Attack classification using feature selection techniques: a comparative study

Thakkar, Ankit; Lohiya, Ritika

doi:10.1007/s12652-020-02167-9

Cited by 100 publications

(38 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[44] in the case wherein these datasets are unbalanced. To address imbalance in training data, researchers focus on feature selection specifically, as in [43,45,46]. Many research groups have followed the success of machine learning, integrating machine learning into NIDS to leverage attack detection quality.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [45] have made comparisons between several machine learning and deep learning algorithms, including decision tree, random forest, K-nearest neighbors, logistic regression, naïve Bayes, support vector machine, and ANN. However, the authors focus more on the feature selection problem, so the above classification algorithms have not been evaluated thoroughly.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

Nguyen

Tran

et al. 2022

Electronics

View full text Add to dashboard Cite

The increasing popularity of the Internet of Things (IoT) has significantly impacted our daily lives in the past few years. On one hand, it brings convenience, simplicity, and efficiency for us; on the other hand, the devices are susceptible to various cyber-attacks due to the lack of solid security mechanisms and hardware security support. In this paper, we present IMIDS, an intelligent intrusion detection system (IDS) to protect IoT devices. IMIDS’s core is a lightweight convolutional neural network model to classify multiple cyber threats. To mitigate the training data shortage issue, we also propose an attack data generator powered by a conditional generative adversarial network. In the experiment, we demonstrate that IMIDS could detect nine cyber-attack types (e.g., backdoors, shellcode, worms) with an average F-measure of 97.22% and outperforms its competitors. Furthermore, IMIDS’s detection performance is notably improved after being further trained by the data generated by our attack data generator. These results demonstrate that IMIDS can be a practical IDS for the IoT scenario.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

Nguyen

Tran

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…In this study, the L1-norm SVM algorithm was used for the statistical and threshold-based feature selection algorithm since it provided high classification performance in many existing methods [25][26][27]. The Chi-square is the most used correlation-based feature selection algorithm [28][29][30][31][32][33]. The features that were obtained from both algorithms were stacked in a vector.…”

Section: Multi-level Feature Selectionmentioning

confidence: 99%

A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

Demir

Siddique

Alswaitti

et al. 2022

JPM

View full text Add to dashboard Cite

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

show abstract

“…Feature selection is a method of selecting relevant features of a dataset by obtaining a subset from the original feature set based on specific criteria. Data dimensionality reduction is often applied to high-dimensional complex data [ 8 ]. Unlike feature extraction, feature selection preserves the physical meaning of the original features by retaining some of the data, and thus makes the model more readable and interpretable [ 9 , 10 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Song

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior and leading to high false-positive and false-negative rates. To alleviate the impact of class imbalance on the detection accuracy of network intrusion detection models and improve their effectiveness, this paper proposes a method based on a feature selection-conditional Wasserstein generative adversarial network (FCWGAN) and bidirectional long short-term memory network (BiLSTM). The method uses the XGBoost algorithm with Spearman’s correlation coefficient to select the data features, filters out useless and redundant features, and simplifies the data structure. A conditional WGAN (CWGAN) is used to generate a small number of samples in the dataset, add them to the original training set to supplement the dataset samples, and apply BiLSTM to complete the training of the model and realize the classification. In comparative tests based on the NSL-KDD and UNSW-NB15 datasets, the accuracy of the proposed model reached 99.57% and 85.59%, respectively, which is 1.44% and 2.98% higher than that of the same type of CWGAN and deep neural network (CWGAN-DNN) model, respectively.

show abstract

Attack classification using feature selection techniques: a comparative study

Cited by 100 publications

References 47 publications

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT

A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Contact Info

Product

Resources

About