A deep auto-encoder based approach for intrusion detection system
Abstract
One of the most challenging problems facing network operators today is network attacks identification due to extensive number of vulnerabilities in computer systems and creativity of attackers. To address this problem, we present a deep learning approach for intrusion detection systems. Our approach uses Deep Auto-Encoder (DAE) as one of the most well-known deep learning models. The proposed DAE model is trained in a greedy layer-wise fashion in order to avoid overfitting and local optima. The experimental results on the KDD-CUP'99 dataset show that our approach provides substantial improvement over other deep learning-based approaches in terms of accuracy, detection rate and false alarm rate.
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In mobile and wearable-based human activity recognition, class imbalance can be caused by distortions in the dataset or inaccuracies in sensor data calibration, reducing the generalizability of the model's performance.
“In the case of mobile and wearable-based human activity recognition, the class imbalance can be caused by distortions in the dataset or inaccuracies in sensor data calibration, reducing the generalizability of the model's performance (Edel & Köppe, 2016) [76].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [76]
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References 15 · Cited by 146
- Authors
- Farahnakian, F., & Heikkonen, J.
- Affiliations
- University of Turku (FI)
- Venue
- 2018 20th International Conference on Advanced Communication Technology (ICACT)
- Publisher
- IEEE
- Published
- 2018-02
- Pages
- 1-1
- ISSN
- 1738-9445 (ISSN-L)
- OpenAlex
- W2795147830
- Cited by
- 146
- References
- 15
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 10 · 2023: 26 · 2022: 25 · 2021: 33 · 2020: 27 · 2019: 12 · 2018: 5
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- International Conference on Advanced Communication Technology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- intrusion detection, deep autoencoder, network attack detection, KDD Cup 1999 dataset, false alarm rate, network security
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