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

Ambusaidi, Mohammed A.; He, Xiangjian; Nanda, Priyadarsi; Tan, Zhiyuan

doi:10.1109/tc.2016.2519914

Cited by 510 publications

(270 citation statements)

References 48 publications

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“…Ambusaidi et al in [33] proposed a mutual information based IDS that selects optimal feature for classification based on feature selection algorithm. Their approach was evaluated using three benchmark data set (KDD Cup 99, NSL-KDD and Kyoto 2006+).…”

Section: Related Workmentioning

confidence: 99%

A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks

Pajouh

Javidan

Khayami

et al. 2019

IEEE Trans. Emerg. Topics Comput.

341

157

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Abstract-With increasing reliance on Internet of Things (IoT) devices and services, the capability to detect intrusions and malicious activities within IoT networks is critical for resilience of the network infrastructure. In this paper, we present a novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks. The proposed model is using component analysis and linear discriminate analysis of dimension reduction module to spate the high dimensional dataset to a lower one with lesser features. We then apply a two-tier classification module utilizing Naïve Bayes and Certainty Factor version of K-Nearest Neighbor to identify suspicious behaviors. The experiment results using NSL-KDD dataset shows that our model outperforms previous models designed to detect U2R and R2L attacks.

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Section: Related Workmentioning

confidence: 99%

A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks

Pajouh

Javidan

Khayami

et al. 2019

IEEE Trans. Emerg. Topics Comput.

341

157

View full text Add to dashboard Cite

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“…The decision-tree outlierclassification feature-based filter algorithm is adopted in this paper. Essentially, decision-tree outlier-classification feature-filtering is a continuous process that moves from high-frequency filtering to low-frequency filtering [28][29][30]. According to the outlier data-containing information flows in the large real-time database, the outlier data phase features before filtering at the local feature time scale parameter are obtained as follows:…”

Section: Improvement Of Outlier Data Mining In a Mobile Internet-basementioning

confidence: 99%

Mining Outlier Data in Mobile Internet‐Based Large Real‐Time Databases

2018

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Mining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This paper proposes a new mining outlier data method, which is used to analyze real-time data features, obtain magnitude spectra models of outlier data, establish a decisional-tree information chain transmission model for outlier data in mobile Internet, obtain the information flow of internal outlier data in the information chain of a large real-time database, and cluster data. Upon local characteristic time scale parameters of information flow, the phase position features of the outlier data before filtering are obtained; the decision-tree outlier-classification feature-filtering algorithm is adopted to acquire signals for analysis and instant amplitude and to achieve the phase-frequency characteristics of outlier data. Wavelet transform threshold denoising is combined with signal denoising to analyze data offset, to correct formed detection filter model, and to realize outlier data mining. The simulation suggests that the method detects the characteristic outlier data feature response distribution, reduces response time, iteration frequency, and mining error rate, improves mining adaptation and coverage, and shows good mining outcomes.

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“…The author proposed a cloud-based IDS. The study in [24] put forward a mutual information-based detection system, which identifies optimal attributes for classifiers on the basis of attribute selection approach. The model has been simulated over three popular corpuses-KDD Cup 99, Kyoto 2006+, and NSL-KDD.…”

Section: Related Workmentioning

confidence: 99%

Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions

Korani¹,

Reddy²

2018

IJET

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Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ish ), and benign scope heuristic ( bsh ), which further uses in predictive analysis to identify the IOT network transaction is prone to intrusion or benign. The experimental study revealed the significance of the proposal with maximal detection accuracy, and minimal miss rate.

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

Cited by 510 publications

References 48 publications

A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks

A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks

Mining Outlier Data in Mobile Internet‐Based Large Real‐Time Databases

Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions

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