[HTML][HTML] Adversarial machine learning in industry: A systematic literature review

FV Jedrzejewski, L Thode, J Fischbach, T Gorschek… - Computers & …, 2024‏ - Elsevier
Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …

Responsible-AI-by-design: A pattern collection for designing responsible artificial intelligence systems

Q Lu, L Zhu, X Xu, J Whittle - IEEE Software, 2023‏ - ieeexplore.ieee.org
Responsible artificial intelligence (AI) issues often occur at the system level, crosscutting
many system components and the entire software engineering lifecycle. We summarize …

“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice

G Apruzzese, HS Anderson, S Dambra… - … IEEE conference on …, 2023‏ - ieeexplore.ieee.org
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

[PDF][PDF] Adversarial machine learning

A Vassilev, A Oprea, A Fordyce, H Anderson - Gaithersburg, MD, 2024‏ - site.unibo.it
Abstract This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts
and defines terminology in the field of adversarial machine learning (AML). The taxonomy is …

An Ontology-Based Cybersecurity Framework for AI-Enabled Systems and Applications

D Preuveneers, W Joosen - Future Internet, 2024‏ - mdpi.com
Ontologies have the potential to play an important role in the cybersecurity landscape as
they are able to provide a structured and standardized way to semantically represent and …

The different faces of ai ethics across the world: A principle-to-practice gap analysis

LN Tidjon, F Khomh - IEEE Transactions on Artificial …, 2022‏ - ieeexplore.ieee.org
Artificial Intelligence (AI) is transforming our daily life with many applications in healthcare,
space exploration, banking, and finance. This rapid progress in AI has brought increasing …

A comparison of neural-network-based intrusion detection against signature-based detection in iot networks

M Schrötter, A Niemann, B Schnor - Information, 2024‏ - mdpi.com
Over the last few years, a plethora of papers presenting machine-learning-based
approaches for intrusion detection have been published. However, the majority of those …

METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities

S Hyun, M Guo, MA Babar - 2024 IEEE Conference on …, 2024‏ - ieeexplore.ieee.org
Large-Language Models (LLMs) have shifted the paradigm of natural language data
processing. However, their black-boxed and probabilistic characteristics can lead to …

Trustworthy ai-generative content in intelligent 6g network: Adversarial, privacy, and fairness

S Li, X Lin, Y Liu, J Li - arxiv preprint arxiv:2405.05930, 2024‏ - arxiv.org
AI-generated content (AIGC) models, represented by large language models (LLM), have
brought revolutionary changes to the content generation fields. The high-speed and …

Boosting credit risk models

B Baesens, K Smedts - The British Accounting Review, 2023‏ - Elsevier
In this article, we give various recommendations to boost the performance of credit risk
models. It is based upon more than two decades of research and consulting on the topic …