Results 71 to 80 of about 804,777 (293)
Secure Fusion‐X harmonizes unstructured NVD descriptions with structured CVSS/CWE/CPE metadata via decision‐level fusion, overcoming the fragility of traditional unimodal models. Automated assessment of software vulnerability exploitability is essential for intelligent cyber defense, yet its effectiveness is often hindered by unstable, delayed, or ...
Mona Dolati +3 more
wiley +1 more source
Defense Management concepts improving Indonesian Maritime Security [PDF]
This study aimed to analyze the concept of Indonesian Defence Management in the 21st century in the context of Indonesian Maritime Security and to determine the readiness of defence management capabilities in facing threats.
Barnas, Rayanda +2 more
core +1 more source
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently
Warren He +4 more
openaire +4 more sources
Defense-VAE: A Fast and Accurate Defense Against Adversarial Attacks [PDF]
Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serious threat to their applications in security-sensitive systems.
Xiang Li 0080, Shihao Ji 0001
openaire +2 more sources
Playing in the Dark: Invisible Chess as a Laboratory for Strategic AI
This paper shows that strategic AI evaluated on perfect‐information benchmarks can be brittle in real adversarial settings. By using invisible chess as a benchmark for hidden state and deception, it argues for stricter testing, human oversight, and more cautious governance of high‐stakes AI systems.
Paolo Ciancarini
wiley +1 more source
Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model
Xinlei Liu +6 more
doaj +1 more source
You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models
Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources.
Renxuan Wang +3 more
doaj +1 more source
Defensive Dual Masking for Robust Adversarial Defense
Abstract Adversarial defenses for textual data have gained considerable attention in recent years due to the increasing vulnerability of Natural Language Processing (NLP) models to adversarial attacks. These attacks exploit subtle perturbations in input text to deceive models, posing significant challenges to model robustness and ...
Wangli Yang +3 more
openaire +3 more sources
Competitiveness to Support the National Defense System [PDF]
The purpose of this research is to identified competitiveness Indonesian Aerospace Inc. (IAe) to support the national defense system. This research is a descriptive with qualitative interview as method and using the theory of reference are: (1) Five ...
Setia, Adang, Purwowidagdo, Sapto J.
core +1 more source
Abstract Parents of children with special educational needs and disabilities (SEND) increasingly seek information and support through online discussions, yet little is known about how architectural features of digital platforms shape the interactions and support that parents encounter. The paper makes two contributions: First, it develops and applies a
Alan Shaw, Patricia A. Shaw
wiley +1 more source

