scholarly journals Identity Theft Detection Using Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1943-1946
Author(s):  
Vanshita Agarwal
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Identity Theft ◽  
Machine Learning Algorithms ◽  
Online Identity ◽  
Detection Algorithms ◽  
The Past ◽  
Email Address ◽  
Critical Form ◽  
Gain Access

Abstract: Online Identity Theft is known to be quite possibly the most genuine and developing dangers to people and organizations for the past decade because of the colossal financial harm caused by it. It is a critical form of cybercrime which uses information such a victim’s name, bank details, email address, passwords, passport or identification details, and other valuable information to gain access to accounts. This aim of this research is to make use of Machine Learning algorithms for the detection of such crimes. Keywords: identity theft; cybercrime; Machine Learning; detection; algorithms

scholarly journals Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Christopher Blier-Wong ◽  
Hélène Cossette ◽  
Luc Lamontagne ◽  
Etienne Marceau
Keyword(s):  
Machine Learning ◽  
Insurance Industry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Actuarial Science ◽  
Science Field ◽  
The Past ◽  
Highly Nonlinear ◽  
Computer Scientists ◽  
Applications Of Machine Learning

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

Machine Learning Based Malicious Android Application Detection

2020 ◽  
Vol 17 (8) ◽  
pp. 3468-3472
Author(s):  
S. L. Jany Shabu ◽  
Rohan Loganathan Reddy ◽  
V. Maria Anu ◽  
L. Mary Gladence ◽  
J. Refonaa
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Learning Algorithms ◽  
Identity Theft ◽  
Machine Learning Algorithms ◽  
Android Application ◽  
Sensitive Data ◽  
Android Applications ◽  
Malicious Apps

The ultimate aim of the project is to improve permission for detecting the malicious android mobile application using machine learning algorithms. In recent years, the usages of smartphones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application users, some intruders are creating malicious android applications as a tool to steal the sensitive data and identity theft/fraud mobile bank, mobile wallets. There are so many malicious applications detection tools and software are available. But an effectiveness of malicious applications detection tools is the need for the hour. They are needed to tackle and handle new complex malicious apps created by intruder or hackers.

Introduction to Machine Learning and Its Implementation Techniques

2020 ◽  
pp. 334-358
Author(s):  
Arul Murugan R. ◽  
Sathiyamoorthi V.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
High Speeds ◽  
Learning Techniques ◽  
Implementation Techniques ◽  
Gain Access

Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.

scholarly journals The ethics of algorithms: key problems and solutions

AI & Society ◽  
2021 ◽  
Author(s):  
Andreas Tsamados ◽  
Nikita Aggarwal ◽  
Josh Cowls ◽  
Jessica Morley ◽  
Huw Roberts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Design Development ◽  
Ethical Problems ◽  
Ethical Implications ◽  
The Past ◽  
Problems And Solutions

AbstractResearch on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms

2020 ◽  
Vol 16 (6) ◽  
pp. 639-652
Author(s):  
Vaira Suganthi Gnanasekaran ◽  
Sutha Joypaul ◽  
Parvathy Meenakshi Sundaram
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Computer Aided Detection ◽  
Breast Masses ◽  
Cad System ◽  
The Past ◽  
Mammogram Images

Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities available for capturing the abnormalities in breasts, mammography is the most commonly used technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms used in the detection of breast cancer developed in recent years. We begin the review with a concise introduction about the fundamental concepts related to mammograms and CAD systems. We then focus on the techniques used in the diagnosis of breast cancer with mammograms.

scholarly journals Anticipating Cryptocurrency Prices Using Machine Learning

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Laura Alessandretti ◽  
Abeer ElBahrawy ◽  
Luca Maria Aiello ◽  
Andrea Baronchelli
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Trading Strategies ◽  
Short Term ◽  
The Past ◽  
Term Evolution ◽  
Daily Data ◽  
Abnormal Profits

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

scholarly journals A Hybrid Framework for Heart Disease Prediction Using Machine Learning Algorithms

2021 ◽  
Vol 309 ◽  
pp. 01043
Author(s):  
L. Chandrika ◽  
K. Madhavi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Prediction Model ◽  
Linear Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
The Past ◽  
Past Data

Cardiovascular Diseases (CVDs) are the primary cause for the sudden death in the world today from the past few years the disease has emerged greatly as a most unpredictable problem, not only in India the whole planet facing the criticality. So, there is a desperate need of valid, accurate and practical solution or application to diagnose the CVD problems in time for mandatory treatment. Predicting the CVD is a great challenge in the health care domain of clinical data analysis. Machine learning Algorithms (MLA) and Techniques has been vastly developed and proven to be effective and efficient in predicting the problems using the past data. Using these MLA techniques and taking the clinical dataset which provided by the healthcare industry. Different studies were takes place and tried only a small part into predicting CVD with ML Algorithms. In this thesis, we propose the different novel methodology which concentrates at finding appropriate features by using MLA techniques resulting at finding out the accurate model to predict CVD. In this prediction model we are trying to implement the models with different combinations of features and several known classification techniques such as Deep Learning, Random Forest, Generalised Linear Model, Naïve Bayes, Logistic Regression, Decision Tree, Gradient Boosted trees, Support Vector Machine, Vote and HRFLM and we have got an higher accuracy level and of 75.8%, 85.1%, 82.9%, 87.4%, 85%, 86.1%, 78.3%, 86.1%, 87.41%, and 88.4% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

scholarly journals Forecasting stock market trend: a comparison of machine learning algorithms

2020 ◽  
Vol 6 (1) ◽  
pp. 37-49
Author(s):  
R. Cervelló-Royo ◽  
F. Guijarro
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Research Topic ◽  
Machine Learning Algorithms ◽  
The Past ◽  
Average Accuracy ◽  
Mathematical Formulas ◽  
Market Trends ◽  
Chart Patterns ◽  
Past Trend

Forecasting the direction of stocks markets has become a popular research topic in recent years. Differentapproaches have been applied by researchers to address the prediction of market trends by consideringtechnical indicators and chart patterns from technical analysis. This paper compares the performanceof four machine learning algorithms to validate the forecasting ability of popular technical indicators inthe technological NASDAQ index. Since the mathematical formulas used in the calculation of technicalindicators comprise historical prices they will be related to the past trend of the market. We assume thatforecasting performance increases when the trend is computed on a longer time horizon. Our resultssuggest that the random forest outperforms the other machine learning algorithms considered in ourresearch, being able to forecast the 10-days ahead market trend, with an average accuracy of 80%.

scholarly journals Performance Analysis of Machine Learning Algorithms Used for Web Based Phishing Detection

2021 ◽  
Vol 23 (05) ◽  
pp. 650-656
Author(s):  
Shailendra Baliram Torane ◽  
◽  
Dr. Narendra Shekokar ◽  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
The Real ◽  
Detection Algorithms ◽  
Confidential Data ◽  
Accuracy Parameter ◽  
Phishing Detection

Phishing is a cybercrime technique in which the attacker creates a copy of genuine websites with the same color pattern, layout, font, and logo and with a domain name that matches with the real one. Then, broadcast this fake website through various online modes like emails and social media. The attacker creates lucrative offers or discounts to lure in people to click on the phishing link. Once the user clicks on this phishing link, they a re directed to the duplicate website that the attacker had created. The user believes that it is the real website and enters his/her login details and other confidential data. This data is stored on the attacker’s server thus giving him full access to the victim’s data. The phishing attack is mainly targeted to collect confidential data of the victim. This data includes Username, Passwords, Bank details, security Credit card numbers etc. Machine Learning algorithms are being used widely in detecting phishing websites. This paper shows performance analysis of three Machine learning algorithms used for URL phishing detection. These algorithms are Extreme Learning Machine, Support Vector Machine and Naïve Bayes algorithm. The paper analyses these algorithms on the parameters of Accuracy, Precision, Recall, F1 score and Confusion matrix. The dataset includes 11,000 entries and 30 features from UC Irvine dataset repository. The literature survey shows how only importance is given to only one parameter i.e., Accuracy parameter when analyzing performance of the URL phishing detection algorithms. This paper concludes on how Accuracy parameter does not show full picture on the overall performance of the URL phishing detection algorithms and also how Precision and Recall parameters are very important in understanding the working of these algorithms.

scholarly journals A survey of Emerging Techniques in Detecting SMS Spam

2019 ◽  
Vol 7 (5) ◽  
pp. 23-35
Author(s):  
Sahar Alqahtani ◽  
Daniyal Alghazzawi
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mobile Users ◽  
Topic Modelling ◽  
The Past ◽  
Machine Learning Methods ◽  
Effective Performance ◽  
Modelling Techniques

In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

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