Weighted feature extraction using a genetic algorithm for intrusion detection

Middlemiss, Melanie; Dick, Grant

doi:10.1109/cec.2003.1299873

Cited by 25 publications

(21 citation statements)

References 6 publications

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“…(Liao and Vumeri, 2002) [10] proposed k-nearest neighbor method for feature selection in KDD99 dataset. (Middlemiss and Dick, 2003) [9] used KNN in conjunction with genetic algorithm to classify the normal and abnormal behavior. The 41 attributes rendered by KDD99 dataset were ranked by [14] through support vector machines and they again combined SVM and neural networks in [15] to rank the features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Chadha

Jain

2015

Intelligent Distributed Computing

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Abstract.With the drastic increase in internet usage, various categories of attacks have also evolved. Conventional intrusion detection techniques to counter these attacks have failed and thus substantial systems are needed to eliminate these attacks before they inflict huge damage. With the ability of computational intelligence systems to adapt, exhibit fault tolerance, high computational speed and error resilience against noisy information, a hybrid genetic fuzzy rule based inference engine has been designed in this paper. The fuzzy logic constructs precise and flexible patterns while the genetic algorithm based on evolutionary computation helps in attaining an optimal solution, thus their collaboration will increase the robustness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal behavior as well as anomalies in the network. Detailed analysis has been done on DARPA-KDD99 dataset to specify the behavior of each connection.

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Section: Related Workmentioning

confidence: 99%

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Chadha

Jain

2015

Intelligent Distributed Computing

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“…Recently, the PSO is commonly be used for searching the optimal solution [23], [25]. The best position of each particle and best position of the group are estimated by the fitness function Fit( ).…”

Section: B the Proposed Pwknnmentioning

confidence: 99%

“…Typical KNN is a fast and non-training classification algorithm that computes correlations of each known vector and uses nearest ksamples to classify unknown vectors. Each feature of KNN is equal importance which means each feature equally affect classification results [22], [23]. For example, an unknown vector is in a feature space, which contains three types of vectors, Types A, B and C, in Fig.1.…”

Section: Introductionmentioning

confidence: 99%

Detection of Gearbox lubrication Using PSO-Based WKNN

Lee¹,

Kuo²,

Liu³

et al. 2013

Measurement Science Review

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This paper proposes an optimization classification model, which combines particle swarm optimization (PSO) with weighted knearest neighbors (WKNN), namely PWKNN. The model optimizes the weight and k parameter of WKNN to improve the detection accuracy of gearbox lubrication levels. In the experiment, the current signals of the generator are measured, and the relative frequency spectrum of the measured signals is illustrated by using fast Fourier transform (FFT). The features from the spectrum are extracted, and then the optimal weight and k parameter of WKNN are obtained by using PSO. The average detection accuracy of gearbox lubrication levels is 96% by using PWKNN, which the result shows that the proposed PWKNN can efficiently detect the lubrication level of gearboxes. The experiment also shows that the performance of the proposed PWKNN by using the current signals of the generator is superior to that by using typical vibration signals of a gearbox. In addition, the accuracy can reach 95.4% even in environments with 20 dB noise interference.

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“…In past studies, some anomaly-based NIDSs focused on the feature weighting and selection, such as Mukkamala and Sung (2002), Sung and Mukkamala (2003), Lee et al (2006), Abbes et al (2004), Stein et al (2005), Hofman et al (2004), Middlemiss and Dick, (2003), Liao and Vemuri (2002). Mukkamala and Sung (2002) applied the Support Vector Machine (SVM) technique to rank the 41 features provided by KDD CUP99 (The UCI KDD Archive).…”

Section: Introductionmentioning

confidence: 99%

Using clustering to improve the KNN-based classifiers for online anomaly network traffic identification

2011

Journal of Network and Computer Applications

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Weighted feature extraction using a genetic algorithm for intrusion detection

Cited by 25 publications

References 6 publications

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks

Detection of Gearbox lubrication Using PSO-Based WKNN

Using clustering to improve the KNN-based classifiers for online anomaly network traffic identification

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