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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.6K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.6K
High-Performance Liquid Chromatography: Types of Detectors01:15

High-Performance Liquid Chromatography: Types of Detectors

1.2K
The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
1.2K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

2.5K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Fed-DTCN: A Federated Disentangled Learning Framework for Unsupervised Zero-Day Anomaly Detection in IoT with Semantic-Aware Augmentation.

Sensors (Basel, Switzerland)·2026
Same author

Explainable CNN for brain tumor detection and classification through XAI based key features identification.

Brain informatics·2025
Same author

Correction: Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

PloS one·2024
Same author

Trust-Based Optimized Reporting for Detection and Prevention of Black Hole Attacks in Low-Power and Lossy Green IoT Networks.

Sensors (Basel, Switzerland)·2024
Same author

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

PloS one·2023
Same author

Insights into the Value of Lyso-Gb1 as a Predictive Biomarker in Treatment-Naïve Patients with Gaucher Disease Type 1 in the LYSO-PROOF Study.

Diagnostics (Basel, Switzerland)·2023

Related Experiment Video

Updated: Nov 9, 2025

Detection of Phytophthora capsici in Irrigation Water using Loop-Mediated Isothermal Amplification
07:25

Detection of Phytophthora capsici in Irrigation Water using Loop-Mediated Isothermal Amplification

Published on: June 25, 2020

6.7K

Detection of malicious consumer interest packet with dynamic threshold values.

Adnan Mahmood Qureshi1, Nadeem Anjum1, Rao Naveed Bin Rais2

  • 1Computer Science, Capital University of Science and Technology, Islamabad, Pakistan.

Peerj. Computer Science
|April 9, 2021
PubMed
Summary

This study enhances Named Data Networking (NDN) security by introducing a mechanism to combat content poisoning attacks. It effectively detects and blocks malicious nodes exploiting interest flooding, improving network robustness.

Keywords:
Content poisoning attacksDynamic thresholdMalicious consumer interest packetMitigation techniquesNamed data networking

More Related Videos

Author Spotlight: Advancing Rapid Detection of Respiratory Pathogens Using Microfluidic Chip
06:11

Author Spotlight: Advancing Rapid Detection of Respiratory Pathogens Using Microfluidic Chip

Published on: March 29, 2024

2.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.5K

Related Experiment Videos

Last Updated: Nov 9, 2025

Detection of Phytophthora capsici in Irrigation Water using Loop-Mediated Isothermal Amplification
07:25

Detection of Phytophthora capsici in Irrigation Water using Loop-Mediated Isothermal Amplification

Published on: June 25, 2020

6.7K
Author Spotlight: Advancing Rapid Detection of Respiratory Pathogens Using Microfluidic Chip
06:11

Author Spotlight: Advancing Rapid Detection of Respiratory Pathogens Using Microfluidic Chip

Published on: March 29, 2024

2.1K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.5K

Area of Science:

  • Computer Science
  • Network Security

Background:

  • Named Data Networking (NDN) offers efficient content retrieval via name-based routing and in-network caching.
  • Existing NDN research primarily focuses on optimizing caching and routing, neglecting security vulnerabilities.
  • Content Poisoning Attacks (CPA) threaten NDN by corrupting caches and isolating content.

Purpose of the Study:

  • To address the emerging threat of interest flooding attacks in NDN.
  • To enhance existing CPA mitigation schemes for improved security and robustness.
  • To propose a novel security mechanism for NDN.

Main Methods:

  • Introduced a security mechanism augmenting Name-Key Based Forwarding and Multipath Forwarding Based Inband Probe.
  • Monitored Cache-Miss Ratio and Queue Capacity at Edge Routers to detect malicious nodes.
  • Dynamically adjusted the cache-miss ratio threshold based on network conditions.

Main Results:

  • Successfully mitigated CPA vulnerabilities by detecting and blocking flooding interfaces.
  • Demonstrated effective defense against interest flooding attacks.
  • Achieved mitigation with minimal verification overhead at NDN routers.

Conclusions:

  • The proposed security mechanism enhances the effectiveness and robustness of CPA mitigation in NDN.
  • Monitoring edge router metrics provides an efficient way to identify and block malicious consumers.
  • This approach offers a practical solution to a critical NDN security challenge.