CoChat
Sign Up
Cited by 507Network Security and Intrusion DetectionNetwork Packet Processing and OptimizationArtificial Immune Systems Applications

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 access
Naseer, Sheraz, Saleem, Yasir, Khalid, Shehzad, Bashir, Muhammad Khawar, Han, Jihun, Iqbal, Muhammad Munwar, Han, Kijun·2018·IEEE Access
DOI 10.1109/access.2018.2863036
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1109/access.2018.2863036

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 44 references

Research works printed but not linked: 8

  1. mcginnis, 2017, Beyond One-Hot An Exploration of Categorical Variables
  2. mcginnis, 2017, BaseN Encoding and Grid Search in Categorical Variables
  3. 2011, Contrast Coding Systems for Categorical Variables
  4. goodfellow, 2016, Deep Learning
  5. minsky, 1969, Perceptrons
  6. chollet, 2015, Keras
  7. zhang, 2017, Strategies to Encode Categorical Variables With Many Categories
  8. wang, 2015, The Applications of Deep Learning on Traffic Identification

Cited by

Sorted by citations

Items 301–400 of 504

Related

See all

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
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018
Volume
6
Pages
48231-48246
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2018.2863036
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
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review