Social Aspects of Food-Sensitive Adults

People living with food-related illnesses find themselves subjugated by commonly held ideologies causing awkwardness in social situations. The current study is a qualitative analysis addressing how people with celiac disease (CD) navigate social situations in light of dominant beliefs that influence behaviors. Initially, I identify macro-level patriarchal, religious, sexist, ableist and etiquette-related commensality ideologies that disadvantage those with CD. Drawing from the communication narrative sense making (CNSM) theory that supports storytelling and memorable messages as a sense-making tool for individuals diagnosed with chronic illness and their family members, this work highlights retrospective stories and memorable messages from 20 randomly selected interviews (out of 66 conducted). Further, I discuss how individual identity evolves while redefining truths in light of having a disease. Three overarching themes emerge from the analysis: 1) questioning ideologies to form revised truths, 2) familial adaptation or non-adaptive responses, and 3) identity transformation. The first theme contemplates what is considered true depending on dominant ideologies on food-related expectations. The second theme examines social stigma that can result when a person in a given social group no longer conforms to these basic, assumed beliefs; or conversely, familial compassion that occurs when family and friends do conform. Finally, the third theme traces the evolution of an individual\u27s transformation when faced with redefining his or her identity, standing with courage and fortitude to influence those around him/her to align with new revised truths that may yield compliance or resistance. This study expands the current knowledge by associating how those with food sensitivities (FS) or CD find themselves subjugated by dominant ideologies that permeate behavior. The dissertation adds to the communication studies conversation by illuminating a seldom-studied population of adults living with the hidden disability of FS or CD, and expands the CNSM by contributing a concept I am calling a homeostatic shift, or the process where rituals are disrupted, causing the person with CD to enter into a state of liminality or transition, reforming truths and eventually shifting to a new state of equilibrium living with the realization that all experiences thereafter are shrouded with the veil of disease

The orchestra conductor

In musical and social representations, the orchestra conductor is portrayed as a figure of autocratic power, one whose authority is recognized and accepted by all. In reality, however, this authority is a social construct which is created over the course of the rehearsals. Moreover, it is highly dependent on the type of legitimacy held by the conductor, i.e., contractual legitimacy, which remains minimal, or professional legitimacy, which is based on the instrumentalists’ approval and recognition. This article attempts to understand which criteria allow this professional legitimacy to be established, for only this can allow the musicians to truly embrace the conductor’s interpretation. The first part of this article presents our ethnographic study of three symphonic orchestras. The second part distinguishes between contractual legitimacy and professional legitimacy and then explores the process by which the latter is constructed in orchestra-conductor interactions

Manage and Evaluate the Performance of the End-to-End 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

The 3G Partnership Project (3GPP) defined network slicing as a set of resources that could be scaled up and down to cover users' requirements. Machine learning and network slicing will be used together to manage and optimize the resources efficiently. In this research, a set of slices is implemented over the 5G networks to provide an efficient service to the end-user using softwarization and virtualization technologies. In the proposed prototype, the end-user connected to more than eight inter and intra-slices according to the demands. Traffic is generated over multiple scenarios then End-to-End slicing was analyzed after generating real-time traffic over the 5G networks and the features extracted from the traffic based on the flow behaviors. A set of elements selected from the datasets according to machine learning behaviours. From the first and second datasets, only five out of seven features will be selected. Then, seven out of nine features will be selected from the third dataset. Machine learning is applied to our datasets using MATLAB. After that, the best model is chosen to train and predict the slices in less CPU usage and less training time to reduce the computational power in future networks to build a sustainable environment. Furthermore, regression application is used to predict the slice type on the third dataset with the minimum squared error

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

The 3G Partnership Project (3GPP) defined network slicing as a set of resources that could be scaled up and down to cover users’ requirements. Machine learning and network slicing will be used together to manage and optimize resources efficiently. Sharing resources across multiple operators, such as towers, spectrum and infrastructure, can reduce the cost of 5G resources. In the proposed prototype, the end-user is connected to more than eight inter and intra-slices according to the demands. A set of slices is implemented over the 5G networks to provide an efficient service to the end-user using softwarization and virtualization technologies. Traffic is generated over multiple scenarios then End-to-End slicing traffic was analyzed after generating realtime traffic over the 5G networks. Also, all the features extracted from the traffic based on the flow behaviours and a set of elements selected from the datasets according to machine learning behaviours. Multiple machine learning algorithms are applied to our datasets using MATLAB classification application. After that, the best model is chosen to train and predict the slices using less CPU and training time to reduce the computational power in future networks and build a sustainable environment. Furthermore, the regression application predicts the slice type on the third dataset with the minimum squared error

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Mitigating Stealthy Link Flooding DDoS Attacks Using SDN-Based Moving Target Defense

With the increasing diversity and complication of Distributed Denial-of-Service (DDoS) attacks, it has become extremely challenging to design a fully protected network. For instance, recently, a new type of attack called Stealthy Link Flooding Attack (SLFA) has been shown to cause critical network disconnection problems, where the attacker targets the communication links in the surrounding area of a server. The existing defense mechanisms for this type of attack are based on the detection of some unusual traffic patterns; however, this might be too late as some severe damage might already be done. These mechanisms also do not consider countermeasures during the reconnaissance phase of these attacks. Over the last few years, moving target defense (MTD) has received increasing attention from the research community. The idea is based on frequently changing the network configurations to make it much more difficult for the attackers to attack the network. In this dissertation, we investigate several novel frameworks based on MTD to defend against contemporary DDoS attacks. Specifically, we first introduce MTD against the data phase of SLFA, where the bots are sending data packets to target links. In this framework, we mitigate the traffic if the bandwidth of communication links exceeds the given threshold, and experimentally show that our method significantly alleviates the congestion. As a second work, we propose a framework that considers the reconnaissance phase of SLFA, where the attacker strives to discover critical communication links. We create virtual networks to deceive the attacker and provide forensic features. In our third work, we consider the legitimate network reconnaissance requests while keeping the attacker confused. To this end, we integrate cloud technologies as overlay networks to our system. We demonstrate that the developed mechanism preserves the security of the network information with negligible delays. Finally, we address the problem of identifying and potentially engaging with the attacker. We model the interaction between attackers and defenders into a game and derive a defense mechanism based on the equilibria of the game. We show that game-based mechanisms could provide similar protection against SLFAs like the extensive periodic MTD solution with significantly reduced overhead. The frameworks in this dissertation were verified with extensive experiments as well as with the theoretical analysis. The research in this dissertation has yielded several novel defense mechanisms that provide comprehensive protection against SLFA. Besides, we have shown that they can be integrated conveniently and efficiently to the current network infrastructure