Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

Shareef, Sarah Mohammed; Hashim, Soukaena Hassan

doi:10.30684/etj.v38i1b.149

Cited by 12 publications

(9 citation statements)

References 8 publications

(8 reference statements)

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“…Data pre-processing is a primary and essential stage for obtaining final datasets that can be considered correct and beneficial for different machine learning algorithms [16]. This stage involves picking up signals of eye movement based on the computer architecture for the EOG using the atmage256 Arduino 6037 board with the AD8232 biological signal sensor and Arduino system programming IDE.…”

Section: Experiment's Setupmentioning

confidence: 99%

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Jasim

Hassan

2022

IJECE

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<span lang="EN-US">In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.</span>

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Section: Experiment's Setupmentioning

confidence: 99%

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Jasim

Hassan

2022

IJECE

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“…It's a computational framework that combines database systems artificial intelligence, and machine learning to detect patterns in multiple datasets. Data mining applications can employ a variety of characteristics to examine diverse data sets [5], [6]. Due to the consequences of rising security threats nowadays, Network Intrusion Detection Systems (NIDS) have become the most crucial component of modern network infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Based on Ada boosting and Bagging Algorithm

2022

IJCCCE

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Computer worms execute damaging functions in the network systems, compromising system security. Although researchers use a variety of methods to detect worms and prevent their spread. Detecting worms remains a challenge for the following reasons: First, a huge volume of irrelevant data affects classification accuracy. Second, frequently used individual classifiers in systems are poor at detecting all types of worms, Third, many systems are built on out-of-date information, rendering them useless for new worm species. As a result, providing a network intrusion detection system is vital for ensuring security and reducing the harm caused by worms on networks to information systems. The goal of the study is to discover computer worms in the computer networks and protect the systems from their damages. The proposed method uses the UNSW NB15 dataset to train and test the ensemble Ada boosting and Bagging algorithms with the Support Vector Nachine (SVM) as a contribution rather than a decision tree. Due to Correlation Feature Selection (CFS) identifying relationships between features and classes, and Chi-square (Chi2) determining whether features and classes are independent or not, we combined these two algorithms as a contribution in a method called CFS&Chi2fs to select the relevant features and reduce the time. The system achieved accuracy reaching 0.998 with Bagging(SVM), and 0.989 with Ada boost(SVM). Index Terms— Intrusion Detection System, Computer Worms, Ada boosting, Bagging.

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“…IDS is designed to defend the computer system from suspicious activities that would go undetected by a traditional packet filter [3]. Establishing high levels of cyber resilience against malicious activity and detecting unauthorized access to a computer system is critical, to scanning network packets for malicious activity signals [4]- [7]. In general, many IDSs have many disadvantages, which are including the inability to distinguish between new malicious threats, low (accuracy, and detection rate), and high false negative and positive rates.…”

Section: Introductionmentioning

confidence: 99%

Proposed Hybrid Ensemble Learning Algorithms for an Efficient Intrusion Detection System

2022

IJCCCE

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Due to sophisticated cyber-attacks, and to produce false alarms on suspicious or unusual behavior to monitor computer resources, Intrusion Detection Systems (IDSs) are required. Hence, Many Machine Learning (ML) and data mining techniques have been proposed to increase the effectiveness of IDSs, whereas current IDS algorithms are still struggling to perform effectively while many IDSs depend on a single classifier to detect intrusions. Single-classifier IDSs cannot achieve high accuracy and low false alarm rates because of zero-day attacks. In this paper, a hybrid ensemble method using AdaBoosting and Bagging for IDS is proposed. This study aims to identify unknown (zero-day attacks) and known (well-known) attacks. So, the proposed model comprises three stages. The first stage is preprocessing. The second stage involves the application of AdaBoosting and Bagging methods by four different classifiers modifying (i.e., Naïve Bayesian (NB), Support Vector Machine (SVM), random forest (RF), and K_Nearest Neighbor (KNN)). Such a modification is performed for the AdaBoosting methods. The AdaBoosting classifier is then combined to work in the Bagging method. For attack recognition, uses the voting technique as the third stage. Experimental results reveal that using the UNSW BN15 dataset yields testing with 85.49% accuracy, 99.96% detection rate, and 0.006 false alarm rate. Therefore, the proposed Hybrid AdaBoosting and Bagging Method (HABBM) can outperform other comparable and state-of-the-art techniques across a variety of parameters. Index Terms—AdaBoosting method, Bagging method, Cyber Security CS, Ensemble Method, Intrusion Detection Systems IDSs.

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Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

Cited by 12 publications

References 8 publications

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer

Intrusion Detection System Based on Ada boosting and Bagging Algorithm

Proposed Hybrid Ensemble Learning Algorithms for an Efficient Intrusion Detection System

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