Related Experiment Video
Updated: Jul 5, 2025
06:37
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
Published on: December 15, 2023
3.8K
Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things.
Ashfaq Hussain Farooqi1, Shahzaib Akhtar1, Hameedur Rahman1
1Faculty of Computing and AI, Air University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|January 11, 2024
Summary
A novel DRX ensemble voting classifier improves Internet of Everything security for 6G networks. This machine learning approach significantly enhances intrusion detection accuracy and reduces false positives across multiple datasets.
Area of Science:
- Cybersecurity
- Machine Learning
- Network Security
Background:
- The Internet of Everything (IoE) in 6G networks expands connectivity, increasing security risks from botnets and other attacks.
- Protecting IoT-enabled metaverse connections requires robust security measures to detect network anomalies.
Purpose of the Study:
- To propose a novel classification technique for enhanced network intrusion detection.
- To improve the accuracy and precision of security systems in the context of rapidly expanding IoT connectivity.
Main Methods:
- Developed a DRX ensemble voting classifier combining Decision Tree, Random Forest, and XGBoost algorithms.
- Evaluated the proposed technique using benchmark datasets: NSL-KDD, UNSW-NB15, and CIC-IDS2017.
Main Results:
- Achieved high accuracy rates: 99.88% (NSL-KDD), 99.93% (UNSW-NB15), and 99.98% (CIC-IDS2017).
- Significantly reduced false positive rates to 0.003%, 0.001%, and 0.00012% across the datasets.
- Demonstrated superior performance compared to other existing methods.
Conclusions:
- The DRX-based ensemble voting classifier is highly effective for network intrusion detection in IoE environments.
- The proposed method offers a robust solution for safeguarding 6G networks against emerging cyber threats.
Related Concept Videos
01:26Classification of Systems-I
188
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
188
01:31Classification of Systems-II
146
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
146
01:30Classification of Signals
466
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
466
01:29Aggregates Classification
326
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326
01:22Force Classification
1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
01:25Distribution Reliability and Automation
107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107