Hybrid architecture with misuse and anomaly detection techniques for wireless networks

Balan, E. Vishnu; Kumar, Priyan Malarvizhi; Devi, G. Usha

doi:10.1109/iccsp.2015.7322846

Cited by 15 publications

(2 citation statements)

References 11 publications

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“…The random forest generates many random trees to classify the attacks. Balan et al 18 had used genetic programming, multi-expression programming, and Gene expression programming on DARPA dataset for attack detection. Linear genetic program is an extended version of genetic program, it focuses mainly on tree-based approach for evaluation of input to get the desired output.…”

Section: Literature Surveymentioning

confidence: 99%

An empirical model in intrusion detection systems using principal component analysis and deep learning models

Rajadurai

Gandhi

2020

Computational Intelligence

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Data are a main resource of a computer system, which can be transmitted over network from source to destination. While transmitting, it faces lot of security issues such as virus, malware, infection, error, and data loss. The security issues are the attacks that have to be detected and eliminated in efficient way to guarantee the secure transmission. The attack detection rates of existing Intrusion Detection Systems (IDS) are low, because the number of unknown attacks are high when compared to the known attacks in the network. Thus, recent researchers focus more on evaluation of known attacks attributes, that will help in identification of the attacks. But the difficulty here is the nature of the IDS datasets. The difficulty in any IDS dataset is to, too many attributes, irrelevant and unstructured in nature. So analyzing such attributes leads to a time consuming process and that produces an inefficient result. This article presents a combined approach Principle Component Analysis and Deep learning (PCA-DL) model to address above issues. The proposed PCA-DL method has achieved the accuracy 92.6% on detecting the attacks correctly.

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Section: Literature Surveymentioning

confidence: 99%

An empirical model in intrusion detection systems using principal component analysis and deep learning models

Rajadurai

Gandhi

2020

Computational Intelligence

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“…26 In this context, DNA microarrays were used to separate patients without disease from F I G U R E 2 General feature selection method oncological ones, based on their "gene expression" profile (two class problems), or to distinguish between multiclass problems. 27 These types of data provide a very huge amount of features: for each patient, in fact, each feature represents a gene expression coefficient corresponding to the abundance of mRNA in a concrete sample. 28 The number of features in the raw data is expected to be around 5000 to 50 000, while the available datasets usually refer to less than 100 patients.…”

Section: Related Workmentioning

confidence: 99%

Forest optimization algorithm‐based feature selection using classifier ensemble

Moorthy

Gandhi

2019

Computational Intelligence

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Features selection is the process of choosing the relevant subset of features from the high-dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high-dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble-based algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR-FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%.

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