A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering

Kumari, V. Valli; Varma, P. Ravi Kiran

doi:10.1109/i-smac.2017.8058397

Cited by 46 publications

(17 citation statements)

References 19 publications

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“…A hybrid semisupervised learning technique was introduced using the Active smart vector learning machine (ASVM) and Fuzzy C-Means (FCM) in the design of an intrusion detection system that has an excellent performance. This system is considered as a binary classification and hence works faster than multi-classifiers [24]. Li et al [27] presented a new hybrid method based on the density peaks clustering and k nearest neighbors in order to increase the accuracy rate that DPNN was used to train, and kNN was used for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Classification for Intrusion Detection via Feature Extraction Based on Deep Learning

Yousefnezhad

Hamidzadeh

Aliannejadi³

2021

Preprint

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An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at thesame time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster-Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSLKDD. Our proposed method indicate the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure.

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Section: Literature Reviewmentioning

confidence: 99%

Ensemble Classification for Intrusion Detection via Feature Extraction Based on Deep Learning

Yousefnezhad

Hamidzadeh

Aliannejadi³

2021

Preprint

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“…With the development of machine learning, Valli Kumari V and Ravi Kiran Varma P have proposed a semisupervised intrusion detection system using active learning SVM and fuzzy c-means clustering [26]. This work demonstrates a hybrid semisupervised machine learning technique that uses active learning support vector machine (ASVM) and fuzzy C-means (FCM) clustering in an efficient IDS design.…”

Section: Related Workmentioning

confidence: 99%

CCID: Cross-Correlation Identity Distinction Method for Detecting Shrew DDoS

Cheng

Zou

et al. 2019

Wireless Communications and Mobile Computing

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This study presents a new method for detecting Shrew DDoS (Distributed Denial of Service) attacks and analyzes the characteristics of the Shrew DDoS attack. Shrew DDoS is periodic to be suitable for the server’s TCP (Transmission Control Protocol) timer. It has lower maximum to bypass peak detection. This periodicity makes it distinguishable from normal data packets. By proposing the CCID (Cross-Correlation Identity Distinction) method to distinguish the flow properties, it quantifies the difference between a normal flow and an attack flow. Simultaneously, we calculated the cross-correlation between the attack flow and the normal flow in three different situations. The server can use its own TCP flow timer to construct a periodic attack flow. The cross-correlation between Gaussian white noise and simulated attack flow is less than 0.3. The cross-correlation between single-door function and simulated attack flow is 0.28. The cross-correlation between actual attack flow and simulated attack flow is more than 0.8. This shows that we can quantitatively distinguish the attack effects of different signals. By testing 4 million data, we can prove that it has a certain effect in practice.

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“…The results of the experiments on intrusion detection datasets of KDDCUP 99 show that the proposed method is effective. [27] demonstrated a hybrid semisupervised machine learning technique that uses Active learning Support Vector Machine (ASVM) and Fuzzy C-Means (FCM) clustering in the design of an efficient IDS. There are primarily two phases in the proposed approach, if the SVM classifier has decided the input sample under test to be abnormal, then FCM is used to find the sub categories based on the previously generated FCM clusters in the training phase.…”

Section: Yang LI and Limentioning

confidence: 99%

Active Learning in Cyberspace Security

Zhang¹,

Liu²,

Cui³

et al. 2018

Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018

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At present, machine learning, especially active learning in machine learning, is introduced in cyberspace security field. In this paper, we first analyze the background of cyberspace security and profile active learning. And then we focus on a survey on the application research of active learning in cyberspace security field, mainly including system software security, network security and application security. In the end, we also explore some potential future issues on active learning in cyberspace security.

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A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering

Cited by 46 publications

References 19 publications

Ensemble Classification for Intrusion Detection via Feature Extraction Based on Deep Learning

Ensemble Classification for Intrusion Detection via Feature Extraction Based on Deep Learning

CCID: Cross-Correlation Identity Distinction Method for Detecting Shrew DDoS

Active Learning in Cyberspace Security

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