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Atomic Fluorescence Spectroscopy01:29

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Atomic fluorescence spectroscopy (AFS) is an analytical technique that involves the electronic transitions of atoms in a flame, furnace, or plasma being excited by electromagnetic (EM) radiation. When these atoms absorb energy, they become excited and subsequently release energy as they return to their original state. This emitted light, or "fluorescence," is observed at a right angle to the incident beam. Both absorption and emission processes transpire at distinct wavelengths, which...
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usfAD based effective unknown attack detection focused IDS framework.

Md Ashraf Uddin1, Sunil Aryal2, Mohamed Reda Bouadjenek2

  • 1School of Information Technology, Deakin University, Waurn Ponds Campus, Geelong, Australia. ashraf.uddin@deakin.edu.au.

Scientific Reports
|November 24, 2024
PubMed
Summary
This summary is machine-generated.

New Intrusion Detection Systems (IDS) use semi-supervised learning to detect cyber threats without attack samples. One Class Classification (OCC) models, particularly usfAD, show superior performance in identifying novel attacks compared to supervised methods.

Keywords:
Anomaly detectionIntrusion detection systemIoTNetwork trafficOne class classificationZero day attacks

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • The proliferation of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems has escalated cyber threats.
  • Traditional Intrusion Detection Systems (IDS) often rely on supervised machine learning, requiring extensive labeled attack data, which is difficult to obtain in real-world scenarios.
  • Supervised IDS struggle to detect zero-day or novel attacks due to evolving threat landscapes.

Purpose of the Study:

  • To propose and evaluate semi-supervised learning strategies for developing effective Intrusion Detection Systems (IDS).
  • To address the challenge of limited attack samples and the inability of traditional IDS to detect unknown cyber threats.
  • To compare the performance of a synthetic data-based supervised model against a One Class Classification (OCC) model trained solely on benign traffic.

Main Methods:

  • Implemented two semi-supervised learning approaches for IDS: (1) supervised learning with synthetic attack samples, and (2) One Class Classification (OCC) trained exclusively on benign network traffic.
  • Utilized the state-of-the-art anomaly detection technique, usfAD, for the OCC model.
  • Evaluated both approaches on 10 recent benchmark Intrusion Detection System (IDS) datasets, simulating real-world conditions.

Main Results:

  • The One Class Classification (OCC) model, specifically the one based on usfAD, demonstrated significantly superior performance.
  • The usfAD-based OCC model outperformed conventional supervised classification methods.
  • The OCC approach also showed better results than other tested OCC-based techniques, especially in detecting previously unseen attacks.

Conclusions:

  • Semi-supervised learning, particularly One Class Classification (OCC) with advanced anomaly detection like usfAD, offers a robust solution for Intrusion Detection Systems (IDS).
  • This approach effectively overcomes the limitations of supervised methods regarding the need for attack samples and the detection of novel cyber threats.
  • The usfAD-based OCC model is highly effective for real-world network security, particularly for identifying zero-day attacks.