Email Fraud Attack Detection Using Hybrid Machine Learning Approach

2019 ◽  
Vol 12 ◽  
Author(s):  
Yousef A. Yaseen ◽  
Malik Qasaimeh ◽  
Raad S. Al-Qassas ◽  
Mustafa Al-Fayoumi
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Attack Detection ◽  
Majority Voting ◽  
Data Set ◽  
Hybrid Classifier ◽  
Machine Learning Approach ◽  
Hybrid Machine ◽  
Communication Methods ◽  
E Mail

Background: : E-mail is an efficient way to communicate. It is one of the most commonly used communication methods, and it can be used for achieving legitimate and illegitimate activities. Many features that can be effective in detecting email fraud attacks, are still under investigation. Method: : This paper proposes an improved classification accuracy for fraudulent emails that is implemented through feature extraction and hybrid Machine Learning (ML) classifier that combines Adaboost and Majority Voting. Eleven ML classifiers are evaluated experimentally within the hybrid classifier, and the performance of the email fraud filtering is evaluated by using WEKA and R tool on a data set of 9298 email messages. Results: : The performance evaluation shows that the hybrid model of Voting using Adaboost outperforms all other classifiers, with the lowest Error Rate of 0.6991%, highest f1-measure of 99.30%, and highest Area Under the Curve (AUC) of 99.9%. Conclusion:: The utilized proposed email features with the combination of Adaboost and Voting algorithms prove the efficiency of fraud email detection.

Abstract WP405: Automated Detection of Hemorrhagic Stroke From Non-Contrast Computed Tomography: A Machine Learning Approach

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Pakinam Aboutaleb ◽  
Arko Barman ◽  
Victor Lopez-Rivera ◽  
Songmi Lee ◽  
Farhaan Vahidy ◽  
...  
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Validation Data ◽  
Data Set ◽  
Segmentation Evaluation ◽  
Machine Learning Approach ◽  
Automated Imaging ◽  
Contrast Ct

Introduction: Automated neuroimaging analysis is being used increasingly in the acute ischemic stroke (AIS) evaluation. However, current algorithms do not factor in an assessment of intracranial hemorrhage (ICH) in the workflow. In this study we present a machine learning (ML) algorithm that uses brain symmetry information to detect ICH. Methods: We prospectively collected non-contrast CT (NCCT) images on patients that presented to the Emergency Department for AIS evaluation between 2017 and 2019. Patients were included if they underwent technically adequate NCCT imaging. Diagnoses of ICH, AIS and non-stroke were confirmed by experienced neuroradiologists as well as review of the clinical record. A ML algorithm which integrates symmetry features as well as standard features for the whole brain was trained on 80% of the sample and validated on the remaining images. Training was performed without any prior segmentation. Evaluation of the model performance was conducted using receiver-operator curve and area under the curve (AUC) analysis. Results are given as median [IQR] and [AUC 95% CI]. Results: Among the 568 patients that met inclusion criteria, median age was 65 [55-76], 47% were female and 34% were white. 128 (23%) patients were determined to have ICH and 440 as non-ICH (70% AIS and 30% non-stroke). Among ICH patients, 108 (84%) had a supratentorial ICH. When analyzing the regions of the CT images that most strongly contributed to the algorithm’s diagnostic decisions, they corresponded with the regions of ICH (Fig. 1A). On the external validation data set, the algorithm successfully detected ICH (Fig. 1B) with high accuracy (AUC 0.99 [0.97-1.00]). Conclusion: We have developed a symmetry-sensitive ML method that can with very high fidelity identify ICH in an automated fashion. Without prior training, the algorithm autonomously was able to learn ICH location. These results may help contribute to an automated imaging workflow for all stroke evaluations, not just AIS.

scholarly journals A hybrid machine-learning approach for segmentation of protein localization data

2005 ◽  
Vol 21 (19) ◽  
pp. 3778-3786 ◽  
Author(s):  
P. M. Kasson ◽  
J. B. Huppa ◽  
M. M. Davis ◽  
A. T. Brunger
Keyword(s):  
Machine Learning ◽  
Protein Localization ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Hybrid Machine ◽  
Localization Data

scholarly journals A Machine Learning Approach for Backoff Manipulation Attack Detection in Cognitive Radio

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 227349-227359
Author(s):  
Wassim Fassi Fihri ◽  
Hassan El Ghazi ◽  
Badr Abou El Majd ◽  
Faissal El Bouanani
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Attack Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

2021 ◽  
Author(s):  
Merlin James Rukshan Dennis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Statistical Approach ◽  
Denial Of Service ◽  
Attack Detection ◽  
Learning Approach ◽  
Ddos Attack ◽  
Machine Learning Approach ◽  
Ddos Detection ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

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