A Network Intrusion Detection System Based On Ensemble CVM Using Efficient Feature Selection Approach

Divyasree, T.H.; Sherly, K. K.

doi:10.1016/j.procs.2018.10.416

Cited by 33 publications

(8 citation statements)

References 7 publications

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“…The proposed method ranks feature according to their correlation and information gain. The features selected are collective with a device; therefore, the unique features are involved, and redundant features are rejected [3]. After preprocessing of data based on utilization of machine learning algorithm, this helps in intrusion detection system capable of reading the incoming data, classify them as per whether they are threatening or non-threatening.…”

Section: Application Protocol-based Intrusion Detection Systems (Apids)mentioning

confidence: 99%

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Intrusion Detection System Based on RNN Classifier for Feature Reduction

Deore¹,

Bhosale²

2021

SN COMPUT. SCI.

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Due to the increase in the use of the internet all over the world for business and education activates, cybercrime is increasing day by day in spite of the development of security protocols and algorithms. Recent research is based on the intrusion detection system. We attempt to develop is a secure protocol to detect malicious data, along with actual data, in the incoming data traffic. An intrusion detection system based on a recurrent neural network (RNN) classifier for feature reduction. Failure in intrusion detection apparatus results in a series of negatives ranging from loss of confidential data and thereof reducing reliability for the end-user. Hence, detection systems play an important role in the service end user. In the proposed work, an intelligent system is developed based on machine learning techniques specifically RNN wherein, a novel algorithm is developed for combining a correlation and information gain, feature reduction is achieved. A feed-forward neural network is then fed these reduced features for testing and training on the NSL-KDD dataset. Normally, pre-processing of the dataset is carried out before the training phase. It helps us to regularize instances of each class in the dataset. Attack and non-attack classes are formed which helps us to implement this developed algorithm for giving the best results in terms of Feature ranking. Our algorithms reduced the number of features, which in turn reduced in preprocessing time of extracting features related to information gain and correlation in the dataset.

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Section: Application Protocol-based Intrusion Detection Systems (Apids)mentioning

confidence: 99%

“…This method consumes the performance of average class values is more minor. Divyasree et al [3], a system to ensemble core vector machine (CVM) was proposed as an efficient intrusion detection system. The Chi-Square test is used in this approach to select the relevant features for each attack.…”

Section: Literature Surveymentioning

confidence: 99%

Intrusion Detection System Based on RNN Classifier for Feature Reduction

Deore¹,

Bhosale²

2021

SN COMPUT. SCI.

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“…Divyasree and Sherly [42] proposed an effective IDS utilizing ensemble core vector machine (CVM) approach. CVMs are algorithms which work based on the idea of Minimum Enclosing Ball.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Network Intrusion Detection System using Machine Learning

Alsaeedi¹,

Zubair²

2019

IJACSA

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With the coming of the Internet and the increasing number of Internet users in recent years, the number of attacks has also increased. Protecting computers and networks is a hard task. An intrusion detection system is used to detect attacks and to protect computers and network systems from these attacks. This paper aimed to compare the performance of Random Forests, Decision Tree, Gaussian Naïve Bayes, and Support Vector Machines in detecting network attacks. An up-to-date dataset was chosen to compare the performance of these classifiers. The results of the conducted experiments demonstrate that both Random Forests and Decision Tree performed effectively in detecting attacks.

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“…In general, the IDS are divided into misuse detection systems where the detection is based upon the signature and anomaly detection systems where the detection is based on profile [11,12]. In Anomaly detection systems there is a variation in ordinary system profile which is the main goal of this method, but in misuse detection systems, the main goal is to match with the attack case [13,14]. The fake alarm rate is normally high in anomaly detection systems because it is used to identify unfamiliar attacks having high-performance rates.…”

Section: Introductionmentioning

confidence: 99%

Developing a Secure Framework Using Feature Selection and Attack Detection燭echnique

Dahiya¹,

Nitin²

2023

Computers, Materials &Amp; Continua

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Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network (BNM-tGAN) attack detection technique. The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection. Initially, the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique (MLFIMT) that identifies the relationship among the missing values and attack classes. Based on the analysis, the missing values are classified as Missing Completely at Random (MCAR), Missing at random (MAR), Missing Not at Random (MNAR), and handled according to the types. Thereafter, categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar (AMD-RS) and the Handling of the imbalanced dataset. The selection of relevant features is initiated using FP that uses '3' Feature Selection (FS) techniques i.e., Inverse Chi Square based Flamingo Search (ICS-FSO) wrapper method, Hyperparameter Tuned Threshold based Decision Tree (HpTT-DT) embedded method, and Xavier Normal Distribution based Relief (XavND-Relief) filter method. Finally, the selected features are trained and tested for detecting attacks using BNM-tGAN. The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time. The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.

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A Network Intrusion Detection System Based On Ensemble CVM Using Efficient Feature Selection Approach

Cited by 33 publications

References 7 publications

Intrusion Detection System Based on RNN Classifier for Feature Reduction

Intrusion Detection System Based on RNN Classifier for Feature Reduction

Performance Analysis of Network Intrusion Detection System using Machine Learning

Developing a Secure Framework Using Feature Selection and Attack Detection燭echnique

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