Deep learning trends and future perspectives of web security and vulnerabilities

The chapter discusses how ethics and transparency relate to creating secure web models for AI. AI plays a role in secure web development, and the authors consider ethics and transparency as two critical aspects of this subject, which influences users, stakeholders, or society. The examination begins with AI principles, which include fairness, accountability, and privacy requirements. They then get into the problems with secure web models. In this chapter, they break down bias and fairness concerns at the source and find ways to resolve them in web models. This relates to trust and accountability, where transparency and explainability are highlighted. They also provide case studies showing the effectiveness of transparent and explainable AI in increasing user engagement. They also delve into decision-making frameworks to help navigate the ethical dilemmas in AI web development. It then represents the conversation on the atmospherics of empowerment tools, such as monitoring and evaluation guidelines for mobilisation implementation practice and governance. To sum up, the authors underline the ethical and transparent views for us to do AI-driven web development. Therefore, they urge all stakeholders to make ethics and transparency the cornerstones of responsible webs.

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AI and Machine Learning in E-Commerce Security

Girimurugan,

Kumaresan,

Nair

et al. 2024

Advances in Web Technologies and Engineering

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With the continuous rise of e-commerce and its integration into many aspects of daily life, advanced methods of protection have grown dearer. AI and machine learning technologies are on the rise, allowing businesses in the e-commerce field to improve their security protocols and processes for securing customer and sensitive corporate data. Over the next decade or so, AI and machine learning will change the face of e-commerce security in multiple positive ways. Also, AI-driven chatbots are one of the trends in e-commerce security. E-commerce transactions can also benefit from risk assessment and mitigation, thanks to the availability of machine learning algorithms. Through analysis of the data and history of transactions in the past, it is possible to detect risks and demand more authentication for high-risk types of transactions. This chapter covers the technologies analysis on large amount of data to identify patterns and anomalies, enabling real-time fraud detection and enhanced user authentication. Emerging trends include the use of AI-driven chatbots for secure customer interactions, ML algorithms for personalized security measures, and predictive analytics to anticipate and mitigate potential threats. Together, AI and ML are setting new standards for safeguarding e-commerce platforms, ensuring a safer shopping experience for consumers.

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Deep learning trends and future perspectives of web security and vulnerabilities

Cited by 4 publications

References 147 publications

Object detection under the lens of privacy: A critical survey of methods, challenges, and future directions

Object detection under the lens of privacy: A critical survey of methods, challenges, and future directions

Ethics and Transparency in Secure Web Model Generation

AI and Machine Learning in E-Commerce Security

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