Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented.

Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.

Generative AI is transforming the art and science of web engineering by automating content generation, design and development through models like GAN, VAE or Transformers. These models can produce authentic text like a writer and other media, including images, so developers would save more time in web development and improve creativity and scalability. From automated web design and content personalization to adaptive user interface or code optimization, the uses of generative AI could be limitless. Integrating AI with web engineering allows developers to generate dynamic and tailored user interface applications that adapt to the unique requirements of each user. Nevertheless, issues like data quality, bias and ethical considerations require ongoing attention to ensure responsible AI usage. Overall, generative AI can revolutionize web development for a future of innovation and more intelligent, efficient, user-centric web applications.

The use of artificial intelligence (AI) in cybersecurity is a need due to the outpacing of conventional security measures by the fast growth of cyber threats. Exploring the function of machine learning (ML), deep learning (DL), and natural language processing (NLP) in recogniz- ing and reacting to new cyber dangers, this chapter offers a thorough introduction to AI-powered threat identification. The paper contrasts AI- based systems with more traditional approaches, demonstrating how AI can better identify unknown assaults, adapt to new threats, and decrease the number of false positives. It touches on topics such as training models using industry-specific data, ensuring they can commu- nicate with one other, and keeping them up-to-date and monitored. Data privacy, legislation compliance, and best practices for AI threat detection are also discussed. We discuss the need for human analysts to work with AI tools and how to overcome obstacles, including AI bias, complicated systems, and ethical considerations.