A Machine-Learning Approach to Phishing Detection and Defense

Akanbi, Oluwatobi Ayodeji; Fazeldehkordi, Elahe; Amiri, Iraj Sadegh

doi:10.1016/c2014-0-03762-8

Cited by 7 publications

(4 citation statements)

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“…The test attributes of each current node are selected based on the attribute that has the maximum information. The operation of a DT on a dataset (DS) is expressed in [51] as follows:…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

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A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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“…The test attributes of each current node are selected based on the attribute that has the maximum information. The operation of a DT on a dataset (DS) is expressed in [51] as follows:…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

“…, n). The SVM represents DS as points in an N-dimensional space and then tries to develop a hyperplane that will split the space into specific class labels with a right margin of error [51]. Equations (3) and 4shows the formula used in the algorithm for the SVM optimisation.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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“…As the last step of dataset preprocessing, the dataset values are normalized using the standard min-max normalization [12], [13] as illustrated in (1):…”

Section: Methodsmentioning

confidence: 99%

DDoS Intrusion Detection Model for IoT Networks using Backpropagation Neural Network

Almotiri¹

2022

IJACSA

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In today's digital landscape, Internet of Things (IoT) networking has grown dramatically broad. The major feature of IoT network devices is their ability to connect to the internet and interact with it through data collecting and exchanging. Distributed Denial of Service (DDoS) is one form of cyber-attacks in which the hackers penetrate a single connection and then multiple machines are operating together to attack one target. The direct connectivity of IoT devices to the internet makes DDoS attacks worse and more dangerous. The more businesses adapted IoT networks to streamline the operations, the more allowing of DDoS intrusions at small and large scales to take place. Therefore, the intrusion detection module in the IoT networks is not optional in today's business environment. To achieve this objective, in this paper, an intelligent intrusion detection model is proposed to detect DDoS attacks in IoT networks. The intelligent model is a backpropagation neural network-based framework. The results are analyzed using different performance measures. The proposed model proves a detection rate of 99.46% and detection accuracy of 95.76% using the up-to-date benchmark CICDDoS2019 dataset. Furthermore, the proposed model has been compared with the most recent DDoS intrusion detection schemes and competitive performance is achieved.

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“…The "DT is a flow-chart-like tree structure that uses a branching technique to clarify every single likely result of a decision" (2) . Algorithm 1 explains the operations of the DT algorithm (2,50) . The settings of our DT model was, max_depth = 4 and criterion = entropy.…”

Section: Machine Learning Modelmentioning

confidence: 99%

The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

Adekoya¹,

Nti²

2020

IJST

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Background/Objectives: The Coronavirus disease in 2019 (COVID-19) was first seen in Wuhan (capital of Hubei, Chain), and has since spread throughout the world, resulting in the World Health Organisation (WHO) declaring the 2019-20 coronavirus a pandemic. Its ongoing spread has resulted in a standstill of the world's economy, total lockdown in some counties, closedown of business and firms globally. As of 25 th Mar 2020, 11:00 am GMT, a total of 491,280 cases were reported globally. The evolution of COVID-19 and its economic impact on the regional financial markets are highly uncertain, which makes it difficult for legislators to formulate a suitable macroeconomic strategy response. Methods: To ascertain the possible economic impact of COVID-19 globally, this study examines the effect of COVID-19 on major stocks indices across the globe. Using a random sampling technique, we selected thirty (30) world stock market indices in different countries infected with COVID-19 from 31 st Dec 2019 to 25 th Mar 2020. However, not like a high percentage of previous studies that focus on the regional stock market, we examine the information on daily reported COVID-19 cases and stock market fluctuation over thirty stock market indices that houses the stock prices of several countries around the globe. Also, we estimate the monetary loss within the period, project the future surge of this pandemic on the stock market and outline some portfolio allocation strategies to help the investor hedge against investment risk. Findings: The experimental results in this study show that even a controlled outbreak of the COVID-19 can significantly influence the world's economy in both the short-term and longterm. Our obtained results of COVID-19 associated economic loss echoed in stock prices movements advise that the cost can escalate severely and quickly into global economic stress. Hence, we conclude by outlining some measure that might help investors hedge against such risk using portfolio allocation strategies.

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A Machine-Learning Approach to Phishing Detection and Defense

Cited by 7 publications

References 0 publications

A comprehensive evaluation of ensemble learning for stock-market prediction

A comprehensive evaluation of ensemble learning for stock-market prediction

DDoS Intrusion Detection Model for IoT Networks using Backpropagation Neural Network

The COVID-19 outbreak and effects on major stock market indices across the globe: A machine learning approach

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