The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Enhanced Network Anomaly Detection Based on Deep Neural Networks
Open accessAbstract
Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.
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“From a deployment perspective, IDS can be divided into two categories: Host-based IDS and Network-based IDS [173] [239].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [173]
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Metadata
- Authors
- Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K.
- Affiliations
- University of Engineering and Technology Lahore (PK)
- Bahria University (PK)
- Kyungpook National University (KR)
- University of Engineering and Technology Taxila (PK)
- ORCID
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 48231-48246
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2886020981
- Cited by
- 507
- References
- 44
- Citations by year
- 2026: 28 · 2025: 59 · 2024: 70 · 2023: 89 · 2022: 88 · 2021: 68 · 2020: 69 · 2019: 35 · 2018: 1
- FWCI
- 26.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Artificial Immune Systems Applications
- Keywords
- network anomaly detection, intrusion detection systems, deep neural network, convolutional neural networks, autoencoder, recurrent neural networks, KDD Cup 1999 dataset, network security
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