Deep-FS: A feature selection algorithm for Deep Boltzmann Machines

Taherkhani, Aboozar; Cosma, Georgina; McGinnity, TM

doi:10.1016/j.neucom.2018.09.040

Cited by 68 publications

(40 citation statements)

References 25 publications

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“…Seven deep learning models were evaluated on CICIDS2018 and the Bot-IoT dataset [58]. The models included RNNs, deep neural networks [59], restricted Boltzmann machines, deep belief networks [60], Convolutional Neural Networks (CNNs) [61], deep Boltzmann machines [62], and deep autoencoders. The Bot-IoT dataset is a 2018 creation from the University of New South Wales (UNSW) that incorporates about 72,000,000 normal and botnetInternet of Things (IoT) instances with 46 features.…”

Section: D'hooge Et Al [19] (Inter-dataset Generalization Strength Omentioning

confidence: 99%

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

2020

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The exponential growth in computer networks and network applications worldwide has been matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 were created to train predictive models on network-based intrusion detection. These datasets are not meant to serve as repositories for signature-based detection systems, but rather to promote research on anomaly-based detection through various machine learning approaches. CSE-CIC-IDS2018 contains about 16,000,000 instances collected over the course of ten days. It is the most recent intrusion detection dataset that is big data, publicly available, and covers a wide range of attack types. This multi-class dataset has a class imbalance, with roughly 17% of the instances comprising attack (anomalous) traffic. Our survey work contributes several key findings. We determined that the best performance scores for each study, where available, were unexpectedly high overall, which may be due to overfitting. We also found that most of the works did not address class imbalance, the effects of which can bias results in a big data study. Lastly, we discovered that information on the data cleaning of CSE-CIC-IDS2018 was inadequate across the board, a finding that may indicate problems with reproducibility of experiments. In our survey, major research gaps have also been identified.

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Section: D'hooge Et Al [19] (Inter-dataset Generalization Strength Omentioning

confidence: 99%

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

2020

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“…Recently, DBN is most valued for its versatile ability and has exposed great success in unsupervised feature dimensionality reduction and supervised pattern classification [41] , [42] . But only limited studies in literature have used DBN in the field of intrusion detection [43] , [44] .…”

Section: The Classifier Scheme For Intrusion Detectionmentioning

confidence: 99%

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

Binbusayyis

Vaiyapuri

2020

Heliyon

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The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.

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“…The output of DBN can be taken as the features of the original signals extracted automatically. In order to reduce the computational cost and the number of required samples (monitoring data) for the training of DBN, its variant, i.e., deep feature selection (Deep-FS) [15] has been adopted in this research. Deep-FS aims to find a set of inputs with useful information, whilst the sample data without useful information about the input data are removed by the generative property of RBM.…”

Section: Signal Feature Extraction Based On Statistical Analysis and mentioning

confidence: 99%

A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning

et al. 2019

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Monitoring and predicting tool wear is an important issue in dynamic process control under changing conditions, especially for machining largesized difficult-to-cut materials used in airplanes. Existing tool wear monitoring and prediction methods are mainly based on given cutting conditions over a period of time. This paper presents a novel method for accurately predicting tool wear under varying cutting conditions based on a proposed new meta-learning model which can be easily trained, updated and adapted to new machining tasks of different cutting conditions. Experiments proved a substantial improvement in the accuracy of predicting tool wear compared with existing deep learning methods. Condition monitoring, Process control, Meta-learning Contents lists available at SciVerse ScienceDirect

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Deep-FS: A feature selection algorithm for Deep Boltzmann Machines

Cited by 68 publications

References 25 publications

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning

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