Design of an intrusion detection system based on distance feature using ensemble classifier

Aravind, M.; Kalaiselvi, V.K.G.

doi:10.1109/icscn.2017.8085661

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Using the Gure KDD dataset and the ADTree, k -Means, and k-NN classifiers through weighted majority voting for binary classification, the tests obtained an accuracy of 99.93% and a detection rate of 99.8%. Aravind and Kalaiselvi [43] performed a similar experiment were using several distance metrics. The authors obtained 90% accuracy using k-means in the UNSW-NB15 dataset.…”

Section: Work Published In 2017mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Section: Work Published In 2017mentioning

confidence: 99%

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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show abstract

“…This method subdivides a big problem, and then adopts targeted solutions for each part. The suc-cess of the ensemble method depends on the diversity of the misclassified instances of each subclass-ifier [7]. This paper is oriented to intrusion detection tasks and uses CART as the classifier to constr-uct a Bagging ensemble learning model.…”

Section: Data Balance Processingmentioning

confidence: 99%

Research on intrusion detection method based on feature selection and integrated learning

Tong²,

Wang

et al. 2022

J. Phys.: Conf. Ser.

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With the introduction of computer technology, network attacks have become more frequent. Some illegal elements may intrude into computers through network attacks to tamper with messages, spread viruses and other destructive behaviors, causing great damage to personal sensitive information, industrial control networks, transaction systems, etc. . For this, this design proposes an improved intrusion detection method based on feature selection and integrated model. The NSL-KDD training data set is used to evaluate the proposed model. First, balance the data categories through the SMOTE-ENN method, and then use feature selection technology and PCA feature extraction technology to reduce the number of irrelevant features and improve the classification accuracy. Finally, using CART as the base classifier, Bagging technology is used to establish an integrated model and an intrusion detection system. Experimental results show that the CART-based Bagging method provides better accuracy, lower false alarm rate and faster model training speed, and the system can detect intrusion attacks with similar attributes and has a certain degree of adaptability.

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“…A realistic insight into the future of malicious activities we face reveals a notable direction towards intelligent intrusion detections that have the artificial intelligence to detect the novel, unknown attacks. Intelligent intrusion detection systems lie in one of four major categories, ie, (1) Clustering techniques, 3-7 (2) Genetic algorithms, 8-11 (3) Fuzzy logic, 12-16 and (4) Artificial neural networks, ie, supervised 22-26 and unsupervised 22-26 …”

Section: Introductionmentioning

confidence: 99%

Cascaded hybrid intrusion detection model based on SOM and RBF neural networks

Almiani

AbuGhazleh

Al‐Rahayfeh

et al. 2019

Concurrency and Computation

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Cybercriminal activities over computer network systems are considered one of the preponderant issues that humanity will face in the coming two decades. The development steps in the design of intrusion detection systems must be carried out in analogous manner to sophistication levels of intrusions developed by hackers. This work proposes a layered hybrid intrusion detection model uses cascaded layers of Clustered Self-Organized Map (CSOM) and Radial Basis Function (RBF) neural networks to improve the efficiency of detecting frequent and least frequent intrusions. K-means clustered SOM was used to filter attacks as a first layer, whereas RBF-based neural network worked as second filtering and attacked classification layer leading to significance reduction in time required to process connection records and notable improvements in the performance of intrusion detection. A new balanced version of cleansed NSL-KDD dataset was used to validate and evaluate the proposed model. Compared with other existing schemes; the proposed model shows high detection performance in terms of accuracy 97.73% and false positive rate as low as 0.023%. In particular, for detecting least and most harmful attacks, U2R and R2L, the system achieved detection rate of 88.6% with false positive rate of 0.016. Comparative results showed that CSOM-RBF model is more suitable for real-life implementation than other many existing state-of-the-art intrusion detection models.

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Design of an intrusion detection system based on distance feature using ensemble classifier

Cited by 8 publications

References 15 publications

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Research on intrusion detection method based on feature selection and integrated learning

Cascaded hybrid intrusion detection model based on SOM and RBF neural networks

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