A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers

Saleh, Ahmed I.; Talaat, Fatma M.; Labib, Labib M.

doi:10.1007/s10462-017-9567-1

Cited by 93 publications

(31 citation statements)

References 22 publications

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“…Jamil et al [60] created several candidate feature subsets by different feature selection algorithms, and chose the best subset according to the results of all the feature subsets' evaluations based on the five ML algorithms. The feature weighted or feature selection method based on KNN [61] has been studied comprehensively in many areas, such as transportation system [62], anomaly detection [63,64], image identification [65] and so on, except for the fine-grained classification of network traffic. Only Dong et al [66] in 2017 presented a modified version of consistency-based method in combination with a layered KNN classifier to evaluate the goodness of a feature subset.…”

Section: Related Workmentioning

confidence: 99%

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Cao

2020

Electronics

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The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.

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Section: Related Workmentioning

confidence: 99%

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Cao

2020

Electronics

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“…Both hybrid and ensemble approaches are used in attack detection to get more accurate prediction. Researchers in [4], [13], [14], [17], [28] models the IDS by using above said methods. Every method performs well in its own way but the specific method is s elected based on the problem to be taken by the researcher.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

“…Saleh et. al in [17] proposes a Hybrid IDS (HIDS) with three main contributions. First contribution is Naïve Base feature selection (NBFS) technique for dimensionality reduction with two submodules: Feature effect identificat ion (FEI) and mutual effect Identificat ion (M EI).…”

Section: Existing Approaches To Idsmentioning

confidence: 99%

Intrusion Detection Analytics: A Comprehensive Survey

Sameera¹,

Shashi²

2019

International Journal of Advanced Scientific Research and Management

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Cyber threat is growing on par with the advancements in the field of co mputer technology and information age which makes Intrusion detection Systems (IDSs) to get a lot of attention now a days. IDS is an evolv ing research area in the field of cyber security, which is aimed to detect cyber-intrusions. The authors have surveyed many research papers on IDS in the resent past and the essence of their survey is presented in this paper by keeping in thought of helping research scholars in the area of IDS. This paper aims at p resenting brief description of IDS and mach ine learning approach for its imp lementation. Though lot of literature survey on IDS exist, in this paper authors attempt to present a clear picture of IDS in all aspects through their extensive survey.

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“…The evaluated results were compared with existing methodologies. Ahmed I. Saleh et al [8] implemented Hybrid IDS for multi-class classification problems. Naïve Bayes Feature Selection (NBFS) technique used for reduction of dimensionality.…”

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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A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers

Cited by 93 publications

References 22 publications

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Intrusion Detection Analytics: A Comprehensive Survey

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

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