Hybrid Classification Approach Using Self-Organizing Map and Back Propagation Artificial Neural Networks for Intrusion Detection

Al-Hamouz, Sadeq; Abu-Shareha, Ahmad Adel

doi:10.1109/dese.2017.28

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…Researchers in [1] applied a long short-term memory (LSTM) model to discover intrusion and utilized the CIDDS001 dataset for assessing the LSTM model's performance and they discovered that it outperforms on SVM, MLP, and Naïve Bayes techniques concerning to multiclassification problem. Regarding the self-organization map (SOM), Sadeq and Ahmad studied the effectiveness of combining the SOM with a backpropagation neural network (BPNN) to reveal intrusion systems [2]. The proposed approach is divided into two-stage.…”

Section: Related Workmentioning

confidence: 99%

“…In such circumstances, intrusion detection has become a crucial part of computer security to ensure that attacks and intrusion activities can be detected [1]. Intrusions are threats to network systems that may come in different forms, such as damaging the systems and making it unavailable, information examination, and information manipulation [2]. There are two kinds of intrusions passive and active, where passive intrusions are surreptitiously and without detection, whereas active intrusions lead to change and effect to network resources.…”

Section: Introductionmentioning

confidence: 99%

“…Intrusion Detection System (IDS), an influential approach, is designated to detect normal and malicious activities in computers. It is a system that defends the network by identifying suspicious activities while analyzing the network traffic to detect and counter intrusions [2] timely. An IDS has two main methods of detecting intrusions: signature-based IDS and anomaly-based IDS.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Model based on Radial basis Function Neural Network for Intrusion Detection

Albahar¹,

Alharbi²,

Alsuwat³

et al. 2020

IJACSA

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An Intrusion Detection System (IDS) is a system that monitors the network for identifying malicious activities. Upon identifying the unusual activities, IDS sends a notification to the network administrators to warn about the hackers' hostile activities. To detect intrusion, signature-based systems are considered to be one of the most effective methods. However, they cannot detect new attacks. Additionally, it is costly and challenging to keep the attack signatures database up to date with known signatures, which constructed a significant drawback. Neural networks are capable of learning through input patterns and have the potential to generalize data. In this paper, we propose a hybrid model based on Directed Batch Growing Self-Organizing Map (DBGSOM) combined with a Radial Basis Function Neural Network (RBFNN) detecting abnormalities in the network. Based on our experiment, the proposed model performed well and has resulted in satisfactory performance measures compared to Self-Organizing Maps and Radial Basis Function Neural Network (SOM&RBFNN) model.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Model based on Radial basis Function Neural Network for Intrusion Detection

Albahar¹,

Alharbi²,

Alsuwat³

et al. 2020

IJACSA

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“…Artificial Neural Network it is a very popular technique in the field of classification and machine learning, because its performance is very good [20]. The general structure of ANN is composed three main layers one input layer, one or more hidden layers and one output layer.…”

Section:  Annmentioning

confidence: 99%

Features Optimization for ECG Signals Classification

Ahmad¹,

Matti²,

Essa³

et al. 2018

ijacsa

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A new method is used in this work to classify ECG beats. The new method is about using an optimization algorithm for selecting the features of each beat then classify them. For each beat, twenty-four higher order statistical features and three timing interval features are obtained. Five types of beat classes are used for classification in this work, atrial premature contractions (APC), normal (NOR), premature ventricular contractions (PVC), left bundle branch (LBBB) and right bundle branch (RBBB). Cuttlefish algorithm is used for feature selection which is a new bio-inspired optimization algorithm. Four classifiers are used within CFA, Scaled Conjugate Gradient Artificial Neural Network (SCG-ANN), K-Nearest Neighborhood (KNN), Interactive Dichotomizer 3 (ID3) and Support Vector Machine (SVM). The final results show an accuracy of 97.96% for ANN, 95.71% for KNN, 94.69% for ID3 and 93.06% for SVM, these results were tested on fourteen signal records from MIT-HIH database, where 1400 beats were extracted from these records.

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“…Hence, various methods for traffic attack detection have been developed. The intelligent adaptive self-learning and self-organizing system for viruses detection based on the integration of the Artificial Immune Systems and Artificial Neural Networks was proposed in [1]; an automated traffic attacks classification approach based on the back propagation neural network was proposed in [2]; [3] proved that the Naive Bayes estimator can achieve high precision of traffic attack detection, et cetera. However, previous methods came across the challenge of data classification for large-scale and asymmetric-distributed datasets, which further reduces the accuracy of intrusion detection.…”

Section: Introductionmentioning

confidence: 99%

Direct Batch Growth Hierarchical Self-Organizing Mapping Based on Statistics for Efficient Network Intrusion Detection

Yang²,

Guo³

et al. 2020

IEEE Access

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A new evaluation mechanism was proposed to enhance the representation of data topology in the directed batch growth hierarchical self-organizing mapping. In the proposed mechanism, the growth threshold and the correlation worked in a case-sensitive manner through the statistic calculation of the input data. Since the proposed model enabled a more thorough representation of data topology from both the horizontal and the vertical directions, it naturally held great potential in detecting various traffic attacks. Numerical experiments of network intrusion detection were carried out on the datasets of KDD99, Moore and CICIDS2017, where the good performance validated the superiority of the proposed method.INDEX TERMS Network intrusion detection, growth hierarchical self-organizing mapping, statistical enhancement, growth threshold, correlation.

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Hybrid Classification Approach Using Self-Organizing Map and Back Propagation Artificial Neural Networks for Intrusion Detection

Cited by 7 publications

References 9 publications

A Hybrid Model based on Radial basis Function Neural Network for Intrusion Detection

A Hybrid Model based on Radial basis Function Neural Network for Intrusion Detection

Features Optimization for ECG Signals Classification

Direct Batch Growth Hierarchical Self-Organizing Mapping Based on Statistics for Efficient Network Intrusion Detection

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