Results 1 to 10 of about 619 (184)

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, EarlyView.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor, Shtwai Alsubai, Monji Mohamed Zaidi, Natalia Kryvinska, Abdullah Al Hejaili, Sidra Abbas   +5 more
wiley   +1 more source

Infrared Adversarial Patch Generation Based on Reinforcement Learning

Mathematics
Recently, there has been an increasing concern about the vulnerability of infrared object detectors to adversarial attacks, where the object detector can be easily spoofed by adversarial samples with aggressive patches.
Shuangju Zhou, Yang Li, Wenyi Tan, Chenxing Zhao, Xin Zhou, Quan Pan   +5 more
doaj   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source

POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee, Jin-Seoung Kim, Han-Jin Lee, Seok-Hwan Choi   +3 more
doaj   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World

Journal of Cybersecurity and Privacy
The traditional process for learning patch-based adversarial attacks, conducted in the digital domain and later applied in the physical domain (e.g., via printed stickers), may suffer reduced performance due to adversarial patches’ limited ...
Dudi Biton, Jacob Shams, Satoru Koda, Asaf Shabtai, Yuval Elovici, Ben Nassi   +5 more
doaj   +1 more source

Major Cybersecurity Breaches: Shaping Corporate Cybersecurity Policies and Closing the Gaps

Journal of Corporate Accounting &Finance, EarlyView.
ABSTRACT As digitalization accelerates, cybercrime has intensified in both scale and impact over the past two decades. This study aims to critically examine major cybersecurity events, assess them through the lens of routine activity theory, examine insight from three other established criminological and organizational theories, and address central ...
Laura K. Rickett, Deborah Smith
wiley   +1 more source

“Just a Patch”: Imperceptible Image Patch Generation for Adversarial Inference

IEEE Access
Image classification models, based on deep neural networks, are vulnerable to adversarial input poisoning attacks where a maliciously crafted input results in incorrect predictions.
Debasmita Manna, Somanath Tripathy, Bruhadeshwar Bezawada, Indrakshi Ray   +3 more
doaj   +1 more source

Semantic Adversarial Attacks on Face Recognition Through Significant Attributes

International Journal of Computational Intelligence Systems, 2023
Face recognition systems are susceptible to adversarial attacks, where adversarial facial images are generated without awareness of the intrinsic attributes of the images in existing works. They change only a single attribute indiscriminately.
Yasmeen M. Khedr, Yifeng Xiong, Kun He
doaj   +1 more source

Why do we burn? Examining arguments underpinning the use of prescribed burning to manage wildfire risk

People and Nature, EarlyView.
Abstract Managing wildfire risk requires consideration of complex and uncertain scientific evidence as well as trade‐offs between different values and goals. Conflicting perspectives on what values and goals are most important, what ought to be done and what trade‐offs are acceptable complicate those decisions.
Pele J. Cannon, Sarah Clement
wiley   +1 more source