Network intrusion detection system by using  genetic algorithm

Suhaimi, Hamizan; Suliman, Saiful Izwan; Musirin, Ismail; Harun, Afdallyna Fathiyah; Mohamad, Rosfarizan

doi:10.11591/ijeecs.v16.i3.pp1593-1599

Cited by 13 publications

(10 citation statements)

References 23 publications

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“…For simplicity, the minimum and maximum value of noise can be set to 0 and 1 assuming that noise level becomes 0 when bats stop emitting sound (because the bats have already found their prey). This activity can be modeled by the mathematical representation in (10) and 11…”

Section: Pi Controllermentioning

confidence: 99%

“…Pulse emission and a constant value in (10) and (11) are presented as ri, α and γ. For every 0< α <1 and γ>0, the mathematical representation can be described in (12).…”

Section: Pi Controllermentioning

confidence: 99%

“…Furthermore, ACO-NM algorithm could provide high quality solutions for lower computational cost. A genetic algorithm was used to detect the network intrusions [10]. An attempt to use firefly algorithm for placing reactive power compensation in distribution network is reported in [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of spark ignition engine speed control using bat algorithm

Setiadi

Jones

Nugroho

et al. 2021

IJECE

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The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.

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Section: Pi Controllermentioning

confidence: 99%

“…Pulse emission and a constant value in (10) and (11) are presented as ri, α and γ. For every 0< α <1 and γ>0, the mathematical representation can be described in (12).…”

Section: Pi Controllermentioning

confidence: 99%

See 1 more Smart Citation

Design of spark ignition engine speed control using bat algorithm

Setiadi

Jones

Nugroho

et al. 2021

IJECE

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“…Parallel RGSOM-CRV method outperformed the regular GSOM, as it reached up to 91.86% ACC, 20.58% FAR, 95.32% DTR or Recall, and Precision up to 94.35%. The authors of paper [12] proposed a genetic algorithm (GA) as a tool that able to identify harmful types of connections in a computer network, as it analyzed features of connection data and types of connection in the network to generate a set of classification rules. The proposed method uses the combination of genetic operators which are cloning, crossover, and mutation processes to generate new chromosomes.…”

Section: Introductionmentioning

confidence: 99%

BotDetectorFW: an optimized botnet detection framework based on five features-distance measures supported by comparisons of four machine learning classifiers using CICIDS2017 dataset

Jabbar

Mohammed

2021

IJEECS

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A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver & kroeber, overlap, and pearson correlation) using C#, followed by selecting the best N features used as input into four classifier algorithms evaluated using machine learning (WEKA); multilayerperceptron, JRip, IBK, and random forest. In BotDetectorFW, the thoughtful and diligent cleaning of the dataset within the preprocessing stage beside the normalization, binary clustering of its features, followed by the adapting of feature selection based on suitable feature distance techniques, and finalized by testing of selected classification algorithms. All together contributed in satisfying the high-performance metrics using fewer features number (8 features as a minimum) compared to and outperforms other methods found in the literature that adopted (10 features or higher) using the same dataset. Furthermore, the results and performance evaluation of BotDetectorFM shows a competitive impact in terms of classification accuracy (ACC), precision (Pr), recall (Rc), and f-measure (F1) metrics.

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“…Many pieces of research [14] show that many existing prevention mechanisms are not sufficient to secure the data packets of the network and maintain the service of WSN [15]. They also cannot prevent the network from all the attacks experienced by them.…”

Section: Introductionmentioning

confidence: 99%

Data mining approach to analyzing intrusion detection of wireless sensor network

Rezvi

Moontaha

Trisha

et al. 2021

IJEECS

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Wireless sensor network (WSN) is a collection of wireless sensor nodes which are distributed in nature and a base station where the dispersed nodes are used to monitor and the physical conditions of the environment is recorded and then these data are organized into the base. Its application has been reached out from critical military application such as battlefield surveillance to traffic, health, industrial areas, intruder detection, security and surveillance. Due to various features in WSN it is very prone to various types external attacks. Preventing such attacks, intrusion detection system (IDS) is very important so that attacker cannot steal or manipulate data. Data mining is a technique that can help to discover patterns in large dataset. This paper proposed a data mining technique for different types of classification algorithms to detect denial of service (DoS) attacks which is of four types. They are Grayhole, Blackhole, Flooding and TDMA. A number of data mining techniques, such as KNN, Naïve Bayes, Logistic Regression, support vector machine (SVM) and ANN algorithms are applied on the dataset and analyze their performance in detecting the attacks. The analysis reveals the applicability of these algorithms for detecting and predicting such attacks and can be recommended for network specialist and analysts.

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Network intrusion detection system by using genetic algorithm

Cited by 13 publications

References 23 publications

Design of spark ignition engine speed control using bat algorithm

Design of spark ignition engine speed control using bat algorithm

BotDetectorFW: an optimized botnet detection framework based on five features-distance measures supported by comparisons of four machine learning classifiers using CICIDS2017 dataset

Data mining approach to analyzing intrusion detection of wireless sensor network

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