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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.5K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.3K
Problem Solving: Dimensional Analysis01:08

Problem Solving: Dimensional Analysis

5.9K
Every mathematical equation that connects separate distinct physical quantities must be dimensionally consistent, which implies it must abide by two rules. For this reason, the concept of dimension is crucial. The first rule is that an equation's expressions on either side of an equality must have the exact same dimension, i.e., quantities of the same dimension can be added or removed. The second rule stipulates that all popular mathematical functions, such as exponential, logarithmic, and...
5.9K
Dimensional Analysis01:23

Dimensional Analysis

2.0K
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
2.0K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

492
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
492

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Related Experiment Videos

Enhanced intrusion detection in cybersecurity through dimensionality reduction and explainable artificial

Hayam Alamro1, Sultan Alahmari2, Nadhem Nemri3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports
|September 30, 2025
PubMed
Summary

This study introduces an Enhanced Intrusion Detection in Cybersecurity through Dimensionality Reduction and Explainable Artificial Intelligence with Attention Mechanism in Deep Learning (EIDCDR-XAIADL) model. The novel approach significantly improves cybersecurity threat detection accuracy using explainable AI and deep learning techniques.

Keywords:
Antlion optimizationCybersecurityDeep learningExplainable artificial intelligenceIntrusion detection system

Related Experiment Videos

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cyber threats are increasingly complex, utilizing evasion techniques to bypass Intrusion Detection Systems (IDSs).
  • Artificial Intelligence (AI) in cybersecurity lacks transparency and interpretability, hindering trust and error identification.
  • Explainable AI (XAI) offers a solution by making AI decisions understandable for cybersecurity experts.

Purpose of the Study:

  • To propose an Enhanced Intrusion Detection in Cybersecurity through Dimensionality Reduction and Explainable Artificial Intelligence with Attention Mechanism in Deep Learning (EIDCDR-XAIADL) model.
  • To develop a robust cybersecurity system that integrates XAI for improved threat detection and decision-making.
  • To address the challenge of recognizing obfuscated malware and enhancing the transparency of AI-driven security.

Main Methods:

  • Data normalization using mean normalization and feature selection via Multiverse Optimization (MVO).
  • Hybrid deep learning model combining Convolutional Neural Network (CNN), Bi-directional Gated Recurrent Unit (BiGRU), and Attention Mechanism (CNN-BiGRU-AM) for attack classification.
  • Hyperparameter optimization using Antlion Optimization (ALO) and explainability provided by Shapley Additive Explanations (SHAP).

Main Results:

  • The EIDCDR-XAIADL model achieved superior accuracy rates of 99.19% on the NSLKDD dataset and 99.12% on the CICIDS 2017 dataset.
  • The integration of XAI (SHAP) provided trustworthy insights, enhancing threat detection and expert decision-making.
  • Dimensionality reduction and optimized deep learning models contributed to effective intrusion detection.

Conclusions:

  • The proposed EIDCDR-XAIADL model demonstrates significant effectiveness in enhancing cybersecurity intrusion detection.
  • Explainable AI integration is crucial for building trust and understanding in AI-powered cybersecurity solutions.
  • The study highlights the potential of combining advanced machine learning, dimensionality reduction, and XAI for robust cyber defense.