Survey on Hybrid Data Mining Algorithms for Intrusion Detection System

Datir, Harshal N.; Jawandhiya, Pradip M.

doi:10.1007/978-981-13-1402-5_22

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Real-time network traffic classification has the capability to deal with the problem of sever network management for Internet Service Providers (ISPs) and their equipment vendors. It is essential for the SSC network operator to keep an eye on network [4], what is flowing on over their networks; thus they can manage and control the system in support of their goals. The end goal is to improve SSC network performance and identify intrusion or malicious of sustainable smart cities traffic.…”

Section: Introductionmentioning

confidence: 99%

“…The end goal is to improve SSC network performance and identify intrusion or malicious of sustainable smart cities traffic. In our previous study, we studied sustainable smart cities and find out effective ML technique for Smart Cities (SM) traffic classification using ML technique [5] based on bijective soft set [6] .Furthermore, traffic classification is also the central part of the automated intrusion detection systems used to detect denial service attacks [4] reallocation of network resources or identify customer resource in a network and then reallocation. In the last decade, IP traffic classification technique is proposed, traditionally the methods have been based on direct inspection of each packet's contents of flows at a specific point on the network.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

Shafiq¹,

Tian²,

Bashir

et al. 2020

Sustainable Cities and Society

181

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

Shafiq¹,

Tian²,

Bashir

et al. 2020

Sustainable Cities and Society

181

View full text Add to dashboard Cite

“…Although anomaly detection is suitable for the detection of new or unknown attacks, its disadvantage is that it cannot detect attacks similar to normal and also generates too many false alarms. Because misuse detection produces signatures or rules, it generates far fewer false alarms, but its disadvantage is that it can detect only known attacks [4].…”

Section: Introductionmentioning

confidence: 99%

Unknown Attack Detection: Combining Relabeling and Hybrid Intrusion Detection

Shin¹,

Kim²,

Kim³

et al. 2021

Computers, Materials &Amp; Continua

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Detection of unknown attacks like a zero-day attack is a research field that has long been studied. Recently, advances in Machine Learning (ML) and Artificial Intelligence (AI) have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully. Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks. Although anomaly detection is adequate for detecting unknown attacks, its disadvantage is the possibility of high false alarms. Misuse detection has low false alarms; its limitation is that it can detect only known attacks. To overcome such limitations, many researchers have proposed a hybrid intrusion detection that integrates these two detection techniques. This method can overcome the limitations of conventional methods and works better in detecting unknown attacks. However, this method does not accurately classify attacks like similar to normal or known attacks. Therefore, we proposed a hybrid intrusion detection to detect unknown attacks similar to normal and known attacks. In anomaly detection, the model was designed to perform normal detection using Fuzzy c-means (FCM) and identify attacks hidden in normal predicted data using relabeling. In misuse detection, the model was designed to detect previously known attacks using Classification and Regression Trees (CART) and apply Isolation Forest (iForest) to classify unknown attacks hidden in known attacks. As an experiment result, the application of relabeling improved attack detection accuracy in anomaly detection by approximately 11% and enhanced the performance of unknown attack detection in misuse detection by approximately 10%.

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