Selection of Candidate Support Vectors in incremental SVM for network intrusion detection

Chitrakar, Roshan; Huang, Chuanhe

doi:10.1016/j.cose.2014.06.006

Cited by 78 publications

(33 citation statements)

References 6 publications

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“…All of the aforementioned detection techniques were evaluated on the KDD Cup 99 dataset. However, due to some limitations in this dataset, which will be discussed in Subsection 5.1, some other detection methods [18], [19], [20], [21], [22], [23] were evaluated using other intrusion detection datasets, such as NSL-KDD [24] and Kyoto 2006+ [25]. A dimensionality reduction method proposed in [25] was to find the most important features involved in building a naive bayesian classifier for intrusion detection.…”

Section: Performance Evaluationmentioning

confidence: 99%

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi

Nanda

et al. 2016

IEEE Trans. Comput.

528

241

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Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

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Section: Performance Evaluationmentioning

confidence: 99%

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi

Nanda

et al. 2016

IEEE Trans. Comput.

528

241

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show abstract

“…Feature selection is a preprocessing step that is usually applied in the area of data mining, thus, improving the classification rate. The so-called CSV-ISVM algorithm was proposed in [22] and uses incremental SVM in order to select candidate support vectors showing the advantages in real-time network intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

Applying a Neural Network Ensemble to Intrusion Detection

Ludwig

2019

Journal of Artificial Intelligence and Soft Computing Research

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An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

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“…The Decentralized Time-Window Based Real-Time Anomaly Detection Mechanism (DTRAD) in Iot model uses an improved Gauss kernel called U-RBF to suppress noise. Other SVM based models include works by Kuang et al [13], Ahmad et al [14] and Chitrakar and Huang [15]. Other machine learn-ing models used on intrusion detection systems include, Artificial Neural Network based model [16], Decision Tree based IDS model [17], Extreme Learning based model [18] and K-Nearest Neighbor based model [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Decentralized Time-Window Based Real-Time Anomaly Detection Mechanism (DTRAD) in Iot

Sanjith¹,

Raj²

2019

IJRTE

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Abstract: Detecting intrusions has become a mandatory service in IoT environments. This is due to the power and resource con-strained nature of the networks. This paper presents a Decentralized Time-Window based Anomaly Detection (DTRAD) model for cost and time effective intrusion detection in IoT environments. The proposed model is composed of time win-dow based training data selection module, which enables better detection and reduced bias. Training data are selected based on their temporal significance and the bag creation process is also temporally performed such that data with similar temporal signatures are grouped into same bags. The ensemble model is created and weighted voting is performed to ena-ble better results. The data reinforcement module enables new data to be appended to the training data, hence maintaining the recency of the data. Further, the entire process is decentralized, hence enabling data processing at appropriate nodes. This keeps the size of the training data low, hence reducing the computational complexity of the model to a large extent. Experiments were performed with benchmark data and comparisons were performed with recent models. Results indicate high performance of the proposed models.

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Selection of Candidate Support Vectors in incremental SVM for network intrusion detection

Cited by 78 publications

References 6 publications

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Applying a Neural Network Ensemble to Intrusion Detection

Decentralized Time-Window Based Real-Time Anomaly Detection Mechanism (DTRAD) in Iot

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