Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Khraisat, Ansam; Gondal, Iqbal; Vamplew, Peter; Kamruzzaman, Joarder; Alazab, Ammar

doi:10.3390/electronics9010173

Cited by 137 publications

(91 citation statements)

References 30 publications

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“…Classification detection categorize the intrusion detection techniques based on anomaly behavior, by various classes. 5 In 2019, Arif Yulianto et al Recommended that the overall efficiency of the Ada Boost on the brand new, demanding CIC be improved using the Synthetic Minority Oversampling Technique (SMOTE), Concept Component Analysis (PCA).SMOTE is chosen to resolve the problem by developing an enhancement approach to overall performance intrusion detection to resolve the unbalance in training results. 9 Muhammad Hilmi Kamarudin et al in 2019 introduced a model called a hybrid feature selection approach that combines all the advantages of selection methods such as filter and wrapper system theoretically.…”

Section: Related Workmentioning

confidence: 99%

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Karthikeyan

Mohanraj²,

Suresh³

et al. 2021

Int J Communication

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Machine learning process is an important part in intrusion detection system (IDS) and plays an essential role in security information. Analysts operate IDS for the study of server logs or packets of data to identify network traffic malware. IDS automates this process with machine learning-based techniques that allow packet detection on a continuous basis without much human effort or intervention through network analyzing data. It has the following limitations on low rate of detection, false alarm rate is high, and so on, to which the conventional IDS has indicated. We proposed a stacking model that is more accurate to detect the attacks than other techniques of Random Forest, Naïve Bayes, and Decision Tree C4.5. Different researchers are exploring various techniques to classify the rules for associations. But the measure of confidence itself, as investigated in various literature, produces many times inaccurate results. So based on a recent value test called the significant rule power factor (SRPF), this paper discusses classifier. The experiments are carried out by implementation in Weka's method. Results indicate that the proposed classifier is a newly created, light weight classifier with better accuracy performance. Detection of different attacks uses NSL-KDD dataset to train and test the data in this technique. This proposed work is measured using different performance metrics to other intrusion detection models. Compared to existing methods, our proposed model offers prominent increase in detection accuracy.

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Section: Related Workmentioning

confidence: 99%

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Karthikeyan

Mohanraj²,

Suresh³

et al. 2021

Int J Communication

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show abstract

“…Khraisat et al [16] proposed a hybrid HIDS (combining the C5 decision tree classification and a support vector machine (SVM)). e purpose of this intrusion detection system was to overcome the problems of traditional intrusion detection systems in detecting known and unknown threats.…”

Section: Related Workmentioning

confidence: 99%

The Anomaly- and Signature-Based IDS for Network Security Using Hybrid Inference Systems

Einy

Öz

Navaei

2021

Mathematical Problems in Engineering

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With the expansion of communication in today’s world and the possibility of creating interactions between people through communication networks regardless of the distance dimension, the issue of creating security for the data and information exchanged has received much attention from researchers. Various methods have been proposed for this purpose; one of the most important methods is intrusion detection systems to quickly detect intrusions into the network and inform the manager or responsible people to carry out an operational set to reduce the amount of damage caused by these intruders. The main challenge of the proposed intrusion detection systems is the number of erroneous warning messages generated and the low percentage of accurate detection of intrusions in them. In this research, the Suricata IDS/IPS is deployed along with the NN model for the metaheuristic’s manual detection of malicious traffic in the targeted network. For the metaheuristic-based feature selection, the neural network, and the anomaly-based detection, the fuzzy logic is used in this research paper. The latest stable version of Kali Linux 2020.3 is used as an attacking system for web applications and different types of operating systems. The proposed method has achieved 96.111% accuracy for detecting network intrusion.

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“…Confusion matrix 80 shown in Table 6, also called contingency table, describes the results of classification. The upper left corner True positive is the number of entities being classified as true positive while those were true.…”

Section: Train Classifiersmentioning

confidence: 99%

“…• Confusion matrix 80 shown in • • Receiver The Operating Characteristic Curve (or ROC Curve) is a plot of the true positive rate against the false-positive rate for the various possible diagnostic test cutpoints. ROC reveals the trade-off between sensitivity and specificity (a decrease in specificity will follow any rise in sensitivity).…”

mentioning

confidence: 99%

Rapid health data repository allocation using predictive machine learning

Uddin

Stranieri

Gondal

et al. 2020

Health Informatics J

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Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used.

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Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Cited by 137 publications

References 30 publications

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

The Anomaly- and Signature-Based IDS for Network Security Using Hybrid Inference Systems

Rapid health data repository allocation using predictive machine learning

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