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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2306.07601 |
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| _version_ | 1866910582363389952 |
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| author | Shivhare, Ishaan Purohit, Joy Jogani, Vinay Attari, Samina Chandane, Madhav |
| author_facet | Shivhare, Ishaan Purohit, Joy Jogani, Vinay Attari, Samina Chandane, Madhav |
| contents | Network intrusions are a significant problem in all industries today. A critical part of the solution is being able to effectively detect intrusions. With recent advances in artificial intelligence, current research has begun adopting deep learning approaches for intrusion detection. Current approaches for multi-class intrusion detection include the use of a deep neural network. However, it fails to take into account spatial relationships between the data objects and long term dependencies present in the dataset. The paper proposes a novel architecture to combat intrusion detection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and with a Support Vector Machine (SVM) classification function. The analysis is followed by a comparison of both conventional machine learning techniques and deep learning methodologies, which highlights areas that could be further explored. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07601 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Intrusion Detection: A Deep Learning Approach Shivhare, Ishaan Purohit, Joy Jogani, Vinay Attari, Samina Chandane, Madhav Cryptography and Security Computer Vision and Pattern Recognition Network intrusions are a significant problem in all industries today. A critical part of the solution is being able to effectively detect intrusions. With recent advances in artificial intelligence, current research has begun adopting deep learning approaches for intrusion detection. Current approaches for multi-class intrusion detection include the use of a deep neural network. However, it fails to take into account spatial relationships between the data objects and long term dependencies present in the dataset. The paper proposes a novel architecture to combat intrusion detection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and with a Support Vector Machine (SVM) classification function. The analysis is followed by a comparison of both conventional machine learning techniques and deep learning methodologies, which highlights areas that could be further explored. |
| title | Intrusion Detection: A Deep Learning Approach |
| topic | Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.07601 |