Network Intrusion Detection Using Deep Learning (original) (raw)

Abstract

Intrusion-detection system aims at detecting attacks against computer systems and networks or, in general, against information systems. Though various part of encryption techniques and firewalls are used to block common attacks, attackers always finds a way in intruding the network. This creates a huge need of dynamic network intrusion detection system where it can protect from nearly all types of attacks. Since dynamic nature of the system is concerned, it should be able to provide with a self-learning mechanism. Deep learning is one of the mostly preferred algorithms in dynamic learning. This paper proposes Convolution Neural Network (CNN) algorithm for intrusion detection.

Key takeaways

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  1. The paper proposes a Convolutional Neural Network (CNN) for dynamic intrusion detection systems.
  2. CNN outperforms traditional machine learning algorithms in accuracy and efficiency.
  3. Strides in CNN reduce output size by shifting pixels in the input data.
  4. Intrusion detection is essential for safeguarding networks against unauthorized access and cyber attacks.
  5. The study emphasizes the need for a self-learning mechanism in intrusion detection systems.

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References (9)

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  8. Blessy S, Samyuktha Ravi, Shurabthini S, Amudha P, "Network Intrusion Detection Using Deep Learning", International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN : 2394-4099, Print ISSN : 2395-1990, Volume 8 Issue 3, pp. 469-472, May-June 2021.
  9. Journal URL : https://ijsrset.com/IJSRSET2183201