A Proactive Fuzzy-Guided Link Labeling Algorithm Based on MIH Framework in Heterogeneous Wireless Networks

Salih, Yass K.; See, Ong Hang; Yussof, Salman; Iqbal, Azlan; Salih, Sinan Q. Mohammad

doi:10.1007/s11277-013-1479-z

Cited by 15 publications

(17 citation statements)

References 15 publications

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“…The GB is an ensemble algorithm that was developed to solve classification and regression tasks [21], [22]. It merges several weak learners into a single strong learner.…”

Section: Gradient Boostingmentioning

confidence: 99%

“…These are GB-DTs, in which each tree is run separately, producing independent forecasts, which are then combined to make a final model's prediction. The number of weak learners is determined as number of estimators parameter [21], [22]. The model's prediction is integrated in classification problems like detecting the MTM attack or DoS by selecting the class label (MTM, normal in MTM dataset or DoS, normal in DoS dataset) with the most votes from all trees [21], [22].…”

Section: Gradient Boostingmentioning

confidence: 99%

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Man-in-the-middle and denial of service attacks detection using machine learning algorithms

Al-Juboori¹,

Hazzaa²,

Jabbar

et al. 2023

Bulletin EEI

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Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT). To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks.

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“…The GB is an ensemble algorithm that was developed to solve classification and regression tasks [21], [22]. It merges several weak learners into a single strong learner.…”

Section: Gradient Boostingmentioning

confidence: 99%

Section: Gradient Boostingmentioning

confidence: 99%

Man-in-the-middle and denial of service attacks detection using machine learning algorithms

Al-Juboori¹,

Hazzaa²,

Jabbar

et al. 2023

Bulletin EEI

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show abstract

“…The toolkit instead has inbuilt knowledge of several simulation protocols. This ensures different investigations use the same protocols [23][24][25][26][27][28][29][30], allowing results to be compared more directly. In addition, by performing specific simulation protocols with a dedicated program, performance optimizations specific to a given protocol are possible with 99.27% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

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Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research.

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“…The eigenvalues are obtained for both AHP and FAHP using covariance matrices of the weighted normalised decision matrices as: {0.1886 0.0057 0.0000 −0.0000 −0.0000 −0.0000} and {0.0398 0.0027 0.0000 0.0000 −0.0000 −0.0000}, respectively. The cumulative contribute rates of the principal components are computed using (9). The first and second principal components are obtained as 97 and 2% for AHP and PCA based schemes, and 93 and 6% for FAHP and PCA based schemes, respectively.…”

Section: Qos Factor Estimation Using Pcamentioning

confidence: 99%

“…Thus service adaptive PCA‐based network selection is required. In [9], a proactive fuzzy guided network selection algorithm is projected to process the network information from the media independent HO function (MIHF) and to generate the HO triggering events. Thus, it can avoid unnecessary HOs and service interruption time in the heterogeneous wireless networks.…”

Section: Related Workmentioning

confidence: 99%

Complexity–consistency trade‐off in multi‐attribute decision making for vertical handover in heterogeneous wireless networks

Chinnappan

Balasubramanian

2016

IET Networks

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The future wireless communication systems require an intelligent and service adaptive network selection to perform vertical handover (HO) in heterogeneous environments. Since multiple parameters are considered to select the best suitable network, reducing the complexity in multi-attribute decision making algorithm becomes apparent. In the proposed scheme, fuzzy logic controllers are used to estimate the necessity of HO and to determine the user satisfaction degree based on the critical parameters, such as mobile terminal speed, network load and service cost. Fuzzy analytic hierarchy process is used to calculate the weights of each attribute according to the traffic. Since it handles the uncertainty and vagueness in mapping the user preference to the priority scales, consistency level of network selection can be improved. Quality of service factor is calculated for each network by processing the weighted decision matrix using principal component analysis. Simulation results show that proposed scheme can ensure the best network selection by providing trade-off between complexity and consistency.

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A Proactive Fuzzy-Guided Link Labeling Algorithm Based on MIH Framework in Heterogeneous Wireless Networks

Cited by 15 publications

References 15 publications

Man-in-the-middle and denial of service attacks detection using machine learning algorithms

Man-in-the-middle and denial of service attacks detection using machine learning algorithms

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Complexity–consistency trade‐off in multi‐attribute decision making for vertical handover in heterogeneous wireless networks

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