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Heuristics01:21

Heuristics

160
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection.

Saif S Kareem1, Reham R Mostafa1, Fatma A Hashim2

  • 1Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel feature selection method, GTO-BSA, to enhance artificial intelligence-based intrusion detection systems for Internet of Things (IoT) security. The new method improves classification accuracy and convergence rates for detecting cyberattacks.

Keywords:
Bird Swarm AlgorithmGorilla Troops OptimizerInternet of Things (IoT)feature selectionintrusion detection systemmachine learning

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Data Science

Background:

  • The proliferation of Internet of Things (IoT) applications generates vast amounts of data, increasing security vulnerabilities.
  • Current security measures and intrusion detection systems (IDS) struggle to cope with sophisticated cyberattacks in IoT environments.
  • Effective feature selection (FS) is crucial for optimizing machine learning algorithms in IDS, improving accuracy and efficiency.

Purpose of the Study:

  • To develop a novel and effective feature selection (FS) method to enhance the performance of artificial intelligence (AI)-based intrusion detection systems (IDS) for Internet of Things (IoT) security.
  • To improve the classification accuracy and convergence speed of AI algorithms used in IoT security by optimizing the feature selection process.
  • To introduce a hybrid optimization approach, GTO-BSA, by integrating the Gorilla Troops Optimizer (GTO) with the Bird Swarm Algorithm (BSA) for superior feature selection.

Main Methods:

  • A new feature selection (FS) method, GTO-BSA, was developed by enhancing the Gorilla Troops Optimizer (GTO) using the Bird Swarm Algorithm (BSA).
  • The GTO-BSA method was designed to improve the exploitation capabilities of GTO, leading to better identification of optimal solutions and enhanced convergence.
  • The performance of the GTO-BSA method was rigorously evaluated on four diverse IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15, and BoT-IoT.

Main Results:

  • The proposed GTO-BSA feature selection method demonstrated a superior convergence rate compared to the original GTO and BSA algorithms.
  • Experiments showed that GTO-BSA achieved higher-quality solutions in feature selection, leading to improved performance metrics for IoT intrusion detection.
  • Comparative analysis against state-of-the-art techniques confirmed the effectiveness and efficiency of the GTO-BSA approach on multiple benchmark datasets.

Conclusions:

  • The GTO-BSA feature selection method offers a significant advancement in AI-driven IoT security, particularly for intrusion detection systems.
  • The hybrid GTO-BSA approach effectively addresses the challenges of feature selection, leading to enhanced accuracy and faster convergence in cyberattack detection.
  • This research provides a promising solution for improving the robustness and performance of security systems in the rapidly expanding landscape of Internet of Things applications.