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Upsampling01:22

Upsampling

346
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
346
Sampling Theorem01:15

Sampling Theorem

835
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
835
Aliasing01:18

Aliasing

276
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
276
Bandpass Sampling01:17

Bandpass Sampling

280
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
280
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

245
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:
245
Downsampling01:20

Downsampling

293
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
293

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Design and Analysis for Fall Detection System Simplification
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Network intrusion detection using oversampling technique and machine learning algorithms.

Hafiza Anisa Ahmed1, Anum Hameed1, Narmeen Zakaria Bawany1

  • 1Department of Computer Science and Software Engineering, Jinnah University for Women, Karachi, Sindh, Pakistan.

Peerj. Computer Science
|February 3, 2022
PubMed
Summary
This summary is machine-generated.

Network Intrusion Detection Systems (NIDS) struggle with modern cyber threats. This study enhances NIDS using machine learning on the UNSW-NB15 dataset, achieving 95.1% accuracy with Random Forest and SMOTE for improved network security.

Keywords:
Imbalanced classesNetwork attackNetwork intrusion detection system (NIDS)Synthetic minority over-sampling technique (SMOTE)UNSW-NB15 dataset

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • The internet's growth increases network security threats, necessitating advanced detection methods.
  • Traditional Network Intrusion Detection Systems (NIDS) struggle to identify novel cyber attacks.
  • Machine learning offers potential for more effective network traffic anomaly detection.

Purpose of the Study:

  • To develop and evaluate a machine learning framework for detecting diverse network attack categories.
  • To assess the performance of various classification algorithms on a contemporary network traffic dataset.
  • To improve NIDS accuracy by addressing class imbalance and feature selection.

Main Methods:

  • Implemented five machine learning algorithms: Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors, and Artificial Neural Networks.
  • Utilized the University of New South Wales (UNSW-NB15) dataset, containing nine network attack categories.
  • Applied Synthetic Minority Oversampling Technique (SMOTE) to handle class imbalance and Principal Component Analysis (PCA) for feature selection.

Main Results:

  • The Random Forest algorithm achieved the highest initial accuracy of 89.29%.
  • SMOTE application significantly improved classification model accuracy.
  • The Random Forest classifier, combined with SMOTE and 24 PCA-selected features, reached an accuracy of 95.1%.

Conclusions:

  • Machine learning, particularly Random Forest with SMOTE, offers a robust approach to enhancing network intrusion detection.
  • The UNSW-NB15 dataset provides a valuable resource for training and validating modern NIDS.
  • Addressing class imbalance is crucial for improving the performance of NIDS in detecting emerging network threats.