Application of Machine Learning Approaches in Intrusion Detection System: A Survey

Haq, Nutan Farah; Onik, Abdur Rahman; Hridoy, Md. Avishek Khan; Rafni, Musharrat; Shah, Faisal Muhammad; Farid, Dewan Md.

doi:10.14569/ijarai.2015.040302

Cited by 63 publications

(16 citation statements)

References 45 publications

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“…Avishek Khan Hridoy ,Musharrat Rafni ,Faisal Muhammad Shah Dewan Md. Farid [9]given a survey named "Application of Machine Learning Approaches in Intrusion Detection System: A Survey". System security is one of the significant worries of the cutting edge time.…”

Section: Viiiliterature Survey Mouhammadmentioning

confidence: 99%

Intrusion Detection using Machine Learning

2019

IJRTE

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System savage technicians work to keep administrations accessible every time by dealing with gatecrasher assaults. Interruption Recognition System (IRS) is one of the possible components that is used to detect and order any anomalous activities. In this manner, the IRS must be dependably fully informed regarding the most recent gatecrasher assaults marks to save privacy, trustworthiness, and accessibility of administrations. The fast of IRS is an imperative problem. This examination work represents how the Knowledge Disclosure and Data Mining (or Knowledge Discovery in Databases) The CART and RBFN have been picked for this investigation. It has been demonstrated that the CART classifier has accomplished the most elevated exactness rate for distinguishing and arranging all KDD dataset assaults, which are of sort DOS, R2C, C2R, and Test

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Section: Viiiliterature Survey Mouhammadmentioning

confidence: 99%

Intrusion Detection using Machine Learning

2019

IJRTE

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“…As the focus of network security, network intrusion detection has gained intensive discussion and wide investigations in recent years [14]. Most of them depend on a set of manual rules, which are still unable to identify emerging diverse attacks intelligently.…”

Section: Related Workmentioning

confidence: 99%

Machine learning‐based IDS for software‐defined 5G network

Zhao

2018

IET Networks

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Abstract:As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing centralized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without collaboration. They are also unable to detect novel attacks with limited intelligent abilities, which are hard to meet the needs of software defined 5G. In this paper, we propose an intelligent intrusion system taking the advances of software defined technology and artificial intelligence based on Software Defined 5G architecture. It flexibly combines security function modules which are adaptively invoked under centralized management and control with a globle view. It can also deal with unknown intrusions by using machine learning algorithms. Evaluation results prove that the intelligent intrusion detection system achieves a better performance. IntroductionSoftware Defined 5G architecture will be a crucial tendency in the development of future 5G networks [1]. It takes the advantage of Software Defined Network (SDN) [2] and Network Functions Virtualization (NFV) [3] through centralized management and dynamic resource allocation to meet the demands of 5G networks. Besides, the separation of the control and execution planes also facilitate the supervision of network status and the collection of information. With the uprising of novel technologies and attacks, it will also be faced with various challenges and severe security situations. As a result, new network security systems and architectures are desperately needed to enhance the security of Software Defined 5G networks [4].As an essential technology in network security, intrusion detection systems have received more and more concerns in efficiently detecting malicious attacks. Existing IDS with separate functions are usually deployed locally within restricted areas which are hard to cooperate with each other. Moreover, they are usually signaturebased by matching behaviors of incoming intrusions with historical knowledge and predefined rules, which are unable to detect novel attacks intelligently.To overcome the limitation of traditional IDS, Artificial Intelligence (AI) has been employed for intelligent detection. They classify abnormal traffic using machine learning techniques with a selflearning ability [5]. At present, there have been a few researches in the combinations of IDS and AI. However, they are still inadequate for coordinated detection considering the evolution and development of network systems.In this paper, we propose an intelligent intrusion detection system for Software Defined 5G networks. Benefit from the Software Defined technology, it integrates relevant security function modules into a unified platform which are dynamically invoked under centralized management and control. Besides, it implements machine learning to intelligently learn rules fro...

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“…In anomaly detection, the system builds a profile of that which can be considered as normal or expected usage patterns over a period of time and triggers alarms for anything that deviates from this behavior (Adel, Zeynep & Adnan, 2014) [1]. The misuse detection is used to identify attacks in a form of signature or pattern [13]. As misuse detection uses the known pattern to detect attacks the main disadvantage is that it will fail to identify any unknown attacks to the network or system [13].…”

Section: Introductionmentioning

confidence: 99%

“…The misuse detection is used to identify attacks in a form of signature or pattern [13]. As misuse detection uses the known pattern to detect attacks the main disadvantage is that it will fail to identify any unknown attacks to the network or system [13].…”

Section: Introductionmentioning

confidence: 99%

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An Analytical Comparison on Filter Feature Extraction Method in Data Mining using J48 Classifier

Onik¹,

Haq²,

Alam³

et al. 2015

IJCA

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The feature selection approach provides improved prediction and minimizes the computation time. Due to the higher numbers of features the understanding of the data in pattern recognition becomes difficult sometimes. That's why researchers have used different feature selection techniques with the single classifiers in their intrusion detection system to build up a model which gives a better accuracy and prediction performance. In this paper, we provide a comparative analysis with the feature selection approach in WEKA machine learning tool using the J48 classifier. The research work show the comparison of the performance of single J48 classifier with filter methods. The prediction performance may differ marginally in some cases but with the removal of irrelevant features time complexity can be easily ignored and a better prediction rate is guaranteed.

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Application of Machine Learning Approaches in Intrusion Detection System: A Survey

Cited by 63 publications

References 45 publications

Intrusion Detection using Machine Learning

Intrusion Detection using Machine Learning

Machine learning‐based IDS for software‐defined 5G network

An Analytical Comparison on Filter Feature Extraction Method in Data Mining using J48 Classifier

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