Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept

Bostani, Hamid; Sheikhan, Mansour

doi:10.1016/j.patcog.2016.08.027

Cited by 55 publications

(26 citation statements)

References 66 publications

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“…However, this approach has a high false positive rate. Bostani and Sheikhan [24] proposed an unsupervised framework based on Optimum-path forest algorithm and KMeans clustering technique. This framework models malicious and normal behavior of networks.…”

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.

340

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.

340

157

View full text Add to dashboard Cite

show abstract

“…This section considers a well-known real-world data set NSL-KDD-99 (Tavallaee et al (2009)), which has been widely used as a benchmark (Bostani and Sheikhan (2017) ;Wang et al (2010);Yang et al (2017b)). This data set is a modified version of KDD Cup 99 data set generated in a military network environment.…”

Section: Methodsmentioning

confidence: 99%

“…The testing data set was also extracted from original Note that the testing data set has been used in a number of projects with different classification approaches. In particular, decision tree, naive Bayes, back-propagation neural network (BPNN), fuzzy clustering-artificial neural network (FC-ANN) have been employed in Wang et al (2010), and modified optimum-path forest (MOPF) was applied in Bostani and Sheikhan (2017). The accuracy of the classification results for each class of network traffic generated by different approaches including the proposed one with the initialised rule base and the optimised rule base is listed in Table 12.…”

Section: Tsk+ Model Evaluationmentioning

confidence: 99%

An extended Takagi–Sugeno–Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

Yang

et al. 2017

Soft Comput

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A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi-Sugeno-Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: (1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base and (2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases.

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“…Though the model was efficient in filtering out noises, it resulted in high FPR ratio. The authors in [17] put forward an unsupervised NIDS through Optimum-path forest approach and K-Means clustering model. The study attempted to model malicious and benign behaviors.…”

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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Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept

Cited by 55 publications

References 66 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

An extended Takagi–Sugeno–Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

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

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