# Intrusion Detection: A Deep Learning Approach

**Authors:** Ishaan Shivhare, Joy Purohit, Vinay Jogani, Samina Attari, Dr. Madhav Chandane  
**arXiv:** [2306.07601](https://arxiv.org/abs/2306.07601)  
**Date:** 2023-06-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.07601v1/

## Key points

- Intrusion detection is a critical issue across various industries
- Recent advancements in artificial intelligence have led to the adoption of deep learning approaches for intrusion detection
- Innovative approach combines a Convolutional Neural Network (CNN) module with a Long Short Term Memory (LSTM) module, complemented by a Support Vector Machine (SVM) classification function
- Architecture aims to enhance the accuracy and efficiency of intrusion detection systems
- Research presented at the 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT 2023)
- Study introduces advanced architecture and conducts comparative analysis between traditional machine learning techniques and deep learning methodologies
- Comprehensive approach showcases potential of deep learning techniques in enhancing cybersecurity measures

## Summary

Intrusion detection is a critical issue across various industries, necessitating effective detection methods to combat network intrusions. Recent advancements in artificial intelligence have led to the adoption of deep learning approaches for intrusion detection. While current strategies for multi-class intrusion detection rely on deep neural networks, they often overlook spatial relationships between data objects and long-term dependencies within the dataset. This innovative approach combines a Convolutional Neural Network (CNN) module with a Long Short Term Memory (LSTM) module, complemented by a Support Vector Machine (SVM) classification function. By integrating these components, the architecture aims to enhance the accuracy and efficiency of intrusion detection systems. The research conducted by authors Ishaan Shivhare, Joy Purohit, Vinay Jogani, Samina Attari, and Dr. Madhav Chandane was presented at the 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT 2023). The study not only introduces this advanced architecture but also conducts a thorough analysis comparing traditional machine learning techniques with deep learning methodologies. This comparative analysis sheds light on areas that warrant further exploration and development in the field of intrusion detection. Overall, this comprehensive approach to intrusion detection showcases the potential of deep learning techniques in enhancing cybersecurity measures and safeguarding against network intrusions across diverse industries.