6,133 research outputs found

    Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review

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    Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader

    A Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure (TRACI)

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    Cybercrime against critical infrastructure such as nuclear reactors, power plants, and dams has been increasing in frequency and severity. Recent literature regarding these types of attacks has been extensive but due to the sensitive nature of this field, there is very little empirical data. We address these issues by integrating Routine Activity Theory and Rational Choice Theory, and we create a classification tool called TRACI (Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure). We take a Design Science Research approach to develop, evaluate, and refine the proposed artifact. We use mix methods to demonstrate that our taxonomy can successfully capture the characteristics of various cyberattacks against critical infrastructure. TRACI consists of three dimensions, and each dimension contains its own subdimensions. The first dimension comprises of hacker motivation, which can be financial, socio-cultural, thrill-seeking, and/or economic. The second dimension represents the assets such as cyber, physical, and/or cyber-physical components. The third dimension is related to threats, vulnerabilities, and controls that are fundamental to establishing and maintaining an information security posture and overall cyber resilience. Our work is among the first to utilize criminological theories and Design Science to create an empirically validated artifact for improving critical infrastructure risk management

    The Adirondack Chronology

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    The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp

    Guilt, Blame, and Oppression: A Feminist Philosophy of Scapegoating

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    In this dissertation I develop a philosophical theory of scapegoating that explains the role of blame-shifting and guilt avoidance in the endurance of oppression. I argue that scapegoating masks and justifies oppression by shifting unwarranted blame onto marginalized groups and away from systems of oppression and those who benefit from them, such that people in dominant positions are less inclined to notice or challenge its workings. I first identify a gap in our understanding of oppression, namely how oppression endures despite widespread formal commitments to principles of equality and justice. I argue that prominent theories of oppression do not place enough weight on the question of how oppression is justified and concealed from us through blame-shifting, in ways that enable its persistence without our explicit approval of its systems. Scapegoating, a concept as old as the Bible, offers potential insight into the means through which we shift blame and avoid responsibility. However, my survey of the genealogy of scapegoating shows that existing conceptualizations of scapegoating are limited in their scope and cannot be applied to explain the endurance of oppression. I propose an ameliorative theory of scapegoating that accounts for deficiencies in prevailing theories of both oppression and scapegoating. Distinct from interpersonal theories and psychologistic analyses of scapegoating, my theory characterizes scapegoating according to its social function in oppression, thereby explaining structural dimensions that are not already captured by other accounts.With the motivation and ingredients in place, I develop my theory of scapegoating as made up of three sub-mechanisms: essentialization of marginalized groups as blameworthy, collective interest in protection against a threat, and social exclusion of the blamed. These sub-mechanisms work together to construct certain groups as scapegoats and encourage us to treat them accordingly through various structural and interpersonal means. I argue that scapegoating has important implications for the formation of social identities; namely, scapegoating constructs social identities in an oppressive arrangement that is largely hidden from us but informs our social and affective relations. By constructing some identities as essentially blameworthy and threatening, dominantly situated identity groups are encouraged to internalize a protected status, act together in defense of their status, and maintain systems of oppressive exclusion. Finally, I elaborate the epistemic dimensions of my theory of scapegoating to argue that scapegoating functions within our social imaginaries and structural epistemic practices. In particular, I focus on the ways that ignorance functions to maintain the scapegoat mechanism, and how scapegoating helps insulate structural forms of ignorance. I end by considering the potential for resistance to scapegoating

    Church growth models and the early Quakers

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    This thesis explores numeric church growth generally, and the early Quaker movement specifically, within the paradigm of models. A wide variety of practitioner church-growth models have appeared since the 1890s. The emergence of Quakerism in the mid-1650s, however, has never before been explored as a model of church growth. This thesis argues that models are effective at theoretically conceptualizing church growth. Its results elevate the theoretical above the descriptive, providing generalizability. With this approach, several original contributions are made in developing a construct-based model framework and establishing a theoretical system of comparison to analyze both modern and primary texts. It is argued that the Quaker pattern of church growth, as outlined by George Fox in his journal, presents a uniquely provocative model, not elsewhere documented among church-growth model authors. Additionally, the model approach provides a systemic perspective of prior scholarship on the Quaker growth phenomenon
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