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Artificial Intelligence System for Tackling Cybersecurity Threats Using SD-WAN Software Simulations

  • Unique Paper ID: 167846
  • Volume: 11
  • Issue: 4
  • PageNo: 658-668
  • Abstract:
  • In today’s increasingly connected digital landscape, the threat of cyber attacks is ever present and evolving, posing significant risks to organizational networks and data security. Traditional network security measures, while essential, are often insufficient to counter sophisticated and rapidly changing attack vectors. This inadequacy highlights the need for more advanced and adaptive cyber security systems, such as Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), which can proactively identify and respond to suspicious activities in real time. However, the effectiveness of IDS/IPS systems is often challenged by several critical issues. This study designs and implements a cyber security system that simulates the functions of an Intrusion Detection System (IDS) and an Intrusion Prevention System (IPS) in a Software Defined Wide Area Network (SD-WAN) using MATLAB. The primary objective is to identify and respond to suspicious activities within a network by analysing traffic patterns, detecting anomalies, and automatically responding to potential threats. This encompasses the use of operational cyber security strategies, tactics, and processes designed to protect the confidentiality, integrity, and availability of data within an organization’s operational environment. The system performance was evaluated based on Precision, Recall, and F1-Score performance metrics indices. The Precision value was calculated as 1.0, which indicates that all instances identified as malicious were indeed malicious. This high precision rate is crucial for minimizing false positives, ensuring that legitimate network activities are not mistakenly flagged as threats. The Recall score was 0.9750, which suggests that the system successfully detected almost all of the malicious activities. The F1-Score, which is the harmonic mean of Precision and Recall, was 0.9873 in this study. The high F1-Score reflects a balanced performance between Precision and Recall, indicating that the system is both accurate and comprehensive in detecting threats. The simulation results demonstrate that the cyber security system designed in this study is highly effective in detecting and responding to malicious activities within a network. With a Precision of 1.0, Recall of 0.9750, and an F1-Score of 0.9873, the system ensures minimal false positives and a near-complete identification of all potential threats. The graphical analysis further supports these findings by providing a clear visual representation of how the system handles normal and malicious traffic. The system's ability to quickly and accurately detect threats, coupled with automated responses, can reduce the risk of successful cyber attacks and protect sensitive data and infrastructure. These results indicate that this study can be applied to various real-world scenarios, making them highly valuable for organizations looking to bolster their cyber-security defenses.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 4
  • PageNo: 658-668

Artificial Intelligence System for Tackling Cybersecurity Threats Using SD-WAN Software Simulations

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