Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.3K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
5.3K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

941
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
941
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.1K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

522
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
522
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

380
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
380

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems.

Journal of analytical science and technology·2026
Same author

What Country, University, or Research Institute, Performed the Best on Covid-19 During the First Wave of the Pandemic?: Bibliometric analysis of scientific literature - analysing a 'snapshot in time' of the first wave of COVID-19.

Annals of data science·2024
Same author

Super-forecasting the 'technological singularity' risks from artificial intelligence.

Evolving systems·2023
Same author

Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0.

Health and technology·2023
Same author

Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2).

Health and technology·2022
Same author

New and emerging forms of data and technologies: literature and bibliometric review.

Multimedia tools and applications·2022
Same journal

Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".

Applied intelligence (Dordrecht, Netherlands)·2026
Same journal

AI-driven 5G IoT e-nose for whiskey classification.

Applied intelligence (Dordrecht, Netherlands)·2025
Same journal

DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis.

Applied intelligence (Dordrecht, Netherlands)·2025
Same journal

ROCIP: robust continuous inertial position tracking for complex actions emerging from the interaction of human actors and environment.

Applied intelligence (Dordrecht, Netherlands)·2025
Same journal

RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia.

Applied intelligence (Dordrecht, Netherlands)·2024
Same journal

Temporally extended goal recognition in fully observable non-deterministic domain models: Temporally extended goal recognition in FOND planning.

Applied intelligence (Dordrecht, Netherlands)·2024
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity
11:19

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

Published on: March 7, 2016

Collaborative penetration testing suite for emerging generative AI algorithms.

Petar Radanliev1,2,3

  • 1Department of Computer Science, University of Oxford, Wolfson Building, Parks Rd, OX1 3QG Oxford, England.

Applied Intelligence (Dordrecht, Netherlands)
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel penetration testing suite to secure generative AI against quantum threats. It effectively identifies and remediates vulnerabilities, enhancing overall system security.

Keywords:
Advanced persistent threatsBlockchain-Enhanced loggingDynamic application security testingGenerative artificial intelligence securityQuantum-Resistant cryptography

More Related Videos

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Related Experiment Videos

Last Updated: Jun 18, 2026

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity
11:19

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

Published on: March 7, 2016

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Area of Science:

  • Cybersecurity
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Generative AI systems face significant cyber threats and quantum computing challenges.
  • Existing security measures are insufficient against advanced adversarial and quantum-assisted attacks.

Purpose of the Study:

  • To propose and evaluate a new penetration testing suite for securing generative AI against quantum security concerns.
  • To integrate advanced security testing methodologies and quantum-resistant protocols.

Main Methods:

  • Integration of Dynamic and Static Application Security Testing (DAST & SAST) using OWASP ZAP, Burp Suite, SonarQube, and Fortify.
  • Real-time monitoring via Interactive Application Security Testing (IAST) with Contrast Assess.
  • Blockchain-enhanced logging with Hyperledger Fabric for tamper-proof records.
  • Implementation of quantum-resistant cryptographic protocols (lattice-based, RLWE).
  • AI-driven red team simulations for adversarial and quantum-assisted attack emulation.

Main Results:

  • Identification and remediation of over 300 vulnerabilities, with a 70% reduction in high-severity issues within two weeks.
  • 90% resolution efficiency for vulnerabilities logged via blockchain.
  • Demonstrated resilience of quantum-resistant protocols against simulated quantum attacks.
  • Successful secure API encryption and data transmission.

Conclusions:

  • The developed penetration testing suite establishes a new protocol for securing generative AI systems.
  • The integrated approach effectively addresses both traditional and quantum-related security vulnerabilities.
  • The findings highlight the importance of proactive and advanced security measures in the era of AI and quantum computing.