Titlebook: Introduction to Machine Learning with Security; Theory and Practice Pramod Gupta,Naresh Kumar Sehgal,John M. Acken Book 2025Latest edition

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Cloud Computing Conceptspportunities and additional security challenges. Former is driven by automation, to offer 24?×?7 monitoring with surveillance cameras, improving industrial productivity and scalability never seen before in the human history. However, addition of edge computing and IoT also expands the attack surface

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Hardware Based AI and MLy using hardware accelerators. These can be based on GPUs, FPGAs or ASIC based designs. In future, the need for AI training and inference can be combined in a single piece of hardware. This will reduce both the capital expenditure (CAPEX) and operational expenditure (OPEX) in a data-center. To summa

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Hardware Based Securitys control, and corporate policies. No single solution can solve all the information security problems. This chapter will concentrate on the hardware features, capabilities and components that support information security in the Cloud.

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Analytics in the Cloudractitioners working on real-life problems using Reinforcement learning. A key need is to develop the ability for enabling incremental training for neural network parameters, when the training dataset is constantly evolving. In this chapter, we will study analytics methods such as MapReduce, Hadoop

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Evolution and Risks of LLMsplications for routine writing tasks, information aggregation, summarization, preparing reports, image creating and logo design etc. This chapter examines the evolution of LLMs and Generative AI with some basic examples. However, the risks in terms of LLM output manipulation should be well understoo

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