Improving Fairness in Network Traffic by Controlling Congestion and Unresponsive Flows

Azath, M.; Banu, R. S. D. Wahida; Madheswari, A. Neela

doi:10.1007/978-3-642-22540-6_34

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…However, the discussion on the social justice framework needs to become an integral part of the self-driving vehicle technology development. Conclusively, the design perspective requires ethical reflections, as some of the engineering fields have already recognized [66][67][68][69][70], along with similar consideration for transportation planning practices [71,72].…”

Section: Development Of Social Justice Frameworkmentioning

confidence: 99%

Development of socially sustainable traffic-control principles for self-driving vehicles: The ethics of anthropocentric design

Mladenović

Abbas

McPherson

2014

2014 IEEE International Symposium on Ethics in Science, Technology and Engineering

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Converging effect of communication, sensing, and in-vehicle computing technology has ensured potential to develop large-scale deployment of self-driving vehicles. Considering the potential impact of this technology, the approach for development cannot overlook needs regarding sustainability and social considerations. This paper argues that control technology for self-driving vehicles has both direct and indirect effect on fundamental human rights, and that the anthropocentric design perspective is a necessary ethical approach. Furthermore, we present current perspectives on operational principles, and relevant theoretical and empirical social implications. We conclude that there is potential for development of traffic-control principles for self-driving vehicles on the basis of mutuallyadvantageous cooperative production. Finally, we present several important areas for further investigation.

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Section: Development Of Social Justice Frameworkmentioning

confidence: 99%

Development of socially sustainable traffic-control principles for self-driving vehicles: The ethics of anthropocentric design

Mladenović

Abbas

McPherson

2014

2014 IEEE International Symposium on Ethics in Science, Technology and Engineering

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“…As part of a continuously evolving perception of engineering ethics (Schmidt 2014;Smith et al 2014), many engineering fields have already recognized the importance of ethics and social justice in designing new technologies (Grodzinsky 2000;Curiel--Esparza et al 2004;Beamon 2005;Kleijnen 2011;Azath et al 2011;Harvey 2010). From the perspective of social justice, the development of C2 presents enormous opportunities, but also two important dangers.…”

Section: The Relevance Of Social Justice For Traffic Controlmentioning

confidence: 99%

Engineering Social Justice into Traffic Control for Self-Driving Vehicles?

Mladenović

McPherson

2015

Sci Eng Ethics

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The convergence of computing, sensing, and communication technology will soon permit large-scale deployment of self-driving vehicles. This will in turn permit a radical transformation of traffic control technology. This paper makes a case for the importance of addressing questions of social justice in this transformation, and sketches a preliminary framework for doing so. We explain how new forms of traffic control technology have potential implications for several dimensions of social justice, including safety, sustainability, privacy, efficiency, and equal access. Our central focus is on efficiency and equal access as desiderata for traffic control design. We explain the limitations of conventional traffic control in meeting these desiderata, and sketch a preliminary vision for a next-generation traffic control tailored to address better the demands of social justice. One component of this vision is cooperative, hierarchically distributed self-organization among vehicles. Another component of this vision is a priority system enabling selection of priority levels by the user for each vehicle trip in the network, based on the supporting structure of non-monetary credits.

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“…The state-of-art cloud-based IDS are not capable of identifying the abnormal activities that take place in the cloud environment and they are mainly focused on increasing the accuracy of the system rather than providing real-time solutions by analyzing the significant complexities. [20][21][22][23][24][25][26][27][28][29][30][31] This article presents a novel malicious detection and prediction system using two stages. Initially, a data preprocessing and detection system is built in the first stage.…”

Section: Introductionmentioning

confidence: 99%

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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Cloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

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Improving Fairness in Network Traffic by Controlling Congestion and Unresponsive Flows

Cited by 11 publications

References 5 publications

Development of socially sustainable traffic-control principles for self-driving vehicles: The ethics of anthropocentric design

Development of socially sustainable traffic-control principles for self-driving vehicles: The ethics of anthropocentric design

Engineering Social Justice into Traffic Control for Self-Driving Vehicles?

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

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