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Cable Subjected to a Distributed Load01:24

Cable Subjected to a Distributed Load

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The analysis of suspension bridges is a complex and critical process that involves multiple factors, including the shape and tension of the main cables. The main cables of suspension bridges are subjected to distributed loads, which result in changes in tensile forces and deformation of the cable. These loads must be carefully considered to ensure that the bridge is safe and capable of supporting the weight of different loads.
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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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High-Speed Network DDoS Attack Detection: A Survey.

Rana M Abdul Haseeb-Ur-Rehman1, Azana Hafizah Mohd Aman1, Mohammad Kamrul Hasan1

  • 1Center for Cyber Security, Faculty of Information Science and Technology, University Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia.

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Summary
This summary is machine-generated.

Detecting distributed denial-of-service (DDoS) attacks on high-speed networks (HSN) is challenging. This review examines machine learning methods for improved network security and Cyber-Physical Systems (CPS) integrity.

Keywords:
cyber–physical systemdenial of servicedistributed denial of serviceexpress data pathhigh-speed networkintrusion detection systemmachine learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Network Engineering

Background:

  • A large number of device connections creates network vulnerabilities, increasing the risk of distributed denial-of-service (DDoS) attacks.
  • DDoS attacks can lead to significant financial losses and data corruption, compromising the integrity of Cyber-Physical Systems (CPS).
  • Current DDoS detection techniques are often ineffective, particularly for high-speed networks (HSN) due to rapid packet processing.

Purpose of the Study:

  • To review and compare various machine learning (ML) approaches for detecting DDoS attacks.
  • To analyze the effectiveness of ML techniques like k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) in intrusion detection systems (IDSs) and flow-based IDSs.
  • To identify challenges and suggest future research directions for DDoS attack detection in HSN.

Main Methods:

  • Qualitative analysis of existing literature on irregular traffic pattern detection.
  • Examination of machine learning techniques (k-means, KNN, NB) within intrusion detection systems (IDSs) and flow-based IDSs.
  • Review of data paths for packet filtering and their impact on HSN performance.

Main Results:

  • Current DDoS detection methods struggle with the complexity of high-speed networks.
  • Machine learning techniques show potential for enhancing DDoS detection accuracy.
  • A detailed taxonomy of DDoS attacks and a classification of detection techniques are presented.

Conclusions:

  • Effective DDoS attack detection is critical for network security and CPS integrity.
  • Further research is needed to optimize ML-based detection for high-speed networks.
  • Addressing the challenges of DDoS attacks in HSN requires innovative solutions and continued investigation.