Artificial intelligence (AI) is fast changing civilian and military practice. Although the discussions of AI ethics often address issues of bias, fairness, and/or autonomous weapons, this paper suggests that the epistemic domain is the most urgent problem. Algorithmic manipulation and disinformation, as a kind of epistemic war, account to new ways of war discourses in the context of AI-enabled societies, where the possibility of responsible agency is put at risk. The main argument is that epistemic responsibility has to take priority over technological control in both ethics and policy. The paper shall then propose a new policy orientation that is based on principle of
epistemic jus in bello
: the principle of knowledge-based justness in warfare. This truth is that the integrity of knowledge environments is ethically no lesser urgent than defending civilian lives during kinetic conflict. By combining a formalized measure of epistemic integrity – via the Epistemic Integrity Index (EII) – with this normative framework, the paper bridges philosophy, ethics, and policy, offering a new model for understanding and regulating AI’s role in the epistemic dimension of modern conflict.
Abasianie Samuel Etuk, B. Stephen, Emmanuel Udoh et al.· E3S Web of Conferences· 0 citations
Cloud service providers face significant challenges in preventing hardware and software failures due to the large-scale and heterogeneous nature of cloud computing. Although many studies have focused on characterising failed jobs, fewer have explored proactive failure prediction. This paper presents a deep learning-based failure prediction model that integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to identify job failures before they occur. The proposed model improves the performance of cloud computing applications by reducing job failures and optimising resource utilisation. Using the Google Cluster Traces dataset, we analyse failure patterns and evaluate the effectiveness of the model across multiple performance metrics. The results demonstrate the robustness of the proposed scheme, achieving an accuracy of 99.96%, along with a high F1-score of 99.92% when compared to existing models. These findings highlight the potential of deep learning in proactive failure mitigation, providing a foundation for future advances in cloud workload reliability.
Wunukhen Shehu Awudu, P. Asuquo, B. Agbor et al.· E3S Web of Conferences· 0 citations
A comprehensive evaluation of deep learning architectures for DDoS attack detection in AMI environments, focusing on Convolutional Neural Network, CNN-Long Short-Term Memory (CNN-LSTM), and CNN-Gated Recurrent Unit (CNN-GRU) hybrid approaches.
S. Bassey, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al.· E3S Web of Conferences· 0 citations