Design of Intrusion Detection System using Fuzzy Class-Association Rule Mining based on Genetic Algorithm

Dhopte, S. V.; Tarapore, Noshir

doi:10.5120/8489-2436

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…In this case, the requests are partial HTTP requests. The attack lasts until all available sockets are reserved by the HTTP requests, causing the server to freeze in response to any legitimate connection [24]. The final attack is the Slowpost attack.…”

Section: Snmp-mib Datasetmentioning

confidence: 99%

“…The rapid growth of intrusion techniques poses a challenge for binarydecision based detection approaches. The crisp set [24] is not suitable for expressing the level of attack, which could improve the expression capabilities of the modern intrusion techniques. The fuzzy system also provide the required boundary smoothing because it can also deal with approximation, not only with precise values.…”

Section: Fuzzy Rule Interpolation (Fri)mentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Rule Interpolation and SNMP-MIB for Emerging Network Abnormality

Almseidin

Alkasassbeh

Kovács

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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It is difficult to implement an efficient detection approach for Intrusion Detection Systems (IDS) and many factors contribute to this challenge. One such challenge concerns establishing adequate boundaries and finding a proper data source. Typical IDS detection approaches deal with raw traffics. These traffics need to be studied in depth and thoroughly investigated in order to extract the required knowledge base. Another challenge involves implementing the binary decision. This is because there are no reasonable limits between normal and attack traffics patterns. In this paper, we introduce a novel idea capable of supporting the proper data source while avoiding the issues associated with the binary decision. This paper aims to introduce a detection approach for defining abnormality by using the Fuzzy Rule Interpolation (FRI) with Simple Network Management Protocol (SNMP) Management Information Base (MIB) parameters. The strength of the proposed detection approach is based on adapting the SNMP-MIB parameters with the FRI. This proposed method eliminates the raw traffic processing component which is time consuming and requires extensive computational measures. It also eliminates the need for a complete fuzzy rule based intrusion definition. The proposed approach was tested and evaluated using an open source SNMP-MIB dataset and obtained a 93% detection rate. Additionally, when compared to other literature in which the same test-bed environment was employed along with the same number of parameters, the proposed detection approach outperformed the support vector machine and neural network. Therefore, combining the SNMP-MIB parameters with the FRI based reasoning could be beneficial for detecting intrusions, even in the case if the fuzzy rule based intrusion definition is incomplete (not fully defined).

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Section: Snmp-mib Datasetmentioning

confidence: 99%

Section: Fuzzy Rule Interpolation (Fri)mentioning

confidence: 99%

Fuzzy Rule Interpolation and SNMP-MIB for Emerging Network Abnormality

Almseidin

Alkasassbeh

Kovács

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…where s * is the support degree of a new rule formed by genetic operation; s min is the pre-given support threshold. If fit(R) > 0, this rule will be kept into the next generation; if fit(R) < 0, this rule will be eliminated in inheritance [20]. The main parameters of the genetic algorithm are set as follows, population size: 100, evolution algebra: 200.…”

Section: ) Net Income Of the Pessmentioning

confidence: 99%

“…The matrix A 1 is substituted into (20) to solve the eigenvector corresponding to the maximum eigenvalue λ = max(x), the weight sequence of the index {WA i } 1×4 (i = 1, 2, . .…”

Section: B Overall Economic Evaluation Of the Pess Based On Entropy Weight-ahpmentioning

confidence: 99%

Planning and Overall Economic Evaluation of Photovoltaic-Energy Storage Station Based on Game Theory and Analytic Hierarchy Process

et al. 2019

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With the application of energy storage systems in photovoltaic power generation, the selection and optimal capacity configuration of energy storage batteries at photovoltaic-energy storage stations (PESS) are becoming more and more important. Aiming at the overall economics of the PESS in the scenario of tracking the planning output, a capacity configuration and economic evaluation method of the PESS based on game theory and analytic hierarchy process (AHP) is proposed. Taking the minimum discarded photovoltaic power and the maximum net income of the PESS as objective functions, a comprehensive model of the PESS is built according to the historical data of the PESS. Combined with the characteristics of different electrochemical energy storage batteries, the relationships among the net income of the PESS, the depths of discharge (DODs) and the capacity of the energy storage batteries are analyzed. When the amount of the discarded photovoltaic power is zero, a selection and capacity configuration model of the energy storage batteries based on game theory is established, and the firefly algorithm (FA) is adopted to solve the model. Starting from the comprehensive evaluation indexes of the PESS, the hierarchical analysis structure under different energy storage schemes is discussed, and the AHP is employed to realize the overall economic evaluation of the PESS, in which the weight of the evaluation indexes in different energy storage schemes is calculated by the entropy weight method. The effectiveness and correctness of the proposed method are verified through the simulation analysis of the actual operation data of a certain PESS in China. The simulation results show that the selection and optimal capacity configuration of the energy storage batteries have an important impact on the overall economics of the PESS, and the overall economics of the PESS obtained by three types of electrochemical energy storage batteries is the highest income of 249.5 Million $, which can provide a theoretical basis for the planning of the PESS and has certain engineering application prospects.

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“…If compromised, appropriate corrective actions were taken using negative information sharing and independent trust-based decision making. Dhopte and Tarapore (2012) worked with fuzzy class association rule mining based on genetic algorithm to design an intrusion detection system which proved to be more accurate when compared with the use of Cross Industry Standard Process Data Mining (CRISP-DM) Approach. The system which could be applied to both misuse and anomaly intrusion detection used the KDD '99 cup dataset to train and test the system while fuzzy set theory was combined with association rule mining algorithm to extract the rules with attributes of continuous value.…”

Section: Introductionmentioning

confidence: 99%

Modelling of an Intrusion Detection System Using C4.5 Machine Learning Algorithm

Olanrewaju

Echobu²,

Mogaji³

2021

FJS

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The increasing growth of wireless networking and new mobile computing devices has caused boundaries between trusted and malicious users to be blurred. The shift in security priorities from the network perimeter to information protection and user resources security is an open area for research which is concerned with the protection of user information’s confidentiality, integrity and availability. Intrusion detection systems are programs or software applications embedded in sophisticated devices to monitor the activities on networks or systems for security, policy or protocol violation or malicious activities detection. In this work, an intrusion detection model was proposed using C4.5 algorithm which was implemented with WEKA tool and RAPID MINER. The model showed good performance when trained and tested with validation techniques. Implementation of the proposed model was conducted on the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) dataset, an improved version of KDD 99 dataset, which showed that the proposed model approach has an average detection rate of 99.62% and reduced false alarm rate of 0.38%.

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Design of Intrusion Detection System using Fuzzy Class-Association Rule Mining based on Genetic Algorithm

Cited by 10 publications

References 22 publications

Fuzzy Rule Interpolation and SNMP-MIB for Emerging Network Abnormality

Fuzzy Rule Interpolation and SNMP-MIB for Emerging Network Abnormality

Planning and Overall Economic Evaluation of Photovoltaic-Energy Storage Station Based on Game Theory and Analytic Hierarchy Process

Modelling of an Intrusion Detection System Using C4.5 Machine Learning Algorithm

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