Semantic Scholar Open Access 2016 1162 sitasi

A Deep Learning Approach for Network Intrusion Detection System

A. Javaid Quamar Niyaz Weiqing Sun M. Alam

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Abstrak

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

Topik & Kata Kunci

Computer Science

Penulis (4)

A. Javaid

Quamar Niyaz

Weiqing Sun

M. Alam

Format Sitasi

APA MLA BibTeX

Javaid, A., Niyaz, Q., Sun, W., Alam, M. (2016). A Deep Learning Approach for Network Intrusion Detection System. https://doi.org/10.4108/eai.3-12-2015.2262516

Akses Cepat

Lihat di Sumber doi.org/10.4108/eai.3-12-2015.2262516

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 1162×
Sumber Database: Semantic Scholar
DOI: 10.4108/eai.3-12-2015.2262516
Akses: Open Access ✓