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Intelligent Techniques for Detecting Network Attacks: Review and Research Directions.

Malak Aljabri1,2, Sumayh S Aljameel3, Rami Mustafa A Mohammad4

  • 1Computer Science Department, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia.

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Network attack detection faces challenges as current intelligent systems, including machine learning (ML) and deep learning (DL) models, lack capabilities to mitigate all threats. This research evaluates these systems to identify research gaps and future directions.

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Area of Science:

  • Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • The proliferation of internet use and network technologies increases network attack risks.
  • Network attacks encompass unauthorized access, damage, and disruption, posing significant threats.
  • Network attack detection is a critical research area within cybersecurity.

Purpose of the Study:

  • To evaluate contemporary intelligent-based research directions for network attack detection.
  • To address the limitations of existing intelligent systems in mitigating diverse network attacks.
  • To identify gaps in current research and suggest future directions for developing robust network defense systems.

Main Methods:

  • Assessment of intelligent-based network attack detection systems.
  • Analysis focused on training datasets, algorithms, and evaluation metrics as key components.
  • Review of existing literature on machine learning (ML) and deep learning (DL) models in cybersecurity.

Main Results:

  • Current intelligent-based approaches, including ML and DL, are effective in specific domains but not universally applicable to all network attacks.
  • Existing systems often lack essential capabilities to reliably confront a wide range of network threats.
  • A gap exists in the development of intelligent systems capable of addressing the full spectrum of current and future network attacks.

Conclusions:

  • There is a need for more comprehensive intelligent systems to counter diverse network attacks.
  • The research provides a foundation for scholars to identify research scopes and develop advanced network defense strategies.
  • Future research should focus on enhancing the capabilities of intelligent systems to achieve reliable and versatile network attack mitigation.