoT), which consists of smart objects such as sensor nodes is the most important technology for providing intelligent services. In the IoT ecosystem, wireless sensor networks deliver collected information from IoT devices to a server via sink nodes, and IoT services are provided by peer-to-peer (P2P) networking between the server and the IoT devices. Particularly, IoT applications with wide service area requires the mobile sink nodes to cover the service area. To employ mobile sink nodes, the network adopts delay-tolerant capability by which delay-tolerant nodes try to transmit data when they connect to the mobile sink node in the application service field. However, if the connection status between a IoT device and a mobile sink node is not good, the efficiency of data forwarding will be decreased. In addition, retransmission in bad connection cause high energy consumption for data transmission. Therefore, data forwarding in the delay-tolerant based services needs to take the connection status into account. The proposed method predicts the connection status using naïve Bayesian classifier and determines whether the delay tolerant node transmits data to the mobile sink node or not. Furthermore, the efficiency of the proposed method was validated through extensive computer simulations.

A popular data acquisition technique for Wireless Sensor Networks (WSNs) is usage of static sink. However, this results in hot-spot or sink-hole problem as the sensor nodes near the sink die as they disseminate the data of the entire network to the sink node. In this work, in order to alleviate these problems, mobile sink (MS) is used. However, designing an optimal trajectory for mobile sink traversal is a complex problem. Further, instead of constrained sensor nodes, relay nodes (RNs) are used to cluster the data sensed. These RNs are deployed using the proposed meta-heuristic Differential Moth Flame Optimization (DMFO) algorithm. Also, a traversal strategy for the MS is proposed in order to collect the sensed data. The proposed strategy is an improvement to most of the existing strategies that use Traveling Salesman Problem (TSP) solver with exponential complexity for sink movement. Extensive simulations are carried out and the results are analyzed for various network scenarios over different performance metrics.

The increasing availability and decreasing cost of mobile devices equipped with WiFi radios has led to increasing demand for multimedia applications in both professional and entertainment contexts. The streaming of multimedia however requires strict adherence to QoS levels in order to guarantee suitable quality for end users. MANETs lack the centralised control, coordination and infrastructure of wireless networks as well as presenting a further element of complexity in the form of device mobility. Such constraints make achieving suitable QoS a nontrivial challenge and much work has already been presented in this area. This paper proposes a bottom-up routing protocol which specifically takes into account mobility and other unique characteristics of MANETs in order to improve QoS for multimedia streaming. Geographic Predictive Routing (GPR) uses Artificial Neural Networks to accurately predict the future locations of neighbouring devices for making location and mobility-aware routing decisions. GPR is intended as the first step towards creating a fully QoS-aware networking framework for enhancing the performance of multimedia streaming in MANETs. Simulation results comparing GPR against well-established ad-hoc routing protocols such as AODV and DSR show that GPR is able to achieve an improved level of QoS in a variety of multimedia and mobility scenarios.

Social Networks (SNs) connect nodes from different geographical areas, keeping users updated about current affairs through message sharing. Natural calamities or deliberately imposed actions can cause Internet disconnections between geographical areas. This results in a SN partition which leads to communication loss between nodes inside the partitioned area. In this paper, we propose an extended Multi-Dimensional Routing (eMDR) algorithm using Greedy routing, which considers multiple attributes for routing. It improves the communication efficiency inside partitioned SNs. The performance of the proposed algorithm is validated by considering three dimensions/attributes, viz., social interest, geographical location and time-zones of social nodes on both real and synthetic SN datasets. The results of topological and routing probabilities for Chord and novel Social Interest Overlay networks, show considerable improvement in communication inside partitioned SNs.

Traditional healthcare services have transitioned into modern healthcare services where doctors remotely diagnose the patients. Cloud computing plays a significant role in this change by providing easy access to patients’ medical records to all stakeholders, such as doctors, nurses, patients, life insurance agents, etc. Cloud services are scalable, cost-effective, and offer a broad range of mobile access to patients’ electronic health record (EHR). Despite the cloud’s enormous benefits like real-time data access, patients’ EHR security and privacy are major concerns. Since the information about patients’ health is highly sensitive and crucial, sharing it over the unsecured wireless medium brings many security challenges such as eavesdropping, modifications, etc. Considering the security needs of remote healthcare, this paper proposes a robust and lightweight, secure access scheme for cloud-based E-healthcare services. The proposed scheme addresses the potential threats to E-healthcare by providing a secure interface to stakeholders and prohibiting unauthorized users from accessing information stored in the cloud. The scheme makes use of multiple keys formed through the key derivation function (KDF) to ensure end-to-end ciphering of information for preventing misuse. The rights to access the cloud services are provided based on the identity and the association between stakeholders, thus ensuring privacy. Due to its simplicity and robustness, the proposed scheme is the best fit for protecting data security and privacy in cloud-based E-healthcare services.

Peer-to-Peer (P2P) traffic is widely used for the purpose of streaming media, file-sharing, instant messaging, games, software etc., which often involves copyrighted data. From the past decade, P2P traffic has been contributing to major portion of Internet traffic which is still rising and hence is consuming a lot of network traffic bandwidth. It also worsens congestion of network traffic significantly and degrades the performance of traditional client–server applications. Popularity of various P2P applications has led Internet Service Providers (ISPs) to face various challenges regarding efficiently and fairly utilizing network resources. The traditional methods of identifying P2P traffic such as port-based and payload-based are proving ineffective due to their significant limitations and can be bypassed. Hence, new approaches based on statistics or behaviour of network traffic needs to be developed and adopted in order to accurately identify existing and new P2P traffic which emerge over the time. This article presents a survey regarding various strategies involved in identifying P2P traffic. Furthermore, conceptual analysis of network traffic measurement and monitoring is also presented.

The rapid evolution of cloud computing technology has enabled seamless connection of devices on a broad spectrum. Also, it enables storage of massive quantity of data in the form of data centers. This suggests a shared pool of resources where users situated all over the world have access to the aforementioned data centers. Such a framework has cyber-security based challenges where it becomes vulnerable to external attacks. There arises a need for an Intrusion Detection System (IDS) to prevent the system from unwanted and malicious attacks. However, the existing IDS have not been able to efficiently combinehigh accuracy with low complexity and time efficiency. In order to overcome these challenges an Enhanced Intrusion Detection System via Agent Clustering and Classification based on Outlier Detection (EIDS-ACC-OD) is proposed. At first, preprocessing is performed to remove unwanted spaces using outlier detection. Then modified K-means clustering algorithm is developed for data segmentation. Further, K-Nearest Neighbor (KNN) is utilized for categorization of the attacks.