Feature Selection Using Particle Swarm Optimization in Intrusion Detection

Ahmad, Iftikhar

doi:10.1155/2015/806954

Cited by 53 publications

(36 citation statements)

References 19 publications

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“…DAR Ensemble [52] 78.88 N/A Naive Bayes-KNN-CF [53] 82.00 05.43 Feature Selection + SVM [54] 82.37 15.00 GAR Forest + Symmatrixal Uncertainity [55] 85.00 12.20 Bagging j48 [56] 84.25 02.79 PCA+PSO [57] 99 From Table 45, we conclude that, with base machine learning classifier j48, random forest, and Reptree, we used Adaboost and Bagging to make predictions more accurate on KDD99 and NSLKDD datasets, for binary and multi classes. J48, Random Forest, and Reptree with Adaboost achieved 99.90 true positive (TP) rate and 00.00% false positive (FP) rate, respectively.…”

Section: Methods Accuracy Detection Rate (%) Fr Rate (%)mentioning

confidence: 99%

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Iwendi

Khan

Anajemba

et al. 2020

Sensors

102

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The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

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Section: Methods Accuracy Detection Rate (%) Fr Rate (%)mentioning

confidence: 99%

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Iwendi

Khan

Anajemba

et al. 2020

Sensors

102

View full text Add to dashboard Cite

show abstract

“…Feature selection is a procedure to find a subset of significant features from the original set of features and reduces the number of irrelevant redundant features from dataset to enhance the performance of the classification and also decreases storing of memory space [8]. Feature selection helps in understanding data, reducing the effect of curse of dimensionality, reducing calculation requirement, enhancing the accuracy of learning and distinguishing which features may be relevant to a particular issue [9].…”

Section: Feature Selectionmentioning

confidence: 99%

Evaluation of DDoS attacks Detection in a New Intrusion Dataset Based on Classification Algorithms

Abdulrahman

Ibrahem²

2019

Iraqi Journal of ICT

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Intrusion detection system is an imperative role in increasing security and decreasing the harm of the computer security system and information system when using of network. It observes different events in a network or system to decide occurring an intrusion or not and it is used to make strategic decision, security purposes and analyzing directions. This paper describes host based intrusion detection system architecture for DDoS attack, which intelligently detects the intrusion periodically and dynamically by evaluating the intruder group respective to the present node with its neighbors. We analyze a dependable dataset named CICIDS 2017 that contains benign and DDoS attack network flows, which meets certifiable criteria and is openly accessible. It evaluates the performance of a complete arrangement of machine learning algorithms and network traffic features to indicate the best features for detecting the assured attack classes. Our goal is storing the address of destination IP that is utilized to detect an intruder by method of misuse detection.

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“…According to the authors the technique can successfully detect the outlier and recognize the data as normal or abnormal. Sinwar and Dhaka [16] [17] has used a combination of PCA and PSO for feature selection and classification in intrusion detection for WSNs. PCA was used for feature reduction and selection while PSO was utilized to choose the ideal subset of feature from the PCA space.…”

Section: Related Workmentioning

confidence: 99%

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

Shia¹,

Tawfeeq²,

Mahmoud³

2019

IJMECS

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The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.

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Feature Selection Using Particle Swarm Optimization in Intrusion Detection

Cited by 53 publications

References 19 publications

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

Evaluation of DDoS attacks Detection in a New Intrusion Dataset Based on Classification Algorithms

High Rate Outlier Detection in Wireless Sensor Networks: A Comparative Study

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