An Empirical Evaluation of Deep Learning for Network Anomaly Detection

Malaiya, Ritesh K.; Kwon, Donghwoon; Kim, Jin Oh; Suh, Sang C.; Kim, Hyun‐Joo; Kim, Ikkyun

doi:10.1109/iccnc.2018.8390278

Cited by 51 publications

(19 citation statements)

References 15 publications

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“…Malaiya et al 104 proposed different IDS models based on fully connected networks, Variational AE, and Sequence‐to‐Sequence (Seq2Seq) structures, respectively. These models were examined for different datasets NSL‐KDD, KyotoHoneypot, UNSW‐NB15, IDS2017, and MAWILab traces 105 .…”

Section: Ai Methods For Nidsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

651

215

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The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

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Section: Ai Methods For Nidsmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

651

215

View full text Add to dashboard Cite

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“…A recurring question in deep one-class classification is how to meaningfully regularize against a feature map collapse φω ≡ c. Without regularization, minimum volume or maximum margin objectives, such as (16), (20), or (22), could be trivially solved with a constant mapping [137], [333]. Possible solutions for this include adding a reconstruction term or architectural constraints [137], [327], freezing the embedding [136], [139], [140], [142], [334], inversely penalizing the embedding variance [335], using true [144], [336], auxiliary [139], [233], [332], [337], or artificial [337] negative examples in training, pseudolabeling [152], [153], [155], [335], or integrating some manifold assumption [333].…”

Section: Deep One-class Classificationmentioning

confidence: 99%

“…We choose the OC-SVM [6] with standard RBF kernel k(x,x) = exp(−γ x −x 2 ) as a method for this task since the data is real-valued and low-dimensional, and the OC-SVM scales sufficiently well for this comparatively small data set. In addition, the ν-parameter formulation [see (20)] enables us to use our prior knowledge and, thus, approximately control the false alarm rate α and, with it, implicitly also the miss rate, which leads to our first recommendation:…”

Section: A Example 1: Thyroid Disease Detectionmentioning

confidence: 99%

“…AD today has numerous applications across a variety of domains. Examples include intrusion detection in cybersecurity [15]- [20], fraud detection in finance, insurance, healthcare, and telecommunication [21]- [27], industrial fault and damage detection [28]- [36], the monitoring of infrastructure [37], [38] and stock markets [39], [40], acoustic novelty detection [41]- [45], medical diagnosis [46]- [60] and disease outbreak detection [61], [62], event detection in the earth sciences [63]- [68], and scientific discovery in chemistry [69], [70], bioinformatics [71], genetics [72], [73], physics [74], [75], and astronomy [76]- [79]. The data available in these domains is continually growing in size.…”

Section: Introductionmentioning

confidence: 99%

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A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

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“…In recent years, the security situation assessment models have been effective investigated, [19][20][21][22][23][24][25][26][27] such as models based on mathematical methods, knowledge reasoning methods, and deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system

Yang

Feng

2021

Int J Intell Syst

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This paper investigates the problem of the existing information system security situation assessment models that are less concerned about fuzzy factors and lack of reasoning. First, Pythagorean fuzzy sets are adopted to deal with the ambiguity and uncertainty of the civil aviation airport security inspection information system security situation indicators, and a Pythagorean fuzzy Petri nets (PFPNs) model is established according to the indicator system. Then, based on the characteristics of PFPN, the propositions credibility reasoning algorithm and the security situation fuzzy reasoning algorithm are designed. Finally, the feasibility of the model is verified through the assessment experiment on the security inspection information system of a civil aviation airport. The experimental results show that the PFPN model has better stability and lower algorithm time complexity compared with other models.

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An Empirical Evaluation of Deep Learning for Network Anomaly Detection

Cited by 51 publications

References 15 publications

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

A Unifying Review of Deep and Shallow Anomaly Detection

A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system

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