Abstract
Given the centralized architecture of cloud computing, there is a genuine concern about its ability to adequately cope with the demands of connecting devices which are sharply increasing in number and capacity. This has led to the emergence of edge computing technologies, including but not limited to mobile edge-clouds. As a branch of Peer-to-Peer (P2P) networks, mobile edge-clouds inherits disturbing security concerns which have not been adequately addressed in previous methods. P2P security systems have featured many trust-based methods owing to their suitability and cost advantage, but these approaches still lack in a number of ways. They mostly focus on protecting client nodes from malicious service providers, but downplay the security of service provider nodes, thereby creating potential loopholes for bandwidth attack. Similarly, trust bootstrapping is often via default scores, or based on heuristics that does not reflect the identity of a newcomer. This work has patched these inherent loopholes and improved fairness among participating peers. The use cases of mobile edge-clouds have been particularly considered and a scalable reputation based security mechanism was derived to suit them. BitTorrent protocol was modified to form a suitable test bed, using Peersim simulator. The proposed method was compared to some related methods in the literature through detailed simulations. Results show that the new method can foster trust and significantly improve network security, in comparison to previous similar systems.

Abstract
Peer-to-Peer (P2P) overlay networks have gained popularity due to their robustness, cost advantage, network efficiency and openness. Unfortunately, the same properties that foster their success, also make them prone to several attacks. To mitigate these attacks, several scalable security mechanisms which are based on the concepts of trust and reputation have been proposed. These proposed methods tend to ignore some core practical requirements that are essential to make them more useful in the real world. Some of such requirements include efficient bootstrapping of each newcomer's reputation, and mitigating seeder(s) exploitation. Additionally, although interaction among participating peers is usually the bases for reputation, the importance given to the frequency of interaction between the peers is often minimized or ignored. This can result in situations where barely known peers end-up having similar trust scores to the well-known and consistently cooperative nodes. After a careful review of the literature, this work proposes a novel and scalable reputation based security mechanism that addresses the aforementioned problems. The new method offers more efficient reputation bootstrapping, mitigation of bandwidth attack and better management of interaction rate, which further leads to improved fairness. To evaluate its performance, the new reputation model has been implemented as an extension of the BitTorrent protocol. Its robustness was tested by exposing it to popular malicious behaviors in a series of extensive PeerSim simulations. Results show that the proposed method is very robust and can efficiently mitigate popular attacks on P2P overlay networks.

Abstract
Through IoT, humans and objects can be connected seamlessly, to guaranty improved quality of service (QoS). IoT-driven e-Health systems benefit from such rich network setting, to transmit health information and deliver health services. It is expected to grow massively in scale, but for that to happen, several issues need to be addressed, including security and trust. Edge computing paradigms, such as Fog computing and Cloudlet, are already popular in IoT based e-Health domain. Fog nodes are leveraged to reduce latency between IoT devices and remote cloud computing infrastructure. In this work, we explain how Mobile edge-clouds, which is a less popular edge computing paradigm, can be employed to achieve similar or lower latency, at a lower cost. We also propose a lightweight mechanism for security and fairness in e-Health protocols that are based on mobile edge-clouds and other paradigms. Detailed simulation experiments show that the proposed method is scalable and can efficiently mitigate attacks that are targeted at e-Health information and the network.

Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state-of-the-art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.

Abstract
Counting subgraph occurrences is a hard but very important task in complex network analysis, with applications in concepts such as network motifs or graphlet degree distributions. In this paper we present a novel approach for this task that takes advantage of knowing that a large fraction of subgraph types does not appear at all on real-world networks. We describe a pattern-growth methodology that is able to iteratively build subgraph patterns that do not contain smaller non-occurring subgraphs, significantly pruning the search space. By using the g-trie data structure, we are able to efficiently only count those subgraphs that we are interested in, reducing the total computation time. The obtained experimental results are very promising allowing us to avoid the computation of up to 99.78% of all possible subgraph patterns. This showcases the potential of this approach and paves the way for reaching previously unattainable subgraph sizes. © Springer Nature Switzerland AG 2019.

Abstract