Scalable Explainable Ai For Cloud-Based Data Science And Analytics

Abstract

The intersection of Cloud computing and generative artificial intelligence (AI) can be a game changer for business 
intelligence (BI), particularly when it comes to enhancing predictive analytics capabilities at scale. In this paper, we 
propose an integrated framework that exploits the elasticity of cloud infrastructure along with the creative 
problemsolving and data synthesis capabilities of generative AI models. Using generative AI in the cloud empowers 
organizations to access and apply dynamic data modeling, automated pattern discovery, and real-time forecasting 
across large, distributed datasets. This study initiation of a scalable architecture for predictive analytics which is 
costeffective, provides accuracy, and designed to abstract the complexities of the underlying models from business 
stakeholders enabling smooth business decisions. The model's adaptability across industries with variable volume of 
data and analytical needs is demonstrated with case studies and simulations. These results reinforce that this 
integrated point of view greatly enhances performance, agility, and cost-savings in leading-edge BI environments. 
Therefore, detection of malicious intrusions forms an important part of an integrated approach to network security. 
In this work, we start by considering the problem of cooperative intrusion detection in WSNs where the nodes are 
equipped with local detector modules and have to identify the intruder in a distributed fashion. We develop a 
lightweight ID system, called Lidia, which follows an intelligent agent-based architecture. In Lidia, nodes overhear 
their neighboring nodes and collaborate with each other in order to successfully detect an intrusion. We show how 
such a system can be implemented, which components and interfaces are needed, and what is the resulting overhead 
imposed. We then expand this ID framework with algorithms that incorporate both classes of intrusion detection 
techniques, i.e., misuse detection and anomaly detection. We investigate in depth some of the most severe routing 
attacks against sensor networks, namely the sinkhole and wormhole attacks, and we emphasize on strategies that an 
attacker can follow to successfully launch them. Then we propose novel localized countermeasures that can make 
legitimate nodes become aware of the threat, while the attack is still taking place. Detailed theoretical analysis and 
simulation results confirm that the proposed algorithms can always thwart these kinds of attacks. Also, by providing 
an implementation on real sensor devices, we demonstrate their practicality and efficiency in terms of memory 
requirements and processing overhead.