[HTML][HTML] Explainable Artificial Intelligence (XAI): Concepts and challenges in healthcare

T Hulsen - AI, 2023 - mdpi.com
Artificial Intelligence (AI) describes computer systems able to perform tasks that normally
require human intelligence, such as visual perception, speech recognition, decision-making …

[HTML][HTML] Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects

IH Sarker, H Janicke, A Mohsin, A Gill, L Maglaras - ICT Express, 2024 - Elsevier
Digital twins (DTs) are an emerging digitalization technology with a huge impact on today's
innovations in both industry and research. DTs can significantly enhance our society and …

A survey on explainable artificial intelligence for cybersecurity

G Rjoub, J Bentahar, OA Wahab… - … on Network and …, 2023 - ieeexplore.ieee.org
The “black-box” nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …

Accelerating SME growth in the African context: Harnessing FinTech, AI, and cybersecurity for economic prosperity

CC Okoye, EE Nwankwo, FO Usman… - International Journal of …, 2024 - ijsra.net
The economic landscape of Africa is evolving rapidly, and the role of Small and Medium
Enterprises (SMEs) is increasingly recognized as a key driver of sustainable development …

A survey on explainable artificial intelligence for network cybersecurity

G Rjoub, J Bentahar, OA Wahab, R Mizouni… - arXiv preprint arXiv …, 2023 - arxiv.org
The black-box nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …

XAI-IDS: Toward Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection Systems

O Arreche, T Guntur, M Abdallah - Applied Sciences, 2024 - mdpi.com
The exponential growth of network intrusions necessitates the development of advanced
artificial intelligence (AI) techniques for intrusion detection systems (IDSs). However, the …

AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification

Z Gong, L Shen, Y Zhang, LY Zhang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The collaborative nature of federated learning (FL) poses a major threat in the form of
manipulation of local training data and local updates, known as the Byzantine poisoning …

Privacy preserving machine unlearning for smart cities

K Chen, Y Huang, Y Wang, X Zhang, B Mi… - Annals of …, 2024 - Springer
Due to emerging concerns about public and private privacy issues in smart cities, many
countries and organizations are establishing laws and regulations (eg, GPDR) to protect the …

[HTML][HTML] A Novel Approach for Efficient Mitigation against the SIP-Based DRDoS Attack

IM Tas, S Baktir - Applied Sciences, 2023 - mdpi.com
Voice over Internet Protocol (VoIP) and its underlying Session Initiation Protocol (SIP) are
widely deployed technologies since they provide an efficient and fast means of both voice …

Decoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence

O Embarak - 2023 9th International Conference on Information …, 2023 - ieeexplore.ieee.org
This review explores the current state of Explainable Artificial Intelligence (XAI). This study
looks at current advances in XAI research, as well as challenges and the future. To …