A survey of intrusion detection system

Dali, Loubna; Bentajer, Ahmed; Abdelmajid, Elmoutaoukkil; Karim, Alamgir; El-Sayed, Hoda; Eladnani, Fatiha; Abderahim, Benihssane

doi:10.1109/wswan.2015.7210351

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…In this study, we choose four popular machine learning algorithms which are k-Nearest Neighbors' algorithm (KNN) (Altman, 1992), Adaptive Boosting (AdaBoost) (Gandhi, 2018), Random Decision Forests (RandomForest) (Ho, 1995) and Support Vector Machine (Cortes and Vapnik, 1995) for our model. The motivation is that those algorithms are recent works on applying machine learning to N-IDS and they provide better results and lower processing time compared to others (Khraisat et al, 2019;Dali et al, 2015). In pattern recognition, the k-Nearest Neighbors' algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression (Altman, 1992).…”

Section: Machine Learning Models To Network Anomaly Detectionmentioning

confidence: 99%

“…An expert system is currently the most feasible solution which uses artificial intelligence to solve problems in a field that requires human expertise. The application of machine learning algorithms is a breakthrough that provide us an efficient tool to apply N-IDS in practice and can be found in detail in (Khraisat et al, 2019;Dali et al, 2015). Moreover, Deep learning is a subset of machine learning that outperforms the traditional machine learning by learning to represent the data as a nested hierarchy of concepts.…”

Section: Introductionmentioning

confidence: 99%

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Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

Hai¹,

Khiem²,

Phúc³

2021

Journal of Computer Science

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Section: Machine Learning Models To Network Anomaly Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

Hai¹,

Khiem²,

Phúc³

2021

Journal of Computer Science

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“…So, it is found to be a novel way of detecting intrusions in case of known attacks [2]. But they have failed to identify new attacks which do not exist in the signature and the database needs to be adequately updated to improve the detection rate [3]. For resolving this issue, Anomaly based detection methods perform a comparison of the present client actions over the fixed profile for detecting abnormalities which may be considered an intrusion.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble of Feature Subset Selection With Deep Belief Network Based Secure Intrusion Detection in Big Data Environment

Kumar¹,

Mohan²,

Nagesh³

2021

INDJCSE

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In recent times, massive quantity of data and its exponential rise have modified the significance of data security and analysis systems for Big Data. An intrusion detection system (IDS) is a process which helps to monitor and analyze the data for detecting the intrusions in the network. The huge quantity, variation, and high speed of data produced in the system pose a difficulty to the conventional techniques for the detection of attacks. Big Data methods are utilized in IDS for dealing with Big Data for precise and effective data analytic processes. This paper introduces a novel feature subset selection (FSS) with deep belief network (DBN) based intrusion detection in big data environment, called FSS-DBN model. The presented model involves data preprocessing stage to improve the data quality to a certain extent. In addition, the FSS can be considered as an optimization problem and is effectively solved by the use of teaching and learning based optimization (TLBO) algorithm. Moreover, DBN model is applied for identifying the class labels (i.e. intrusions) in the network. For validating the proficient results analysis of the FSS-DBN model, an extensive set of simulations were performed and the superior performance is also highlighted with the maximum sensitivity of 0.9898, specificity of 0.9865, accuracy of 0.9854, F-score of 0.9872, and kappa of 0.9867.

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“…The public cloud platform is very complicated when contrasted with a conventional data center environment. Based on the model of Cloud computing, an institution or organization gives up direct access to significant features of security, provides a high level of trust over the Cloud provider [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System

Nallamuthu¹

2020

J Comput Sci Intell Technol

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The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

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A survey of intrusion detection system

Cited by 31 publications

References 17 publications

Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

An Ensemble of Feature Subset Selection With Deep Belief Network Based Secure Intrusion Detection in Big Data Environment

A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System

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