Enhanced Network Anomaly Detection Based on Deep Neural Networks

Naseer, Sheraz; Saleem, Yasir; Khalid, Shehzad; Bashir, Muhammad Khawar; Han, Jing; Iqbal, Muhammad Munwar; Han, Kijun

doi:10.1109/access.2018.2863036

Cited by 396 publications

(179 citation statements)

References 16 publications

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“…Table II confirms that A(X) achieves better results compared to another work included two different methods [9]. Although these methods are semi-supervised, they used anomalous records of [10] 83.28 DCNN [11] 85.00 AE [9] 88.28 Sparse AE and MLP [12] 88.39 Random Tree [13] 88.46 De-noising AE [9] 88.65 LSTM [11] 89.00 Random Tree and NBTree [13] 89.24 Ours A(X) 91.39…”

Section: B Evaluationsupporting

confidence: 64%

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

Mohammadi

Sabokrou

2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

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This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity of network attacks in addition to the need for generalization, motivate us to propose a semi-supervised method. Inspired by the successes of Generative Adversarial Networks (GANs) for training deep models in semi-unsupervised setting, we have proposed an endto-end deep architecture for IDS. The proposed architecture is composed of two deep networks, each of which trained by competing with each other to understand the underlying concept of the normal traffic class. The key idea of this paper is to compensate the lack of anomalous traffic by approximately obtain them from normal flows. In this case, our method is not biased towards the available intrusions in the training set leading to more accurate detection. The proposed method has been evaluated on NSL-KDD dataset. The results confirm that our method outperforms the other state-of-the-art approaches.

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Section: B Evaluationsupporting

confidence: 64%

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

Mohammadi

Sabokrou

2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

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“…Precision: say the right intrusion estimate fraction with predictable overall intrusions as in (11) Recall: say the allowed intrusion estimate fraction separated by the full amount of valid intrusion possibilities in the test set in (12).…”

Section: Evaluation Discussionmentioning

confidence: 99%

“…Naseer [12] explored the appropriate anomaly-based strategy to IDS produced on multiple profound ANN like convolutionary neural, regular neural systems and auto encoders which are competent on NSLKDD dataset. These are done on a GPU-related test bed that uses theano-backed keras.…”

Section: Existing Work R Vinayakumarmentioning

confidence: 99%

An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

T¹,

Saravanan²

2020

IJACSA

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The necessity for safety of information in a network has inflated due to the impressive growth of web applications. Several methods of intrusion detection are used to detect irregularities which depend on precision, detection frequency, other parameters and are anticipated to familiarize to vigorously varying risk scenes. To accomplish consistent abnormalities detection in a network many machine learning algorithms have been formulated by researchers. A technique based on unsupervised machine learning that use two separate machine learning algorithms to identify anomalies in a network viz convolutional autoencoder and softmax classifier is proposed. These profound models were skilled as well as evaluated on NSLKDD test data sets on the NSLKDD training dataset. Using well-known classification metrics such as accuracy, precision and recall, these machine learning models were assessed. The developed intrusion detection system model experimental findings showed promising outcomes in anomaly detection systems for real-world implementation and is compared with the prevailing definitive machine learning techniques. This strategy increases the detection of network intrusion and offers a renewed intrusion detection study method.

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“…It is a branch of Machine Learning, and has better learning capability of analysis of composite data. Sheraz Naseer et al [11] implemented different Deep Neural Networks models for IDS including CNN, Autoencoders, and RNN. Many deep learning models are used GPU powered test-bed for train and test of NSL-KDD dataset.…”

Section: Related Studymentioning

confidence: 99%

An Intrusion Detection Model Based on Deep Long Short Term Recurrent Neural Network

Rao,

PVGD

2019

IJEAT

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With the rapid increase of network based services and internet users on various platforms are becoming the major targets of attacks. Intrusion detection is the process of monitoring the attacks and analyzing their signs and violation of security policies which are occurring in the systems or networks. Intrusion Detection System is a prominent research area in security analysis and evaluation. In order to identify the attack type, we proposed Deep Long Short Term Memory-Recurrent Neural Network (DLSTM-RNN) method with seven optimizers and 500 epochs to train and test a dataset. Initially the data transformation, normalization are used to preprocess the data. The preprocessed train and test data is given input to the model. The bench mark NSL-KDD dataset used to train and test the model. The results are obtained for five-class classification (attack types).The model outperformed with adamax optimizer on NSL-KDD dataset. The metrics accuracy, detection rate, and false alarm rate areevaluated to ascertain the detection efficacy of the model. We compare the model to existing convolutional learning methods.

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Enhanced Network Anomaly Detection Based on Deep Neural Networks

Cited by 396 publications

References 16 publications

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

An Intellectual Detection System for Intrusions based on Collaborative Machine Learning

An Intrusion Detection Model Based on Deep Long Short Term Recurrent Neural Network

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