Jove
Visualize
Contact Us

Related Concept Videos

Inductive Reasoning00:59

Inductive Reasoning

59.6K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
59.6K
Deductive Reasoning01:16

Deductive Reasoning

54.7K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
54.7K
Reasoning01:30

Reasoning

37
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
37
Observational Learning01:12

Observational Learning

97
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
97
Naturalistic Observations02:30

Naturalistic Observations

15.3K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.3K
Reason and Intuition01:37

Reason and Intuition

6.3K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study.

Sensors (Basel, Switzerland)·2024
Same author

PRASH: A Framework for Privacy Risk Analysis of Smart Homes.

Sensors (Basel, Switzerland)·2021
See all related articles
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 Experiment Video

Updated: May 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.4K

Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation.

Kayode S Adewole1,2, Andreas Jacobsson1,2, Paul Davidsson1,2

  • 1Department of Computer Science and Media Technology, Malmö University, 205 06 Malmö, Sweden.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

This study presents an Intrusion Detection System (IDS) framework for Internet of Things (IoT) security. XGBoost demonstrated superior performance in detecting intrusions, offering a transparent and trustworthy solution.

Keywords:
IDSensemble modelsexplainabilityfeature selectionintrusion detectionmachine learningrule inductionsecurity

More Related Videos

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Related Experiment Videos

Last Updated: May 10, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.4K
A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

5.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • The rapid expansion of Internet of Things (IoT) devices introduces significant security and privacy challenges due to their resource constraints and diverse nature.
  • Vulnerabilities in IoT systems can be exploited by attackers through various threats like spoofing and denial-of-service attacks.
  • Intrusion Detection Systems (IDS) are crucial for monitoring network traffic and detecting security breaches in IoT environments.

Purpose of the Study:

  • To develop and evaluate an IDS framework integrating ensemble learning with rule induction for enhanced IoT security.
  • To assess the performance of five ensemble learning algorithms for effective intrusion detection in IoT networks.
  • To provide a transparent and explainable IDS solution for better decision-making by stakeholders.

Main Methods:

  • Implemented an IDS framework utilizing ensemble learning algorithms: Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost.
  • Evaluated the performance of these algorithms on two public datasets: CIC-IDS2017 and CICIoT2023.
  • Integrated a rule induction method to enhance the explainability of the developed IDS models.

Main Results:

  • XGBoost significantly outperformed other ensemble algorithms in intrusion detection accuracy and AUC-ROC.
  • On the CIC-IDS2017 dataset, XGBoost achieved 99.91% accuracy and 99.88% AUC-ROC.
  • On the CICIoT2023 dataset, XGBoost achieved 98.54% accuracy and 93.06% AUC-ROC, demonstrating robust performance.

Conclusions:

  • The proposed IDS framework effectively enhances IoT security through superior intrusion detection capabilities.
  • The integration of rule induction provides a lightweight, transparent, and trustworthy IDS.
  • The system supports security analysts and stakeholders in making informed decisions regarding intrusion events.