Infection Curve Flattening via Targeted Interventions and Self-Isolation

Understanding the impact of network clustering and small-world properties on epidemic spread can be crucial in developing effective strategies for managing and controlling infectious diseases. Particularly in this work, we study the impact of these network features on targeted intervention (e.g., self-isolation and quarantine). The targeted individuals for self-isolation are based on centrality measures and node influence metrics. Compared to our previous works on scale-free networks, small-world networks are considered in this paper. Small-world networks resemble real-world social and human networks. In this type of network, most nodes are not directly connected but can be reached through a few intermediaries (known as the small-worldness property). Real social networks, such as friendship networks, also exhibit this small-worldness property, where most people are connected through a relatively small number of intermediaries. We particularly study the epidemic curve flattening by centrality-based interventions/isolation over small-world networks. Our results show that high clustering while having low small-worldness (higher shortest path characteristics) implies flatter infection curves. In reality, a flatter infection curve implies that the number of new cases of a disease is spread out over a longer period of time, rather than a sharp and sudden increase in cases (a peak in epidemic). In turn, this reduces the strain on healthcare resources and helps to relieve the healthcare services

Protection against Contagion in Complex Networks

In real-world complex networks, harmful spreads, commonly known as contagions, are common and can potentially lead to catastrophic events if uncontrolled. Some examples include pandemics, network attacks on crucial infrastructure systems, and the propagation of misinformation or radical ideas. Thus, it is critical to study the protective measures that inhibit or eliminate contagion in these networks. This is known as the network protection problem. The network protection problem investigates the most efficient graph manipulations (e.g., node and/or edge removal or addition) to protect a certain set of nodes known as critical nodes. There are two types of critical nodes: (1) predefined, based on their importance to the functionality of the network; (2) unknown, whose importance depends on their location in the network structure. For both of these groups and with no assumption on the contagion dynamics, I address three major shortcomings in the current network protection research: namely, scalability, imprecise evaluation metric, and assumption on global graph knowledge. First, to address the scalability issue, I show that local community information affects contagion paths through characteristic path length. The relationship between the two suggests that, instead of global network manipulations, we can disrupt the contagion paths by manipulating the local community of critical nodes. Next, I study network protection of predefined critical nodes against targeted contagion attacks with access to partial network information only. I propose the CoVerD protection algorithm that is fast and successfully increases the attacker’s effort for reaching the target nodes by 3 to 10 times compared to the next best-performing benchmark. Finally, I study the more sophisticated problem of protecting unknown critical nodes in the context of biological contagions, with partial and no knowledge of network structure. In the presence of partial network information, I show that strategies based on immediate neighborhood information give the best trade-off between performance and cost. In the presence of no network information, I propose a dynamic algorithm, ComMit, that works within a limited budget and enforces bursts of short-term restriction on small communities instead of long-term isolation of unaffected individuals. In comparison to baselines, ComMit reduces the peak of infection by 73% and shortens the duration of infection by 90%, even for persistent spreads

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

ENSURING SPECIFICATION COMPLIANCE, ROBUSTNESS, AND SECURITY OF WIRELESS NETWORK PROTOCOLS

Several newly emerged wireless technologies (e.g., Internet-of-Things, Bluetooth, NFC)—extensively backed by the tech industry—are being widely adopted and have resulted in a proliferation of diverse smart appliances and gadgets (e.g., smart thermostat, wearables, smartphones), which has ensuingly shaped our modern digital life. These technologies include several communication protocols that usually have stringent requirements stated in their specifications. Failing to comply with such requirements can result in incorrect behaviors, interoperability issues, or even security vulnerabilities. Moreover, lack of robustness of the protocol implementation to malicious attacks—exploiting subtle vulnerabilities in the implementation—mounted by the compromised nodes in an adversarial environment can limit the practical utility of the implementation by impairing the performance of the protocol and can even have detrimental effects on the availability of the network. Even having a compliant and robust implementation alone may not suffice in many cases because these technologies often expose new attack surfaces as well as new propagation vectors, which can be exploited by unprecedented malware and can quickly lead to an epidemic

Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks

Link Prediction (LP) in social networks (SN) is referred to as predicting the likelihood of a link formation in SNs in the near future. There are several types of SNs that are available such as human interaction network, biological network, protein-to-protein interaction network, and so on. Earlier LP researches used heuristics methods, including Common Neighbors, Resource Allocation, and many other similarity score methods. Even though heuristics methods perform better in some types of SNs, their performance is limited in other types of SNs. Finding the best heuristics for a given type of SN is a trial and error process. Recent state-of-the-art research, WLNM and SEAL showed that with deep learning techniques and subgraphing, the heuristics selection could be automated and increase the accuracy of LP. However, WLNM and SEAL have some limitations and still having performance lack in some types of SNs. The objective of this paper is to introduce a novel framework that overcomes the limitations of state-of-the-art methods and improves the accuracy of LP over various types of social networks. We propose a Link Prediction framework called PLACN that analyzes common neighbors based subgraphs using deep learning technique to predict links. PLACN is equipped with two new algorithms that are a subgraph extraction algorithm that efficiently extracts common neighbors of targeted nodes and a proposed new node labeling algorithm based on hop number and average path weight that creates consistent node orders over subgraphs. In addition to the algorithms, we derived a formula based on network properties to find an optimal number node for a given SN. PLACN converts the LP problem into an Image Classification problem and utilizes a Convolutional Neural Network to classify the links. We tested the proposed PLACN on seven different types of real-work networks and compared the performance against heuristics, latent methods, and state-of-the-art methods. Our results show that PLACN outperformed the compared Link Prediction methods while reaching above 96% AUC in tested benchmark social networks

Novel Analytical Modelling-based Simulation of Worm Propagation in Unstructured Peer-to-Peer Networks

Millions of users world-wide are sharing content using Peer-to-Peer (P2P) networks, such as Skype and Bit Torrent. While such new innovations undoubtedly bring benefits, there are nevertheless some associated threats. One of the main hazards is that P2P worms can penetrate the network, even from a single node and then spread rapidly. Understanding the propagation process of such worms has always been a challenge for researchers. Different techniques, such as simulations and analytical models, have been adopted in the literature. While simulations provide results for specific input parameter values, analytical models are rather more general and potentially cover the whole spectrum of given parameter values. Many attempts have been made to model the worm propagation process in P2P networks. However, the reported analytical models to-date have failed to cover the whole spectrum of all relevant parameters and have therefore resulted in high false-positives. This consequently affects the immunization and mitigation strategies that are adopted to cope with an outbreak of worms. The first key contribution of this thesis is the development of a susceptible, exposed, infectious, and Recovered (SEIR) analytical model for the worm propagation process in a P2P network, taking into account different factors such as the configuration diversity of nodes, user behaviour and the infection time-lag. These factors have not been considered in an integrated form previously and have been either ignored or partially addressed in state-of-the-art analytical models. Our proposed SEIR analytical model holistically integrates, for the first time, these key factors in order to capture a more realistic representation of the whole worm propagation process. The second key contribution is the extension of the proposed SEIR model to the mobile M-SEIR model by investigating and incorporating the role of node mobility, the size of the worm and the bandwidth of wireless links in the worm propagation process in mobile P2P networks. The model was designed to be flexible and applicable to both wired and wireless nodes. The third contribution is the exploitation of a promising modelling paradigm, Agent-based Modelling (ABM), in the P2P worm modelling context. Specifically, to exploit the synergies between ABM and P2P, an integrated ABM-Based worm propagation model has been built and trialled in this research for the first time. The introduced model combines the implementation of common, complex P2P protocols, such as Gnutella and GIA, along with the aforementioned analytical models. Moreover, a comparative evaluation between ABM and conventional modelling tools has been carried out, to demonstrate the key benefits of ease of real-time analysis and visualisation. As a fourth contribution, the research was further extended by utilizing the proposed SEIR model to examine and evaluate a real-world data set on one of the most recent worms, namely, the Conficker worm. Verification of the model was achieved using ABM and conventional tools and by then comparing the results on the same data set with those derived from developed benchmark models. Finally, the research concludes that the worm propagation process is to a great extent affected by different factors such as configuration diversity, user-behaviour, the infection time lag and the mobility of nodes. It was found that the infection propagation values derived from state-of-the-art mathematical models are hypothetical and do not actually reflect real-world values. In summary, our comparative research study has shown that infection propagation can be reduced due to the natural immunity against worms that can be provided by a holistic exploitation of the range of factors proposed in this work