Intrusion Detection System Based on Improved SVM Incremental Learning

Du, Hongle; Teng, Shaohua; Yang, Ming; Zhu, Q.A.

doi:10.1109/aici.2009.254

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…Secondly, for the reason that IMCSVM is an incremental learning method, we compared our model with the five different baselines about incremental learning. For the new data from old classes, we compared the IMC with the All data, RS-ISVM for multiclass classification [27], standard ISVM for multiclass classification [3], and IRotBoost [24]. For the new data from new classes, we compared the IMC with the All data, CIELM [36], MULTIpLE [37], and SVMF-RTST [5].…”

Section: Methodsmentioning

confidence: 99%

“…However, incremental learning is more used in target recognition [4] and image classification [5] and less used in the field of information security. For example, Du et al [24] present the incremental support vector machine learning algorithm based on clustering for intrusion detection. Elham et al [25] propose an incremental ensemble learning for spam filtering.…”

Section: Incremental Learningmentioning

confidence: 99%

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Incremental Learning for Malware Classification in Small Datasets

Xue

et al. 2020

Security and Communication Networks

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Information security is an important research area. As a very special yet important case, malware classification plays an important role in information security. In the real world, the malware datasets are open-ended and dynamic, and new malware samples belonging to old classes and new classes are increasing continuously. This requires the malware classification method to enable incremental learning, which can efficiently learn the new knowledge. However, existing works mainly focus on feature engineering with machine learning as a tool. To solve the problem, we present an incremental malware classification framework, named “IMC,” which consists of opcode sequence extraction, selection, and incremental learning method. We develop an incremental learning method based on multiclass support vector machine (SVM) as the core component of IMC, named “IMCSVM,” which can incrementally improve its classification ability by learning new malware samples. In IMC, IMCSVM adds the new classification planes (if new samples belong to a new class) and updates all old classification planes for new malware samples. As a result, IMC can improve the classification quality of known malware classes by minimizing the prediction error and transfer the old model with known knowledge to classify unknown malware classes. We apply the incremental learning method into malware classification, and the experimental results demonstrate the advantages and effectiveness of IMC.

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Section: Methodsmentioning

confidence: 99%

Section: Incremental Learningmentioning

confidence: 99%

Incremental Learning for Malware Classification in Small Datasets

Xue

et al. 2020

Security and Communication Networks

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“…Aiming at the problem that the speeds of learning and classifying are slow in large training set, researchers have proposed many improved SVM algorithms. For example, the literature and combined clustering algorithm with SVM. Much like traditional SVM, our proposed WCS‐SVM increases the training time as the scale of training set enlarges.…”

Section: Methodsmentioning

confidence: 99%

A new intrusion detection method based on SVM with minimum within‐class scatter

An¹,

Liang²

2012

Security Comm Networks

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Intrusion detection has become an indispensable technique to ensure the security and reliability of information systems. Support vector machine (SVM) and its many improved algorithms have been successfully applied to intrusion detection systems in recent years. However, the training process of SVM ignores an important prior knowledge, the within‐class structure in the training set. In this paper, we propose an improved classification algorithm, which combines minimum within‐class scatter in Fisher discriminant analysis with traditional SVM. The central idea is to find an optimal separating hyperplane such that the margin is maximized, whereas the within‐class scatter is kept as small as possible. This new algorithm is called SVM with minimum within‐class scatter (WCS‐SVM). A set of experiments is conducted on ten benchmarking datasets and KDDCUP'99 experimental data of MIT Lincoln Laboratory to test the generalization performance of the WCS‐SVM algorithm. Experimental results show that the WCS‐SVM algorithm has better discriminatory power than traditional SVM and kernel Fisher discriminant analysis and it has higher true detection rate and lower false positive rate for intrusion detection systems. Copyright © 2012 John Wiley & Sons, Ltd.

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“…It based on the close degree and the discrete degree of the same cluster. Make sure that data has higher similarity in the same cluster while having higher difference among different clusters [8].…”

Section: Determine the Remaining Cluster Centermentioning

confidence: 99%

Study of Local Monitoring System Based on SMS Under the Cloud Environment

Zhang

Zou

2015

Ubiquitous Computing Application and Wireless Sensor

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To enhance the overall safety of cloud service and ensure the data integrity and to ease the burden of monitoring system, cluster analysis is introduced into the paper to divide users into different trust domain. The monitoring system's objects only include special users and users whose trust domain is minimal. The system has function of SMS alarm, and it permits users to recover the data by means of smart mobile phone. The system takes both special users and general users into consideration and ensures the reliability of the service and enhances the experience of users.

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Intrusion Detection System Based on Improved SVM Incremental Learning

Cited by 13 publications

References 10 publications

Incremental Learning for Malware Classification in Small Datasets

Incremental Learning for Malware Classification in Small Datasets

A new intrusion detection method based on SVM with minimum within‐class scatter

Study of Local Monitoring System Based on SMS Under the Cloud Environment

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