Self-adaptive systems offer several attack surfaces due to the communication via different channels and the different sensors required to observe the environment. Often, attacks cause safety to be compromised as well, making it necessary to consider these two aspects together. Furthermore, the approaches currently used for safety and security analysis do not sufficiently take into account the intermediate steps of an adaptation. Current work in this area ignores the fact that a self-adaptive system also reveals possible vulnerabilities (even if only temporarily) during the adaptation. To address this issue, we propose a modeling approach that takes into account the different relevant aspects of a system, its adaptation process, as well as safety hazards and security attacks. We present several models that describe different aspects of a self-adaptive system and we outline our idea of how these models can then be combined into an Attack-Fault Tree. This allows modeling aspects of the system on different levels of abstraction and co-evolve the models using transformations according to the adaptation of the system. Finally, analyses can then be performed as usual on the resulting Attack-Fault Tree.

Artificial intelligence as a service (AIaaS) refers to the use of pre-trained machine learning algorithms, robotic process automation (RPA) to natural language processing (NLP) in the cloud to automate business processes. In this respect, it is similar to software as a service (SaaS). However, AIaaS allows business users to access AI models without requiring advanced AI programming skills. This blog shares the anticipated growth figures of the AI business covering as per deployment model, end-user application, verticals, and geography. Building the own AI solution for these different small services will therefore not make any sense for companies because they are all associated with substantial upfront costs and management, which is sometimes not easy for the new joiners.

The "AI and Advance Machine Learning in BFSI Market By Component, Deployment Model, Enterprise Size and Application: Global Opportunity Analysis and Industry Forecast, 2021-2030" report has been added to ResearchAndMarkets.com's offering. Artificial intelligence (AI) in finance is transforming the BFSI industry, as AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. In addition, advanced machine learning technology is being used to help organizations to improve customer experience and to enhance their market share. Furthermore, it provides various solutions to the baking sector to replace routine manual work with automation and to increase productivity. In addition, AI and advanced machine learning help in reducing credit default frauds by monitoring transactions to detect suspicious transactions with compliance concerns.

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems emerge recently, and are expected to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack of comprehensive investigation. ECG recordings differ from images in the visualization, dynamic property and accessibility, thus, the existing image-targeted attack may not directly applicable. To fill this gap, this paper proposes ECGadv to explore the feasibility of adversarial attacks on arrhythmia classification system. We identify the main issues under two different deployment models(i.e., cloud-based and local-based) and propose effective attack schemes respectively. Our results demonstrate the blind spots of DNN-powered diagnosis system under adversarial attacks, which facilitates future researches on countermeasures.