A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering

Wang, Gang; Jiang, Hao; Ma, Jian; Huang, Lihua

doi:10.1016/j.eswa.2010.02.102

Cited by 428 publications

(232 citation statements)

References 12 publications

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“…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%

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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Section: Methodsmentioning

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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“…In study of Wang et al (2010), the authors suggested a method which is based upon Artificial Neural Network and fuzzy clustering which according to them improves ANN-based IDS and aims to resolve the following two problems: i) less recognition accuracy ii)recognition stability. Their proposed method has the following three stages.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficacious method of detecting DDoS using artificial neural networks

Aljumah

2017

Int. j. adv. appl. sci

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DDoS has evolved as most common and devastating attack that has been confronted from previous years. Since hundreds and thousands of network replies, mostly RREP work together simultaneously to accomplish DDoS attack. Thus, no information system can tolerate and survive once they confront this ruthless attack and there are many existing intrusion detection systems to prevent and protect system as well as network from DDoS but still DDoS is still complex to detect and perplexing. In this research article, we have developed an IDS based on basics of latency and delays in neural networks. In order to form a multi-layer architecture, every node is kept on surveillance once the detectors are deployed in the network topology and the activities of every single node is tracked by their close hop nodes mutually to ensure their status of survival. Only after all of the information is collected in a table is forwarded for integrated analysis by their selected expert module. The nodes covered in first and second layer of firewall experience some suspected packets or streams as that of DDoS pattern and the core expert module that started right after the 2 nd firewall will take some effective action and invoke the defense module to ensure the safety of the information system. And the nodes which didn't stood against defense module will be isolated first and rebooted later to ensure the normal functionality of the network.

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“…have been used on KDD CUP 1999 data for Intrusion Detection [1][2][3][4][5][6][7][8][9][10], with neural networks as main tool in this type of problem. Different neural network algorithms have been used, including Grey Neural Networks [4], RBF [10,11] Recirculation Neural Networks [2], PCA [6,12] and MLP [5], with MLP generally showing better results than others [2].These works are mainly focusing on misuse detection.…”

Section: Related Workmentioning

confidence: 99%

“…It is therefore very important to reduce both types of these alarms, and the best way to do it is by combining anomaly and misuse detection [5,8].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System using Fuzzy Logic

Selman¹

2013

scjournal

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Intrusion detection plays an important role in today's computer and communication technology. As such it is very important to design time efficient Intrusion Detection System (IDS) low in both, False Positive Rate (FPR) and False Negative Rate (FNR), but high in attack detection precision. To achieve that, this paper proposes IDS model based on Fuzzy Logic. Proposed model consists of three parts, Input Reduction System (IRS), which uses Principal Component Analysis to reduce the dimensions of the system from 41 to 10, Classification System, which uses Fuzzy C Means to create data clusters based on training data and Pattern Recognition System based on Nearest Neighborhood method, which classifies new-coming data records to their respective clusters. Based on different attack types, the system performance in classification process is different and the best performance is achieved for PROBE attack, with 99.3% success rate, and the best performance in pattern recognition is achieved for U2R with 58.8% of success rate.

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A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering

Cited by 428 publications

References 12 publications

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

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

An efficacious method of detecting DDoS using artificial neural networks

Intrusion Detection System using Fuzzy Logic

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