Over the last few years, the Web has seen a
massive growth in the number and kinds of web services.
Web facilities such as online banking, gaming, and social
networking have promptly evolved as has the faith upon them
by people to perform daily tasks. As a result, a large amount
of information is uploaded on a daily to the Web. As these
web services drive new opportunities for people to interact,
they also create new opportunities for criminals. URLs are
launch pads for any web attacks such that any malicious
intention user can steal the identity of the legal person by
sending the malicious URL. Malicious URLs are a keystone
of Internet illegitimate activities. The dangers of these sites
have created a mandates for defences that protect end-users
from visiting them. The proposed approach is that classifies
URLs automatically by using Machine-Learning algorithm
called logistic regression that is used to binary classification.
The classifiers achieves 97% accuracy by learning phishing
URLs
Search Engine Optimization (SEO) Seminar ReportNandu B Rajan
The document discusses search engine optimization (SEO) techniques for improving a website's search engine ranking, including both on-page optimization of elements within the website and off-page optimization involving external links and social media. Proper use of keywords, metadata, content, linking, and social signals can help a website rank higher organically in search engine results pages. Understanding and applying both on-page and off-page SEO strategies is important for online business success and increased website traffic.
A Hybrid Approach For Phishing Website Detection Using Machine Learning.vivatechijri
In this technical age there are many ways where an attacker can get access to people’s sensitive information illegitimately. One of the ways is Phishing, Phishing is an activity of misleading people into giving their sensitive information on fraud websites that lookalike to the real website. The phishers aim is to steal personal information, bank details etc. Day by day it’s getting more and more risky to enter your personal information on websites fearing that it might be a phishing attack and can steal your sensitive information. That’s why phishing website detection is necessary to alert the user and block the website. An automated detection of phishing attack is necessary one of which is machine learning. Machine Learning is one of the efficient techniques to detect phishing attack as it removes drawback of existing approaches. Efficient machine learning model with content based approach proves very effective to detect phishing websites.
Our proposed system uses Hybrid approach which combines machine learning based method and content based method. The URL based features will be extracted and passed to machine learning model and in content based approach, TF-IDF algorithm will detect a phishing website by using the top keywords of a web page. This hybrid approach is used to achieve highly efficient result. Finally, our system will notify and alert user if the website is Phishing or Legitimate.
Basics of Off Page SEO. Learn what is Onpage SEO and how it is done.
Visit My Website : http://freedigitalmarketingcourses.co.in/
Visit My Youtube channel : https://www.youtube.com/channel/UCKloSpgus_lfOko-wgGVMKg
This Power point presentation or ppt is about following content
CONTENT OF PRESENTATION
What is social media ?
What is marketing ?
Social media marketing
Social media marketing analysis
5 ways to analyze your social media marketing performance
How do you find out our Facebook followers’ demographics?
Why demographics are useful?
How do you decide optimal times/days to post on social media?
What metrics do you monitor on a daily basis?
How do you present to the marketing department?
Think With Google - Youtube Breakout SessionHarsha MV
This document discusses how brands can leverage YouTube during the festive season in India to engage customers. It provides examples of popular content categories and festivals from September to December. It also outlines campaign strategies brands can use, such as creating relevant video content, targeting key channels, and coordinating campaigns with new movie and product releases. Finally, it promotes YouTube Blast as a solution to reach a large audience and drive actions like searches, visits, and conversions.
This document discusses finger vein authentication technology. It begins with an introduction and overview of biometrics and finger vein authentication. It then describes the four components of finger vein detection and authentication: image acquisition, pre-processing, extraction, and matching. It highlights benefits of finger vein authentication such as accuracy, speed, security, compact size, and difficulty to forge. It concludes with examples of applications for finger vein authentication such as PC login, identity management, time/attendance tracking, cashless catering, banking, and access control for secure areas.
Biometric authentication uses unique human physical and behavioral characteristics for authentication purposes. Physical biometrics include fingerprints, facial patterns, iris scans, and retinal patterns. Behavioral biometrics analyze keystrokes, gait, voice, mouse movements, signatures, and cognition. Biometrics provide stronger authentication than passwords alone but have disadvantages like inability to change compromised biometrics and potential for "master fingerprints" to trick some devices. Biometrics are increasingly used for consumer, government, and corporate authentication.
Working in digital industry? Learn how to perform a website Technical Audit for SEO. Hannah Thorpe explains
What are the core elements to check for an SEO audit?
What are the warning sites your site might be struggling?
How to fix common problems flagged during a technical audit
Search Engine Optimization (SEO) Seminar ReportNandu B Rajan
The document discusses search engine optimization (SEO) techniques for improving a website's search engine ranking, including both on-page optimization of elements within the website and off-page optimization involving external links and social media. Proper use of keywords, metadata, content, linking, and social signals can help a website rank higher organically in search engine results pages. Understanding and applying both on-page and off-page SEO strategies is important for online business success and increased website traffic.
A Hybrid Approach For Phishing Website Detection Using Machine Learning.vivatechijri
In this technical age there are many ways where an attacker can get access to people’s sensitive information illegitimately. One of the ways is Phishing, Phishing is an activity of misleading people into giving their sensitive information on fraud websites that lookalike to the real website. The phishers aim is to steal personal information, bank details etc. Day by day it’s getting more and more risky to enter your personal information on websites fearing that it might be a phishing attack and can steal your sensitive information. That’s why phishing website detection is necessary to alert the user and block the website. An automated detection of phishing attack is necessary one of which is machine learning. Machine Learning is one of the efficient techniques to detect phishing attack as it removes drawback of existing approaches. Efficient machine learning model with content based approach proves very effective to detect phishing websites.
Our proposed system uses Hybrid approach which combines machine learning based method and content based method. The URL based features will be extracted and passed to machine learning model and in content based approach, TF-IDF algorithm will detect a phishing website by using the top keywords of a web page. This hybrid approach is used to achieve highly efficient result. Finally, our system will notify and alert user if the website is Phishing or Legitimate.
Basics of Off Page SEO. Learn what is Onpage SEO and how it is done.
Visit My Website : http://freedigitalmarketingcourses.co.in/
Visit My Youtube channel : https://www.youtube.com/channel/UCKloSpgus_lfOko-wgGVMKg
This Power point presentation or ppt is about following content
CONTENT OF PRESENTATION
What is social media ?
What is marketing ?
Social media marketing
Social media marketing analysis
5 ways to analyze your social media marketing performance
How do you find out our Facebook followers’ demographics?
Why demographics are useful?
How do you decide optimal times/days to post on social media?
What metrics do you monitor on a daily basis?
How do you present to the marketing department?
Think With Google - Youtube Breakout SessionHarsha MV
This document discusses how brands can leverage YouTube during the festive season in India to engage customers. It provides examples of popular content categories and festivals from September to December. It also outlines campaign strategies brands can use, such as creating relevant video content, targeting key channels, and coordinating campaigns with new movie and product releases. Finally, it promotes YouTube Blast as a solution to reach a large audience and drive actions like searches, visits, and conversions.
This document discusses finger vein authentication technology. It begins with an introduction and overview of biometrics and finger vein authentication. It then describes the four components of finger vein detection and authentication: image acquisition, pre-processing, extraction, and matching. It highlights benefits of finger vein authentication such as accuracy, speed, security, compact size, and difficulty to forge. It concludes with examples of applications for finger vein authentication such as PC login, identity management, time/attendance tracking, cashless catering, banking, and access control for secure areas.
Biometric authentication uses unique human physical and behavioral characteristics for authentication purposes. Physical biometrics include fingerprints, facial patterns, iris scans, and retinal patterns. Behavioral biometrics analyze keystrokes, gait, voice, mouse movements, signatures, and cognition. Biometrics provide stronger authentication than passwords alone but have disadvantages like inability to change compromised biometrics and potential for "master fingerprints" to trick some devices. Biometrics are increasingly used for consumer, government, and corporate authentication.
Working in digital industry? Learn how to perform a website Technical Audit for SEO. Hannah Thorpe explains
What are the core elements to check for an SEO audit?
What are the warning sites your site might be struggling?
How to fix common problems flagged during a technical audit
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
Companion piece to this blog post: http://www.greenlaneseo.com/blog/2016/02/semrushin-around-recap/
If you’re working in the SEO industry, you probably know about SEMrush, keyword research and competitor analysis tool that offers numerous useful features. Our very own Sean Malseed is pretty familiar with the tool since he used to work at SEMrush as Director of Strategic Development, and he recently gave a Knowledge Share focusing on three of its most useful features: Domain vs. Domain, ranking history tools, and filters. Here’s an SEMrush review and the key takeaways from his presentation!
Hi guys , here is new presentation which is related to password authentication named as Graphical Password Authentication.Here i have covered all the topics which are related to GPA .I will also provide a documentation regarding this topic if u need .So please comment below for the document and fallow @shobha rani
Schema markup is code (semantic vocabulary) that you put on your website to help the search engines return more informative results for users. If you've ever used rich snippets, you'll understand exactly what schema markup is all about.
2022 has been a whirlwind of a year for the SEO industry, and there’s no sign of slowing down.
This year alone, Google dropped eight confirmed and several unconfirmed updates – leaving many businesses scrambling to keep up.
With so much volatility, how can you adapt your SEO strategy to keep it fresh and relevant?
How will this year’s algorithm changes affect your 2023 SEO strategy?
How can you prepare for Google’s next move and get ahead of the curve?
In our next webinar, Pat Reinhart, VP of Customer Success at Conductor, discusses how to handle frequent algorithm changes and market shifts.
We’ll recap the biggest SEO insights of this year, share some expert predictions based on 2022’s algorithm updates, and uncover what next year may hold.
Key Takeaways:
What a crazy 2022 for Google means for 2023.
How the growth of social media search will impact strategy in 2023.
What the popularity of visual search will mean going forward.
If you struggled keeping up with this year’s constant changes, the SEO predictions you’ll discover in this webinar could be a game-changer for your business.
Are you ready to optimize your SEO strategy to stay competitive in 2023?
Internal linking is important for search engine optimization (SEO) as it allows users to navigate websites, establishes information hierarchies, and helps spread ranking power. Some key tactics for improving internal linking include choosing an optimal link structure, using text menus, adding keywords to anchor text strategically, cross-linking important pages, and eliminating duplicate, broken, or dangling links. Proper internal linking passes link juice between pages and helps increase page views and rankings.
In this presentation, we review the most important elements of setting up a Bing Webmasters Tool accounts. We also cover the details associated with reviewing data and error notifications.
The document discusses using handwritten signature verification as an additional security measure for computer systems. It notes that signature verification must be cheap, reliable, and unobtrusive. It explains that online signature verification analyzes dynamic features of signing, like speed and pressure, which presents challenges in differentiating consistent versus varying behavioral elements of a person's signature over time. The document outlines the signature acquisition and identification process using global features and training models, and notes the benefits of low error rates, ability to detect forgery even with copied signatures, fast training, and cheap storage requirements.
Applying NLP and Machine Learning to Keyword AnalysisDan Segal
From a presentation at Text Analytics Forum, Washington, DC, Nov. 7, 2018. Keyword research allows companies to learn the voice of their customers and tune their marketing messages for them. One of the challenges in keyword research is to find collections of keywords that are topically relevant and in demand and therefore likely to draw search traffic and customer engagement. Data sources such as search logs and search engine result pages provide valuable sources of keywords, as well as insight into audience-specific language. Additionally, cognitive technologies such as natural language processing and machine learning provide capabilities for mining those sources at scale. With a few tools and some minimal coding, an analyst can generate clusters of best-bet keywords that are not only syntactically similar but semantically related. This how-to talk presents some practical techniques for automated analysis of keyword source data using off-the-shelf APIs.
This document provides an introduction to web crawlers. It defines a web crawler as a computer program that browses the World Wide Web in a methodical, automated manner to gather pages and support functions like search engines and data mining. The document outlines the key features of crawlers, including robustness, politeness, distribution, scalability, and quality. It describes the basic architecture of a crawler, including the URL frontier that stores URLs to fetch, DNS resolution, page fetching, parsing, duplicate URL elimination, and filtering based on robots.txt files. Issues like prioritizing URLs, change rates, quality, and politeness policies are also discussed.
Anthony Marquez - esports and marketing portfolioAnthonyMarquez33
Anthony is an experienced marketing specialist with a track record of developing brand and marketing strategies that engage consumers. He has previously worked with Impact Gaming, developing their innovative content marketing strategy that led to over 30,000 engaged fans. For Jumper, an AI streaming app, he created a social media plan focused on open dialogue between the community and developers, generating over 1,000 app downloads from a single Reddit post and helping Jumper surpass 10,000 social media followers. Anthony ensures marketing messages align with companies' goals to resonate with consumers and create engaged communities.
This document presents a literature review and proposed methodology for detecting malicious URLs. It discusses prior work on blacklisting, heuristic approaches, and machine learning techniques for malicious URL detection. The proposed methodology develops three categories of features - URL lexical features based on TF-IDF, source code features to identify obfuscated JavaScript, and network features like payload size. These features would be used to train a machine learning model like SVM to classify URLs as malicious or benign in real-time. The goal is to automatically detect new web attacks and force attackers to make tradeoffs to evade detection.
Web crawling involves automated programs called crawlers or spiders that browse the web methodically to index web pages for search engines. Crawlers start from seed URLs and extract links from visited pages to discover new pages, repeating the process until a desired size or time limit is reached. Crawlers are used by search engines to build indexes of web content and ensure freshness through revisiting URLs. Challenges include the web's large size, fast changes, and dynamic content generation. APIs allow programmatic access to web services and information through REST, HTTP POST, and SOAP.
Crime Pattern Detection using K-Means ClusteringReuben George
Crime pattern detection uses data mining techniques like clustering to analyze crime data and identify patterns. This involves plotting past crimes geographically, clustering similar crimes to detect sprees, and analyzing the results to draw conclusions. It helps improve crime solving by learning from history and preempting future crimes. The method augments detectives' work but has limitations like relying on data quality. Overall, crime pattern detection aids operational efficiency and enhancing resolution rates by optimizing resource deployment based on observed crime trends.
Graphical password authentication system pptsNimisha_Goel
This document discusses different types of authentication systems including token-based, biometric-based, knowledge-based, recognition-based, pure recall-based, cued recall-based, and hybrid systems. It then focuses on graphical passwords, describing how they work by having users select images in a specific order. The document outlines a project to create an Android application for graphical password authentication to unlock private files using a cued recall system where users must select images in the correct sequence from a random grid. It discusses security considerations like guessing attacks and proposes that this approach provides stronger passwords while increasing the workload for attackers.
How To Rank #1 On Google | How To Improve Google Ranking | SEO Tutorial For B...Simplilearn
This presentation on SEO will help you understand the various factors that help you rank #1 on Google which includes keyword research, creating high-quality content, how to optimize the content using on-page elements and website level factors which influence Google ranking and we will also discuss off-site engagement. But, ranking on Google is not easy. There are a lot of factors that influence this. Now, let us get started and understand the major factors influencing Google ranking in the year 2019.
Below topics are explained in this SEO presentation:
1. Keyword research
2. High-quality content
3. Optimize on-page elements and website factors
4. Off-site engagement
Why learn Digital Marketing?
Businesses and recruiters prefer marketing professionals with genuine knowledge, skills, and experience verified by a certification that is accepted across industries. Continuous learning for any working professional is not only important for keeping themselves up to date with the current market trends, but it also helps them expand their array of skill set and become more flexible in the workplace.
What skills will you learn from this Digital Marketing course?
This course will enable you to:
1. Gain an in-depth understanding of the various digital marketing disciplines: search engine optimization (SEO), social media marketing, pay-per-click (PPC), website conversion rate optimization, web analytics, content marketing, mobile marketing, email marketing, programmatic buying, marketing automation and digital marketing strategy
2. Master digital marketing execution tools: Google Analytics, Google Ads, Facebook Marketing, Twitter Advertising, and YouTube Marketing
3. Become a virtual digital marketing manager for an e-commerce company with Mimic Pro simulations included in our course. Practice SEO, SEM, Website Conversion Rate Optimization, email marketing and more.
4. Gain real-life experience by completing projects using Google Analytics, Google Ads, Facebook Marketing, and YouTube Marketing
5 Create the right marketing messages tailored for the right audiences
6. Prepare for top digital marketing certification exams such as OMCA, Google Analytics, Google Ads, Facebook Marketing, and YouTube Marketing certifications
Who should take this Digital Marketing course?
Anyone who is looking to further his or her career in digital marketing should take this course, especially those seeking leadership positions. Any of these roles can benefit from the Digital Marketing Specialist training:
1. Marketing Managers
2. Digital Marketing Specialists
3. Marketing or Sales Professionals
4. Management, Engineering, Business, or Communication Graduates
5. Entrepreneurs or Business Owners
6. Marketing Consultant
Learn more at https://www.simplilearn.com/digital-marketing/digital-marketing-certified-associate-training
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
1) The document presents a system for detecting malicious links using machine learning techniques. It aims to improve the effectiveness of classifiers for identifying dangerous websites.
2) The system utilizes logistic regression as a supervised machine learning algorithm to categorize URLs based on their linguistic properties and behaviors. New attributes are extracted from URLs to train the classifier.
3) The proposed approach aims to identify malicious URLs based solely on their structure and properties, without relying on webpage content. This could lead to significant resource savings and a safer browsing experience for users.
This document outlines a presentation on detecting phishing websites using machine learning. The goals of the project are to develop a machine learning model to identify phishing URLs and integrate it into a web application. It will discuss collecting and preprocessing data, selecting machine learning algorithms, developing the web app, and addressing challenges with existing phishing detection techniques. The project aims to help reduce online theft and educate users on phishing risks and countermeasures.
Iris recognition is an automated method of bio metric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Retinal scanning is a different, ocular-based bio metric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally.
Companion piece to this blog post: http://www.greenlaneseo.com/blog/2016/02/semrushin-around-recap/
If you’re working in the SEO industry, you probably know about SEMrush, keyword research and competitor analysis tool that offers numerous useful features. Our very own Sean Malseed is pretty familiar with the tool since he used to work at SEMrush as Director of Strategic Development, and he recently gave a Knowledge Share focusing on three of its most useful features: Domain vs. Domain, ranking history tools, and filters. Here’s an SEMrush review and the key takeaways from his presentation!
Hi guys , here is new presentation which is related to password authentication named as Graphical Password Authentication.Here i have covered all the topics which are related to GPA .I will also provide a documentation regarding this topic if u need .So please comment below for the document and fallow @shobha rani
Schema markup is code (semantic vocabulary) that you put on your website to help the search engines return more informative results for users. If you've ever used rich snippets, you'll understand exactly what schema markup is all about.
2022 has been a whirlwind of a year for the SEO industry, and there’s no sign of slowing down.
This year alone, Google dropped eight confirmed and several unconfirmed updates – leaving many businesses scrambling to keep up.
With so much volatility, how can you adapt your SEO strategy to keep it fresh and relevant?
How will this year’s algorithm changes affect your 2023 SEO strategy?
How can you prepare for Google’s next move and get ahead of the curve?
In our next webinar, Pat Reinhart, VP of Customer Success at Conductor, discusses how to handle frequent algorithm changes and market shifts.
We’ll recap the biggest SEO insights of this year, share some expert predictions based on 2022’s algorithm updates, and uncover what next year may hold.
Key Takeaways:
What a crazy 2022 for Google means for 2023.
How the growth of social media search will impact strategy in 2023.
What the popularity of visual search will mean going forward.
If you struggled keeping up with this year’s constant changes, the SEO predictions you’ll discover in this webinar could be a game-changer for your business.
Are you ready to optimize your SEO strategy to stay competitive in 2023?
Internal linking is important for search engine optimization (SEO) as it allows users to navigate websites, establishes information hierarchies, and helps spread ranking power. Some key tactics for improving internal linking include choosing an optimal link structure, using text menus, adding keywords to anchor text strategically, cross-linking important pages, and eliminating duplicate, broken, or dangling links. Proper internal linking passes link juice between pages and helps increase page views and rankings.
In this presentation, we review the most important elements of setting up a Bing Webmasters Tool accounts. We also cover the details associated with reviewing data and error notifications.
The document discusses using handwritten signature verification as an additional security measure for computer systems. It notes that signature verification must be cheap, reliable, and unobtrusive. It explains that online signature verification analyzes dynamic features of signing, like speed and pressure, which presents challenges in differentiating consistent versus varying behavioral elements of a person's signature over time. The document outlines the signature acquisition and identification process using global features and training models, and notes the benefits of low error rates, ability to detect forgery even with copied signatures, fast training, and cheap storage requirements.
Applying NLP and Machine Learning to Keyword AnalysisDan Segal
From a presentation at Text Analytics Forum, Washington, DC, Nov. 7, 2018. Keyword research allows companies to learn the voice of their customers and tune their marketing messages for them. One of the challenges in keyword research is to find collections of keywords that are topically relevant and in demand and therefore likely to draw search traffic and customer engagement. Data sources such as search logs and search engine result pages provide valuable sources of keywords, as well as insight into audience-specific language. Additionally, cognitive technologies such as natural language processing and machine learning provide capabilities for mining those sources at scale. With a few tools and some minimal coding, an analyst can generate clusters of best-bet keywords that are not only syntactically similar but semantically related. This how-to talk presents some practical techniques for automated analysis of keyword source data using off-the-shelf APIs.
This document provides an introduction to web crawlers. It defines a web crawler as a computer program that browses the World Wide Web in a methodical, automated manner to gather pages and support functions like search engines and data mining. The document outlines the key features of crawlers, including robustness, politeness, distribution, scalability, and quality. It describes the basic architecture of a crawler, including the URL frontier that stores URLs to fetch, DNS resolution, page fetching, parsing, duplicate URL elimination, and filtering based on robots.txt files. Issues like prioritizing URLs, change rates, quality, and politeness policies are also discussed.
Anthony Marquez - esports and marketing portfolioAnthonyMarquez33
Anthony is an experienced marketing specialist with a track record of developing brand and marketing strategies that engage consumers. He has previously worked with Impact Gaming, developing their innovative content marketing strategy that led to over 30,000 engaged fans. For Jumper, an AI streaming app, he created a social media plan focused on open dialogue between the community and developers, generating over 1,000 app downloads from a single Reddit post and helping Jumper surpass 10,000 social media followers. Anthony ensures marketing messages align with companies' goals to resonate with consumers and create engaged communities.
This document presents a literature review and proposed methodology for detecting malicious URLs. It discusses prior work on blacklisting, heuristic approaches, and machine learning techniques for malicious URL detection. The proposed methodology develops three categories of features - URL lexical features based on TF-IDF, source code features to identify obfuscated JavaScript, and network features like payload size. These features would be used to train a machine learning model like SVM to classify URLs as malicious or benign in real-time. The goal is to automatically detect new web attacks and force attackers to make tradeoffs to evade detection.
Web crawling involves automated programs called crawlers or spiders that browse the web methodically to index web pages for search engines. Crawlers start from seed URLs and extract links from visited pages to discover new pages, repeating the process until a desired size or time limit is reached. Crawlers are used by search engines to build indexes of web content and ensure freshness through revisiting URLs. Challenges include the web's large size, fast changes, and dynamic content generation. APIs allow programmatic access to web services and information through REST, HTTP POST, and SOAP.
Crime Pattern Detection using K-Means ClusteringReuben George
Crime pattern detection uses data mining techniques like clustering to analyze crime data and identify patterns. This involves plotting past crimes geographically, clustering similar crimes to detect sprees, and analyzing the results to draw conclusions. It helps improve crime solving by learning from history and preempting future crimes. The method augments detectives' work but has limitations like relying on data quality. Overall, crime pattern detection aids operational efficiency and enhancing resolution rates by optimizing resource deployment based on observed crime trends.
Graphical password authentication system pptsNimisha_Goel
This document discusses different types of authentication systems including token-based, biometric-based, knowledge-based, recognition-based, pure recall-based, cued recall-based, and hybrid systems. It then focuses on graphical passwords, describing how they work by having users select images in a specific order. The document outlines a project to create an Android application for graphical password authentication to unlock private files using a cued recall system where users must select images in the correct sequence from a random grid. It discusses security considerations like guessing attacks and proposes that this approach provides stronger passwords while increasing the workload for attackers.
How To Rank #1 On Google | How To Improve Google Ranking | SEO Tutorial For B...Simplilearn
This presentation on SEO will help you understand the various factors that help you rank #1 on Google which includes keyword research, creating high-quality content, how to optimize the content using on-page elements and website level factors which influence Google ranking and we will also discuss off-site engagement. But, ranking on Google is not easy. There are a lot of factors that influence this. Now, let us get started and understand the major factors influencing Google ranking in the year 2019.
Below topics are explained in this SEO presentation:
1. Keyword research
2. High-quality content
3. Optimize on-page elements and website factors
4. Off-site engagement
Why learn Digital Marketing?
Businesses and recruiters prefer marketing professionals with genuine knowledge, skills, and experience verified by a certification that is accepted across industries. Continuous learning for any working professional is not only important for keeping themselves up to date with the current market trends, but it also helps them expand their array of skill set and become more flexible in the workplace.
What skills will you learn from this Digital Marketing course?
This course will enable you to:
1. Gain an in-depth understanding of the various digital marketing disciplines: search engine optimization (SEO), social media marketing, pay-per-click (PPC), website conversion rate optimization, web analytics, content marketing, mobile marketing, email marketing, programmatic buying, marketing automation and digital marketing strategy
2. Master digital marketing execution tools: Google Analytics, Google Ads, Facebook Marketing, Twitter Advertising, and YouTube Marketing
3. Become a virtual digital marketing manager for an e-commerce company with Mimic Pro simulations included in our course. Practice SEO, SEM, Website Conversion Rate Optimization, email marketing and more.
4. Gain real-life experience by completing projects using Google Analytics, Google Ads, Facebook Marketing, and YouTube Marketing
5 Create the right marketing messages tailored for the right audiences
6. Prepare for top digital marketing certification exams such as OMCA, Google Analytics, Google Ads, Facebook Marketing, and YouTube Marketing certifications
Who should take this Digital Marketing course?
Anyone who is looking to further his or her career in digital marketing should take this course, especially those seeking leadership positions. Any of these roles can benefit from the Digital Marketing Specialist training:
1. Marketing Managers
2. Digital Marketing Specialists
3. Marketing or Sales Professionals
4. Management, Engineering, Business, or Communication Graduates
5. Entrepreneurs or Business Owners
6. Marketing Consultant
Learn more at https://www.simplilearn.com/digital-marketing/digital-marketing-certified-associate-training
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
1) The document presents a system for detecting malicious links using machine learning techniques. It aims to improve the effectiveness of classifiers for identifying dangerous websites.
2) The system utilizes logistic regression as a supervised machine learning algorithm to categorize URLs based on their linguistic properties and behaviors. New attributes are extracted from URLs to train the classifier.
3) The proposed approach aims to identify malicious URLs based solely on their structure and properties, without relying on webpage content. This could lead to significant resource savings and a safer browsing experience for users.
This document outlines a presentation on detecting phishing websites using machine learning. The goals of the project are to develop a machine learning model to identify phishing URLs and integrate it into a web application. It will discuss collecting and preprocessing data, selecting machine learning algorithms, developing the web app, and addressing challenges with existing phishing detection techniques. The project aims to help reduce online theft and educate users on phishing risks and countermeasures.
Phishing Detection using Decision Tree ModelIRJET Journal
This document summarizes a research paper that proposes detecting phishing websites using a decision tree machine learning model. It begins by defining phishing attacks and their goal of stealing user data. It then describes extracting features from URLs to train a decision tree classifier, which achieved 95% accuracy in distinguishing real from phishing websites. The model was tested on a dataset of over 25,000 URLs. When users input a URL, the model classifies it as real or phishing to help protect users from fraudulent sites.
IRJET - An Automated System for Detection of Social Engineering Phishing Atta...IRJET Journal
1) The document presents a machine learning approach to detect phishing URLs using logistic regression. It trains a logistic regression model on a dataset of 420,467 URLs that have been classified as either phishing or legitimate.
2) It preprocesses the URLs using tokenization before training the logistic regression model. The trained model is able to classify new URLs with 96% accuracy as either phishing or legitimate based on the URL features.
3) The proposed approach provides an automated way to detect phishing URLs in real-time and help prevent phishing attacks. Future work could involve developing a browser extension using this approach and increasing the dataset size for higher accuracy.
Detecting malicious URLs using binary classification through ada boost algori...IJECEIAES
Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. In this study, we have developed a complete prototype of Malicious URL Detection using machine learning methods. In particular, we have attempted an exact formulation of Malicious URL exposure from a machine learning perspective and proposed an approach using the AdaBoost algorithm - the proposed approach has brought forward more accuracy than other existing algorithms.
Phishing Website Detection using Classification AlgorithmsIRJET Journal
This document discusses using machine learning algorithms to classify phishing websites. It begins with background on phishing and then discusses prior research applying algorithms like random forest, decision trees, SVM and KNN to detect phishing websites. The paper aims to address phishing website classification using various classifiers and ensemble learning approaches. It tests classifiers like random forest, decision tree, KNN, AdaBoost and GradientBoost on a phishing testing dataset and evaluates performance using metrics like accuracy, f1-score, precision and recall. The proposed approach achieves 97% accuracy in classifying phishing websites according to experimental results.
This document discusses the development of a machine learning model to accurately detect phishing websites in real-time. It begins with an introduction to the problem of phishing and the need for reliable phishing detection systems. It then discusses using supervised machine learning, specifically logistic regression, to classify websites as legitimate or phishing based on discriminatory features. The goal is to determine an optimal feature combination to train a classifier with high performance at detecting phishing sites. Previous literature on phishing detection is reviewed, including techniques using fuzzy rough set theory and a three-tiered approach using web crawler traffic data, content analysis, and URL analysis.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Deep Learning Technique for Web Phishing Detection Combined URL Features an...IJCNCJournal
The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
Detecting Phishing Websites Using Machine LearningIRJET Journal
1. The document proposes a system to detect phishing websites in real-time using machine learning. It trains a random forest classifier on a dataset of URLs and associated features to classify websites as legitimate or phishing.
2. The system collects URL and page content features from a user's browser and sends them to a cloud-based random forest model. The model was trained on over 40,000 URLs and achieved 97.36% accuracy at detecting phishing sites.
3. The proposed system provides advantages like real-time detection, using a large dataset for training, detecting new phishing sites, and independence from third-party services. It aims to protect users by identifying phishing sites before sensitive information is entered.
Phishing Website Detection Paradigm using XGBoostIRJET Journal
This document presents research on using the XGBoost (Extreme Gradient Boosting) machine learning algorithm to detect phishing websites. The researchers collected a dataset from Kaggle containing URL features and used it to train and test an XGBoost model. They found that the XGBoost model was able to accurately predict whether a URL led to a phishing website or legitimate website, achieving 86.4% accuracy according to the confusion matrix. The researchers concluded that XGBoost is a robust and efficient approach for phishing website detection due to its ability to generate highly accurate results with low bias and variance from the ensemble of decision trees.
IRJET- Detecting the Phishing Websites using Enhance Secure AlgorithmIRJET Journal
This document discusses detecting phishing websites using an enhanced secure algorithm. It begins by defining phishing attacks and how they are used to steal personal information from users. It then discusses how current techniques are not fully effective at stopping sophisticated phishing attacks. The proposed methodology checks for features of phishing websites, especially in URLs and domain names, to identify fake websites. Some features checked include IP addresses, long URLs, prefixes/suffixes, and symbols. Future work could involve updating datasets, detecting other attacks, and improving accuracy and efficiency. In conclusion, education is important to help users identify phishing attacks, as technical solutions are still limited.
Multi level parsing based approach against phishing attacks with the help of ...IJNSA Journal
The increasing use of internet all over the world, be it in households or in corporate firms, has led to an
unprecedented rise in cyber-crimes. Amongst these the major chunk consists of Internet attacks which are
the most popular and common attacks are carried over the internet. Generally phishing attacks, SSL
attacks and some other hacking attacks are kept into this category. Security against these attacks is the
major issue of internet security in today’s scenario where internet has very deep penetration. Internet has
no doubt made our lives very convenient. It has provided many facilities to us at penny’s cost. For instance
it has made communication lightning fast and that too at a very cheap cost. But internet can pose added
threats for those users who are not well versed in the ways of internet and unaware of the security risks
attached with it. Phishing Attacks, Nigerian Scam, Spam attacks, SSL attacks and other hacking attacks are
some of the most common and recent attacks to compromise the privacy of the internet users. Many a times
if the user isn’t careful, then these attacks are able to steal the confidential information of user (or
unauthorized access). Generally these attacks are carried out with the help of social networking sites,
popular mail server sites, online chatting sites etc. Nowadays, Facebook.com, gmail.com, orkut.com and
many other social networking sites are facing these security attack problems.
MALICIOUS URL DETECTION USING CONVOLUTIONAL NEURAL NETWORKijcseit
The World Wide Web has become an important part of our everyday life for information communication
and knowledge dissemination. It helps to transact information timely, rapidly and easily. Identifying theft
and identity fraud are referred as two sides of cyber-crime in which hackers and malicious users obtain the
personal data of existing legitimate users to attempt fraud or deception motivation for financial gain.
Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting
users to become victims of scams (monetary loss, theft of private information, and malware installation),
and cause losses of billions of dollars every year. To detect such crimes systems should be fast and precise
with the ability to detect new malicious content. Traditionally, this detection is done mostly through the
usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated
malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have
been explored with increasing attention in recent years. In this paper, I use a simple algorithm to detect
and predicting URLs it is good or bad and compared with two other algorithms to know (SVM, LR).
MALICIOUS URL DETECTION USING CONVOLUTIONAL NEURAL NETWORKijcseit
The World Wide Web has become an important part of our everyday life for information communication
and knowledge dissemination. It helps to transact information timely, rapidly and easily. Identifying theft
and identity fraud are referred as two sides of cyber-crime in which hackers and malicious users obtain the
personal data of existing legitimate users to attempt fraud or deception motivation for financial gain.
Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting
users to become victims of scams (monetary loss, theft of private information, and malware installation),
and cause losses of billions of dollars every year. To detect such crimes systems should be fast and precise
with the ability to detect new malicious content. Traditionally, this detection is done mostly through the
usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated
malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have
been explored with increasing attention in recent years. In this paper, I use a simple algorithm to detect
and predicting URLs it is good or bad and compared with two other algorithms to know (SVM, LR).
This document summarizes a research paper that proposes a phishing detector plugin called PHISCAN that uses machine learning to detect phishing websites. The plugin is developed for the Chrome browser using JavaScript and HTML. It extracts features from URLs to train classifiers like random forest that can accurately classify URLs as phishing or benign in less than a second while maintaining user privacy. The paper conducts a literature review of existing phishing detection systems and techniques using blacklists, heuristics, or machine learning. It motivates the need for the proposed plugin by discussing the increasing prevalence and sophistication of phishing attacks.
This document presents a proposed system for detecting phishing websites using a Chrome extension. The system compares URLs to entries in two databases - the Phishtank database of known phishing sites, and a local IndexedDB of frequently visited sites. If a match is found in either database, the Chrome extension will flag the site as potentially malicious by changing color. The system was tested on 53 URLs, achieving an accuracy of 92.45% at detecting phishing sites. The proposed system aims to alert users to phishing sites and protect them from disclosing sensitive information to attackers.
IRJET - Chrome Extension for Detecting Phishing WebsitesIRJET Journal
This document describes a Chrome browser extension that was developed to detect phishing websites using machine learning. The extension extracts features from URLs and classifies them as legitimate or phishing using a Random Forest classifier trained on a dataset of URLs. A user interface was designed for the extension using HTML, CSS and JavaScript. When a user visits a website, the extension automatically extracts 16 features from the URL and page content and inputs them into the Random Forest model to determine if the site is phishing or legitimate. The model was trained on a dataset of over 11,000 URLs labeled as phishing or legitimate. Evaluation of the model showed it achieved high accuracy in detecting phishing URLs. The goal of the extension is to help protect users from revealing
Phishing Website Detection Using Machine LearningIRJET Journal
This document describes research into detecting phishing websites using machine learning. It discusses how phishing websites trick users into providing sensitive information by posing as legitimate websites. The researchers collected a dataset of real URLs labeled as legitimate or phishing and preprocessed the data. They then trained several machine learning models using URL-based features like length of hostname, use of URL shortening services, presence of @ symbols or IP addresses. The goal is to identify the most effective model for classifying URLs based on precision, false positive and false negative rates to help detect phishing websites in real-time.
KNOWLEDGE BASE COMPOUND APPROACH AGAINST PHISHING ATTACKS USING SOME PARSING ...cscpconf
The increasing use of internet all over the world, be it in households or in corporate firms, has led to an unprecedented rise in cyber-crimes. Amongst these the major chunk consists of
Internet attacks which are the most popular and common attacks are carried over the internet. Generally phishing attacks, SSL attacks and some other hacking attacks are kept into this
category. Security against these attacks is the major issue of internet security in today’s scenario where internet has very deep penetration. Internet has no doubt made our lives very
convenient. It has provided many facilities to us at penny’s cost. For instance it has made communication lightning fast and that too at a very cheap cost. But internet can pose added
threats for those users who are not well versed in the ways of internet and unaware of the security risks attached with it. Phishing Attacks, Nigerian Scam, Spam attacks, SSL attacks and other hacking attacks are some of the most common and recent attacks to compromise the privacy of the internet users. This paper discusses a Knowledge Base Compound approach
which is based on query operations and parsing techniques to counter these internet attacks using the web browser itself. In this approach we propose to analyze the web URLs before
visiting the actual site, so as to provide security against web attacks mentioned above. This approach employs various parsing operations and query processing which use many techniques to detect the phishing attacks as well as other web attacks. The aforementioned approach is completely based on operation through the browser and hence only affects the speed of browsing. This approach also includes Crawling operation to detect the URL details to further enhance the precision of detection of a compromised site. Using the proposed methodology, a new browser can easily detects the phishing attacks, SSL attacks, and other hacking attacks.
With the use of this browser approach, we can easily achieve 96.94% security against phishing as well as other web based attacks
Similar to Malicious-URL Detection using Logistic Regression Technique (20)
Total Ionization Cross Sections due to Electron Impact of Ammonia from Thresh...Dr. Amarjeet Singh
In the present paper, we have employed modified Khare-BEB method [Atoms, (2019)] to evaluate total ionization cross sections by the electron impact for ammonia in energy range from the ionization threshold to 10 MeV. The theoretical ionization cross sections have been compared to the available previous theoretical and experimental results. The collision parameters dipole matrix squared M_j^2 and CRP also have been calculated. The present calculations were found in remarkable agreement with the available experimental results.
A Case Study on Small Town Big Player – Enjay IT Solutions Ltd., BhiladDr. Amarjeet Singh
Adequately trained Manpower is a problem that affects the IT industry as a whole, but it is particularly acute for Enjay IT Solution. Enjay's location in a semi-urban or rural area makes it even more difficult to find a talented employee with the right skills. As the competition for skilled workers grows, it becomes more difficult to attract and keep those workers who have the requisite training and experience.
Effect of Biopesticide from the Stems of Gossypium Arboreum on Pink Bollworm ...Dr. Amarjeet Singh
Pink bollworm and Lepidoptera development quickly in numbers which is a typical animal group that produces around 100 youthful ones inside certain days or weeks. This assault influences the harvests broadly in the tropical and sub-tropical temperature areas. Thus, to keep up with the yield of harvests the vermin ought to be kept away by utilizing pesticides. The unnecessary measure of the purpose of pesticides influences the dirt, land, and as well as human well-being, and contaminates the climate. Thus, an ozone-accommodating biopesticide is extracted from the stems of the Gossypium arboreum. Thus, the extraction of biopesticide from the stems of Gossypium arboreum demonstrated that the quantity of pink bollworm and Lepidoptera is diminished step by step in the wake of showering the arrangement on the impacted region of the plant because of the presence of the gossypol.
Artificial Intelligence Techniques in E-Commerce: The Possibility of Exploiti...Dr. Amarjeet Singh
This document discusses the potential applications of artificial intelligence techniques in e-commerce in Saudi Arabia. It begins with an introduction to e-commerce and AI, and how AI is being used increasingly in e-commerce applications worldwide. It then reviews literature on how AI can be integrated into e-commerce systems and the various applications of AI in e-commerce. Some key applications discussed include AI assistants, personalized recommendations, demand forecasting, supply chain management, fraud detection and more. The document concludes that Saudi Arabia is well positioned to benefit from using AI to boost its growing e-commerce sector.
Factors Influencing Ownership Pattern and its Impact on Corporate Performance...Dr. Amarjeet Singh
This document summarizes a research study that analyzed the factors influencing ownership patterns of selected Indian companies and the impact of ownership patterns on corporate performance. The study used data from 5 industries over 5 years from 2017 to 2021. Multiple regression, ANOVA, and correlation analyses were conducted. The results found that the percentage of independent directors on the board and the size of the company had a significant impact on Indian promoter holdings. Additionally, non-institutional ownership was found to have a significant impact on corporate performance measures like asset utilization ratio. The study concluded that ownership patterns can influence corporate performance and companies should work to optimize factors like debt-equity ratio and board independence to improve financial outcomes.
An Analytical Study on Ratios Influencing Profitability of Selected Indian Au...Dr. Amarjeet Singh
Every country with a well-developed transportation network has a well-developed economy. The automobile industry is a critical engine of the nation's economic development. The automobile industry has significant backward and forward links with every area of the economy, as well as a strong and progressive multiplier impact. The automotive industry and the auto component industry are both included in the vehicle industry. It includes passenger waggons, light, medium, and heavy commercial vehicles, as well as multi-utility vehicles such as jeeps, three-wheelers, military vehicles, motorcycles, tractors, and auto-components such as engine parts, batteries, drive transmission parts, electrical, suspension and chassis parts, and body and other parts. In the last several years, India's automobile sector has seen incredible growth in sales, production, innovation, and exports. India's car industry has emerged as one of the best in the world, and the auto-ancillary sector is poised to assist the vehicle sector's expansion. Vehicle manufacturers and auto-parts manufacturers account for a significant component of global motorised manufacturing. Vehicle manufacturers from across the world are keeping a close eye on the Indian auto sector in order to assess future demand and establish India as a global manufacturing base. The current research focuses on three automotive behemoths: TATA Motors, MRF, and Mahindra & Mahindra.
A Study on Factors Influencing the Financial Performance Analysis Selected Pr...Dr. Amarjeet Singh
The growth of a country's banking sector has a significant impact on its economic development. The banking sector plays a critical role in determining a country's economic future. A well-planned, structured, efficient, and viable banking system is an essential component of an economy's economic and social infrastructure. In modern society, a strong banking system is required because it meets the financial needs of the modern society. In a country's economy, the banking system plays a crucial role. Because it connects surplus and deficit economic agents, the bank is the most important financial intermediary in the economy. The banking system is regarded as the economy's lifeline. It meets the financial needs of commerce, industry, and agriculture. As a result, the country's development and the banking system are intertwined. They are critical in the mobilisation of savings and the distribution of credit to various sectors of the economy. India's private sector banks play a critical role in the country's economic development. So The financial performance of private sector banks must be evaluated carefully.
An Empirical Analysis of Financial Performance of Selected Oil Exploration an...Dr. Amarjeet Singh
After the United States, China, and Japan, India was the world's fourth biggest consumer of oil and petroleum products. The nation is significantly reliant on crude oil imports, the majority of which come from the Middle East. The Indian oil and gas business is one of the country's six main sectors, with important forward links to the rest of the economy. More than two-thirds of the country's overall primary energy demands are met by the oil and gas industry. The industry has played a key role in placing India on the global map. India is now the world's sixth biggest crude oil user and ninth largest crude oil importer. In addition, the country's portion of the worldwide refining market is growing. India's refining industry is now the world's sixth biggest. With plans for Reliance Petroleum Limited to commission another refinery with a capacity of 29 MTPA next 16 to its 33 MTPA refinery in Jamnagar, Gujarat, this position is projected to be enhanced. As a consequence, the Reliance refinery would be the biggest single-site refinery in the world. Based on secondary data gathered from CMIE, the current research examines the ratios influencing the profitability of selected oil exploration and production businesses in India during a 10-year period.
Since 1991, thanks to economic policy liberalization, the Indian economy has entered an era in which Indian businesses can no longer disregard global markets. Prior to the 1990s, the prices of a variety of commodities, metals, and other assets were carefully regulated. Others, which were not rolled, were primarily dependant on regulated input costs. As a result, there was no uncertainty and, as a result, no price fluctuations. However, in 1991, when the process of deregulation began, the prices of most items were deregulated. It has also resulted in the exchange being partially deregulated, easing trade restrictions, lowering interest rates, and making significant advancements in foreign institutional investors' access to the capital markets, as well as establishing market-based government securities pricing, among other things. Furthermore, portfolio and securities price volatility and instability were influenced by market-determined exchange rates and interest rates. As a result, hedging strategies employing a variety of derivatives were exposed to a variety of risks. The Indian capital market will be examined in this study, with a focus on derivatives.
Theoretical Estimation of CO2 Compression and Transport Costs for an hypothet...Dr. Amarjeet Singh
This document discusses theoretical estimates for the costs of compressing and transporting CO2 from a hypothetical carbon capture and storage project at the Saline Joniche Power Plant in Italy. It first provides background on the power plant project from 2008 that proposed converting the site to coal power. It then details the methodology used to size the compression system, estimating power needs for multi-stage compression up to pipeline pressures. Costs are considered for constructing, operating, and maintaining both the compression plant and pipeline to a potential offshore storage site. The aim is to evaluate retrofitting the existing plant with carbon capture and storage as a way to enable continued coal power production consistent with climate goals.
Analytical Mechanics of Magnetic Particles Suspended in Magnetorheological FluidDr. Amarjeet Singh
In this paper, the behavior of MR particles has been systematically investigated within the scope of analytical mechanics. . A magnetorheological fluid belongs to a class of smart materials. In magnetorheological fluids, the motion of magnetic particles is controlled by the action of internal and external forces. This paper presents analytical mechanics for the interaction of system of particles in MR fluid. In this paper, basic principles of Analytical Mechanics are utilized for the construction of equations.
Techno-Economic Aspects of Solid Food Wastes into Bio-ManureDr. Amarjeet Singh
Solid waste is health hazard and cause damage to the environment due to improper handling. Solid waste comprises of Industrial Waste (IW), Hazardous Waste (HW), Municipal Solid Waste (MSW), Electronic waste (E-waste), Bio-Medical Waste (BMW) which depend on their supply & characteristics. Food waste or Bio-waste composting and its role in sustainable development is explained in food waste is a growing area of concern with many costs to our community in terms of waste collection, disposal and greenhouse gases. When rotting food ends up in landfill it turns into methane, a greenhouse gas that is particularly damaging to the environment. Composting is biochemical process in which organic materials are biologically degraded, resulting in the production of organic by products and energy in the form of heat. Heat is trapped within the composting mass, leading to the phenomenon of self-heating. This overall process provide us Bio-Manure.
Crypto-Currencies: Can Investors Rely on them as Investment Avenue?Dr. Amarjeet Singh
The purpose of this study is to examine investors’ perceptions about investing in crypto-currencies. We think that investors trust in crypto-currencies is largely driven by crypto-currency comprehension, trust in government, and transaction speed. This is the first study to examine crypto-currencies from the investor’s perspective. Following that, we discover important antecedents of crypto-currency confidence. Second, we look at the government's role in crypto-currencies. The importance of this study is: first, crypto-currencies have the potential to disrupt the current economic system as the debate is all about impact of decentralization of transactions; thus, further research into how it affects investors trust is essential; and second, access to crypto-currencies. Finally, if Fin-Tech companies or banks want to enter the bitcoin industry may not attract huge advertising costs as well as marketing to soothe clients' concerns about investing in various digital currencies The research sheds light on indecisiveness in the context of marketing aspects adopted by demonstrating investors are aware about the crypto.
Awareness of Disaster Risk Reduction (DRR) among Student of the Catanduanes S...Dr. Amarjeet Singh
The Island Province of Catanduanes is prone to all types of natural hazards that includes torrential and heavy rains, strong winds and surge, flooding and landslide or slope failures as a result of its geographical location and topography. RA 10121 mandates local DRRM bodies to “encourage community, specifically the youth, participation in disaster risk reduction and management activities, such as organizing quick response groups, particularly in identified disaster-prone areas, as well as the inclusion of disaster risk reduction and management programs as part of youth programs and projects. The study aims to determine the awareness to disaster of the student of the Catanduanes State University. The disaster-based questionnaire was prepared and distributed among 636 students selected randomly from different Colleges and Laboratory Schools in the University
The Catanduanes State University students understood some disaster-related concepts and ideas, but uncertain on issues on preparedness, adaptation, and awareness on the risks inflicted by these natural hazards. Low perception on disaster risks are evidently observed among students. The responses of the students could be based on the efficiency and impact of the integration of DRR education in the senior high school curriculum. Specifically, integration of the concepts about the hazards, hazard maps, disaster preparedness, awareness, mitigation, prevention, adaptation, and resiliency in the science curriculum possibly affect the knowledge and understanding of students on DRR. Preparedness drills and other forms of capacity building must be done to improve awareness of the student towards DRRM.
The study further recommends that teachers and instructor must also be capacitated in handling disaster as they are the prime movers in the implementation of the DRRM in education. Preparedness drills and other forms of capacity building must be done to improve awareness of the student towards DRRM. Core subjects in Earth Sciences must be reinforced with geologic hazards. Learning competencies must also be focused on hazard identification and mapping, and coping with different geologic disaster.
The 1857 war was a watershed moment in the history of the Indian subcontinent. The battle has sparked academic debate among historians and sociologists all around the world. Despite the fact that it has been more than 150 years, this battle continues to pique the interest of historians. The war's causes and events that occurred throughout the conflict, persons who backed the British and anti-British fighters, and the results and ramifications, are all aspects of this conflict. In terms of outcomes, many academics believe that the war was a failure for those who started it. It is often assumed that the Indians who battled the British in this conflict were unable to achieve their goals. Many gains accrued to Indians as a result of the conflict, but these achievements are overshadowed by the dispute over the war's failure. This research effort focuses on the war's achievements for India, and the significance of those achievements.
Haryana's Honour Killings: A Social and Legal Point of ViewDr. Amarjeet Singh
Life is unpredictably unpredictable. Nobody knows what will happen in the next minute of their lives. In this circumstance, every human being has the right and desire to conduct their lives according to their own desires. No one should be forced to live a life solely for the benefit and reputation of others. Honour killing is defined as the assassination of a person, whether male or female, who refuses to accept the family's arranged marriage or decides to move her or his marital life according to her or his wishes solely because it jeopardizes the family's honour. The family's supreme authority looks after the family's name but neglects to consider the love and affection shared among family members. I have discussed honour killing in India in my research work. This sort of murder occurs as a result of particular triggers, which are also examined in relation to the role of the law in honour killing. No one can be released free if they break the law, and in this case, it is a felony that violates various regulations designed to safeguard citizens. This crime is similar to many others, but it is distinct enough to be differentiated in the report. When the husband is of low social standing, it lowers the position and caste of the female family, prompting the male family members to murder the girl. But they forget that the girl is their kid and that while rank may be attained, a girl's life can never be replaced, and that caste is less valuable than the girl's life and love spent with them.
Optimization of Digital-Based MSME E-Commerce: Challenges and Opportunities i...Dr. Amarjeet Singh
This document summarizes a research article about optimizing digital-based MSME e-commerce during the COVID-19 pandemic. The article discusses how the pandemic severely impacted MSMEs, with many going out of business. However, digitalization and e-commerce provide opportunities for MSMEs to transform their business models. The article reviews literature showing how technologies like websites, social media, and mobile applications can help MSMEs reach more customers online. Case studies of MSMEs in different countries found that those utilizing digital tools through e-commerce were more successful compared to those relying only on offline sales. The article concludes digitalization is both a challenge and opportunity for MSMEs to adapt their traditional business models and survive or grow
Modal Space Controller for Hydraulically Driven Six Degree of Freedom Paralle...Dr. Amarjeet Singh
This paper presents the Modal space decoupled control for a hydraulically driven parallel mechanism has been presented. The approach is based on singular values decomposition to the properties of joint-space inverse mass matrix, and mapping of the control and feedback variables from the joint space to the decoupling modal space. The method transformed highly coupled six-input six-output dynamics into six independent single-input single-output (SISO) 1 DOF hydraulically driven mechanical systems. The novelty in this method is that the signals including control errors, control outputs and pressure feedbacks are transformed into decoupled modal space and also the proportional gains and dynamic pressure feedback are tuned in modal space. The results indicate that the conventional controller can only attenuate the resonance peaks of the lower eigenfrequencies of six rigid modes properly, and the peaking points of other relative higher eigenfrequencies are over damped, The further results show that it is very effective to design and tune the system in modal space and that the bandwidth increased substantially except surge (x) and sway (y) motions, each degree of freedom can be almost tuned independently and their bandwidths can be increased near to the undamped eigenfrequencies.
It is a known fact that a large number of Steel Industry Expansion projects in India have been delayed due to regulatory clearances, environmental issues and problems pertaining to land acquisition. Also, there are challenges in the tendering phase that affect viability of projects thus delaying implementation, construction phase is beset with over-runs and disputes and last but not the least; provider skills are weak all across the value chain. Given the critical role of Steel Sector in ensuring a sustained growth trajectory for India, it is imperative that we identify the core issues affecting completion of infrastructure projects in India and chalk out initiatives that need to be acted upon in short term as well as long term.
A blockchain is a decentralised database that is shared across computer network nodes. A blockchain acts as a database, storing information in a digital format. The study primarily aims to explore how in the future, block chain technology will alter several areas of the Indian economy. The current study aims to obtain a deeper understanding of blockchain technology's idea and implementation in India, as well as the technology's potential as a disruptive financial technological innovation.
Secondary sources such as reports, journals, papers, and websites were used to compile all the data. Current and relevant information were utilised to help understand the research goals. All the information is rationally organised to fulfil the objectives. The current research focuses on recommendations for enhancing India's Blockchain ecosystem so that it may become one of the best in the world at utilising this new technology.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
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Malicious-URL Detection using Logistic Regression Technique
1. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 6 (December 2019)
www.ijemr.net https://doi.org/10.31033/ijemr.9.6.18
108 This work is licensed under Creative Commons Attribution 4.0 International License.
Malicious-URL Detection using Logistic Regression Technique
Vanitha N1
and Vinodhini V2
1
Assistant Professor, Department of Information Technology, Dr. N.G.P Arts and Science College, Coimbatore, INDIA
2
Associate Professor, Department of Information Technology, Dr. N.G.P Arts and Science College, Coimbatore, INDIA
2
Corresponding Author: vinodhini@drngpasc.ac.in
ABSTRACT
Over the last few years, the Web has seen a
massive growth in the number and kinds of web services.
Web facilities such as online banking, gaming, and social
networking have promptly evolved as has the faith upon them
by people to perform daily tasks. As a result, a large amount
of information is uploaded on a daily to the Web. As these
web services drive new opportunities for people to interact,
they also create new opportunities for criminals. URLs are
launch pads for any web attacks such that any malicious
intention user can steal the identity of the legal person by
sending the malicious URL. Malicious URLs are a keystone
of Internet illegitimate activities. The dangers of these sites
have created a mandates for defences that protect end-users
from visiting them. The proposed approach is that classifies
URLs automatically by using Machine-Learning algorithm
called logistic regression that is used to binary classification.
The classifiers achieves 97% accuracy by learning phishing
URLs.
Keywords— URL, Logistic Regression, Machine
Learning, Data
I. INTRODUCTION
Phishing websites are being employed to steal
personal information, such as credit cards and passwords,
and to implement drive-by downloads. Phishing is popular
among muggers since it is easier to trick someone. In most
cases, such annoying activity engages network resources
intended for other use into clicking a malicious link which
seems legitimate than trying to break through a computer’s
defense systems. In most cases, such annoying activity
engages network properties intended for other uses, and
nearly always threatens the security of the network and/or
its data. Properly designing and deploying a Phishing URL
will help block the intruders. phishing domain (or
Fraudulent Domain) characteristics, the features that
discriminate them from appropriate domains, why it is
important to detect these domains, and how they can be
detected using machine learning techniques.
Background Study
This section discusses related methodologies
used by researchers who have tried to solve the problem of
phishing URL detection and classification.
The authors Mohammed Nazim Feroz and,
Susan Mengel[3] has describes an approach that classifies
URLs automatically based on their lexical and host-based
features. These methods are able to learn highly analytical
models by extracting and automatically Mahout is
established for such scalable machine learning problems,
and online learning is considered over batch learning. The
classifier achieves 93-95% accuracy by detecting a large
number of phishing hosts, while maintaining a modest false
positive rate.
Justin Ma, Lawrence K. Saul, Stefan Savag and,
Geoffrey M. Voelker[4] describes an approach to this
problem based on automated URL classification, using
statistical methods to discover the tell-tale lexical and
host-based properties of malicious Web site URLs. These
methods are able to learn highly analytical models by
extracting and repeatedly examining tens of thousands of
features potentially indicative of suspicious URLs. The
resulting classifiers obtain 91-94% accuracy, detecting
large numbers of malicious Web sites from their URLs,
with only modest false positives.
Frank Vanhoenshoven, Gonzalo N apoles,
Rafael Falcot, Koen Vanhoof and Mario K¨oppent
Universiteit Hasselt Campus Diepenbeek [1]determines
online learning approaches for detecting malicious Web
sites (those involved in criminal scams) using lexical and
host-based features of the related URLs. We show that this
application is mostly suitable for online algorithms as the
size of the training data is larger which can be efficiently
processed in batch also the distribution of features that
typify malicious URLs is changing unceasingly.
II. PROPOSED METHOD
To ripen a defined manners from the data-sets,
the model is to be sketched out like obliquely identify the
data from which it has to be practised. The pillar of this
model is data-sets and hence it should be sufficient and
perfect data for good as well as bad URLs existing in the
data for the model to be trained upon. A list of URLs that
have been classified as either malicious or benevolent and
characterize each URL via a set of attributes such as
number of dots presents in URL, distance of the URL,
token-based diagrams such as google.com. To train a
model, binary classification technique which is also called
as binary regression technique is used in a model.
Advantage of proposed method
The proposed method acquires maximum learning
accuracy comparing to other machine learning
algorithms.
2. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 6 (December 2019)
www.ijemr.net https://doi.org/10.31033/ijemr.9.6.18
109 This work is licensed under Creative Commons Attribution 4.0 International License.
It consumes less time to learning phishing URLs.
III. UNIFORM RESOURCE LOCATOR
(URL)
A URL is a exclusive identifier used to locate a
resource on the internet. It is also denoted to as a web
address. URLs consist of multiple parts -- including a
protocol and domain name -- that tell a web browser how
and where to recover a resource. End operators use URLs
by typing them directly into the address bar of a browser or
by ticking a hyperlink found on a webpage, bookmark list,
in an email or from additional application. A URL is the
most collective type of Uniform Resource Identifier (URI).
URIs are strings of typescripts used to identify a source
over a network. URLs are vital to traversing the internet.
URL Structure
The URL encompasses the name of
the protocol required to access a resource, as well as a
resource name. The first portion of a URL identifies what
protocol to use as the primary access medium. The second
portion identifies the IP address or domain name -- and
possibly subdomain -- where the resource is located.
After the domain, a URL can also specify:
A path to a exact page or file within a domain;
A network port to use to make the link.
A request or search parameters used -- commonly
found in URLs for search results.
Fig 1: Structure of URL
Malicious URL
Within the gathering of cyber threats out there,
mischievous websites play a critical role in today’s attacks
and scams.
Malicious URLs can be carried to users via email,
text message, pop-ups or sheltered advertisements. The
end effect can often be downloaded malware, spyware,
ransom ware, compromised accounts. It should be obvious
that being aware of what a Malicious URL is, and how it
can do harm. Launch phishing movements meant to
bargain your private information,
When we ticking a URL it directs us to phishing
Sites and get you to install malware, viruses or Trojans,
whether by transferring a file or as a drive-by-download
that is provoked by something as simple as a mouse-over or
other trick.
Example of Malicious URL
timothycopus.aimoo.com
cracks.vg/d1.php
svisionline.de/ngfi/administrator/components/com
_backup/classes/fx29id.com
IV. MACHINE-LEARNING APPROACH
Machine learning Methodology – This approach
consists of two parts,
First chunk is Machine Learning model and the second
chunk is Data-sets.
Fig 2: Classification of Machine Learning Algorithms
First Chunk- Machine learning
Machine learning is a subsection of artificial
intelligence (AI) that offers systems the skill to
mechanically learn and improve from experience without
being explicitly programmed. Machine learning
concentrates on the development of computer
programs that can access data and use it learn for
themselves.
The procedure of learning begins with data, such
as examples, direct understanding, or instruction, in order
to look for outlines in data and make better conclusions in
the feature based on the examples that provide. The main
aim is to permit the computers to learn
automatically without human interference or assistance
and regulate actions consequently.
Supervised learning
Supervised learning, in the background of artificial
intelligence (AI) and machine learning, is a type of system
in which both input and preferred output data are provided.
Input and output data are labelled for classification to
deliver a learning basis for future data processing.
Supervised learning models have some benefits over the
unsupervised approach, but they also have boundaries. The
3. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 6 (December 2019)
www.ijemr.net https://doi.org/10.31033/ijemr.9.6.18
110 This work is licensed under Creative Commons Attribution 4.0 International License.
systems are more likely to make decisions that humans can
relate to, for example, because humans have provided the
basis for decisions. However, in the case of a
retrieval-based method, supervised learning systems have
distress dealing with new information.
Regression
Regression predictive modeling is the task of
approaching a mapping function (f) from input variables
(X) to a continuous output variable (y). A constant output
variable is a real-value, such as an integer or floating point
value
Stepwise regression
It is used when there is doubt about which of a set
of analyst variables should be included in a regression
model. It works by adding and/or removing separate
variables from the model and detecting the resulting effect
on its accuracy. Stepwise regression is no longer stared as a
valid tool for dimensionality reduction because it yields
unstable results that heavily over fit the training data.
Multivariate Adaptive Regression Splines (MARS)
It is a form of regression analysis. It is
a non-parametric regression technique and can be seen as
an extension of linear models that automatically models
nonlinearities and communicate between variables.
Logistic Regression
The logistic regression technique includes
dependent variable which can be signified in the binary (0
or 1, true or false, yes or no) values, means that the result
could only be in either one form of two. For example, it can
be applied when we need to find the probability of positive
or fail event. Here, the same method is used with the
additional sigmoid function, and the value of Y ranges
from 0 to 1.Consider a model with two predictors, x1
and x2; these may be constant variables or indicator
functions for binary variables (taking value 0 or 1). Fig 5
represents the comparison method [7].
1/(1+e^-x)
Fig 5: Logistic Function
Comparison of linear and Logistic Regression
Linear and Logistic regression are the furthermost
basic form of regression which are usually used. The
crucial difference between these two is that Logistic
regression is used when the dependent variable is binary in
nature. In difference, Linear regression is used when the
dependent variable is continuous and nature of the
regression line is linear.
Regression is a method is used to predict the value
of a response (dependent) variables, from one or more
predictor variables, where the variable is numeric. There
are several forms of regression such as linear, multiple,
logistic, polynomial, non-parametric, etc.
V. WORKING METHODOLOGY
System Flow
Fig4: system overflow
Second Chunk -Data Sets
The training data set in Machine Learning is the
genuine dataset used to train the model for performing
various actions. This is the actual data the current
development process models learn with several API and
algorithm to train the machine to work automatically.
Fig6: Dataset Classification
Training Dataset
There are two types of data sets – Training, and
Test that are used at several stage of development. Training
dataset is the leading of two of them, while test data
functions as closure of approval and you don’t need to use
till the end of the development.
Test Dataset
This is the data typically used to provide an
balanced evaluation of the final that are completed and fit
Start Collection of
dataset
Extraction of
feature
Bad
URL
Logistic
algorithm
Creation of training
and testing data
Good URL
4. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 6 (December 2019)
www.ijemr.net https://doi.org/10.31033/ijemr.9.6.18
111 This work is licensed under Creative Commons Attribution 4.0 International License.
on the training dataset. Essentially, such data is used for
testing the model whether it is responding or working
properly or not.
All of URL in our dataset are labelled
Data sets are collected from
https://github.com/VAD3R-95/Malicious-URL-
Detection/blob/master/data_URL.csv yahoo-phish tank
Extraction of Feature
In machine learning, a feature is an separate
assessable property or characteristic of a phenomenon
being detected. Picking informative, perceptive and
independent features is a vital step for effective algorithms
in pattern recognition, classification and regression. When
the input data to an algorithm is too huge to be processed
and it is suspected to be redundant. then it can be
converted into a reduced set of features The selected
features are expected to contain the appropriate
information from the input data, so that the desired task can
be performed by using this reduced demonstration instead
of the complete initial data. Since the URLs are in our
dataset are different from our normal text documents so we
have to use text feature extraction method for construct a
feature vector. Fig 7 shows the feature factorizing methods
[8].
Fig 7: Feature Vectorizing Methods
Count Vectorizer
The most straightforward one, it counts the
number of times a token shows up in the document and uses
this value as its weight.
Hash Vectorizer
This one is measured to be as memory efficient as
possible. In its place of storing the tokens as strings, the
vectorizer applies the hashing trick to encode them as
numerical indexes. The problem of this method is that once
vectorized, the features’ names can no longer be recovered.
TF-IDF Vectorizer
TF-IDF stands for “term frequency-inverse
document frequency”, means the weight allocated to each
token not only depends on its frequency in a document but
also how persistent that term is in the entire corpora.
Preparing Data
Subsequently the URLs are dissimilar from our
typical text documents, we need to engrave our
own purification method to get the appropriate data from
raw URLs. To contrivance our distillation function in
python to filter the URLs with following code as shown in
trial code. This will give us the desired URL data-set values
to sequence the model and test it. The data-set will partake
two pillar, one is for URLs and other is for labels. Here we
have proceeded with the use of Tf-idf machine learning
text feature extraction approach from the python module of
sk-learn.
Feature Vector Construction
http:// www. Bfuduuioolfb .mobi /ws/ ebayisapi
.dll
Fig:8 Vector Construction
Features Considered
Blacklist Queries
Lexical Features
Blacklist
List of known malicious sites from yahoo phish
tank, google crawlers.
List of malicious URLs from various domain
Providers like SORBS, URIBL, SURBL,
Spamhaus.
Lexical Features
Tokens in URL hostname + path
Length of URL
Entropy of the domain name
Reading Data
It is essential to recite the data-sets into data
frames and matrix, which can be presumed by the
Vectorizer. After Vectorizer data are arranged and
distributed onto the term-frequency and inverse document
frequency, which is called as text extraction approach.
Pandas component in python is used for the task to be
implemented.
Splitting Data
The data we use is typically split into training data
and test data. The training set covers a known output and
the model learns on this data in order to be universal to
other data later on. The test dataset (or subset) is to test
our model’s prediction on this subset. In order to use the
splitting method we have to import pandas library
training set—a subset to train a model. (80%)
test set—a subset to test the trained model. (20%)
Training Model
To train model call the logistic algorithm that is
imported using sklearn model from python sci-kit library.
(From sklearn.linear_model import Logistic Regression).
It uses train data set for learning. After learning it prints
score of trained model.
Feature
vectorizer
Hash
Vectorizer
TF-IDF Vectorizer
Count
Vectorizer
5. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 6 (December 2019)
www.ijemr.net https://doi.org/10.31033/ijemr.9.6.18
112 This work is licensed under Creative Commons Attribution 4.0 International License.
Fig 9. Training model
Testing Model
Pass the various URLs as inputs in to trained
model. It predicts whether the URL is good or bad and
returns the output as good/bad.
VI. EXPERIMENT AND RESULT
DISCUSSION
The Model is to be sketched to detect malicious
URLs by using machine learning methods. Machine
learning model and datasets are the two dissimilar
quantities of the process.
Table I. Learning Accuracy –random Splitting
Split
ratio
Random
forest
Naive Bayes Logistic
Regression
1:1 88.36 76.46 91.23
4:1 92.48 87.11 96.21
10:1 95.46 92.48 98.42
Since the URLs are always dissimilar from our
usual script, to get appropriate data from URLs, own
sanitization method is written using pandas package. Then
the data in dataset are imported as data frames and arrays,
for that machine-learning numpy package is used. The data
can be understood by vectorizer which we prepared by
using Tf-idf machine learning, this is the type of machine
learning for text feature extraction method from the python
module called sklearn. Then the logistic regression method
is used to train and test our data.
The model is trained for multiple times with
different data split ratio such as 1:1, 1:4, 10:1 and
compared with previous models and the learning accuracy
score is compared.
The Table I shows the comparison of Logistic
Regression method with Other methods.
Training Model
The model is trained with various split ratios
such as 1:1,4:1,10:1 and learning accuracy is noted.
proposed method is compared with other algorithms such
as naïve bays and random forest. The comparison of
learning accuracy is shown in the below table.
Fig 10: Testing Model
Fig. 11 : Comparison of Logistic Regression with Other
methods with split ratio 1:1
Fig. 12: Comparision of Logistic Regression with Other
methods
Fig. 13
6. International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 6 (December 2019)
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113 This work is licensed under Creative Commons Attribution 4.0 International License.
Fig. 14
VII. CONCLUSION AND FUTURE
ENHANCEMENT
Malicious URL detection plays a serious role for
many cyber security applications, and networking
applications. The majority of computer attacks are
launched by visiting a malicious webpage. A user can be
tricked into voluntarily giving away private information on
a phishing page or become target to a drive-by download
resulting in a malware infection. In this approach we
showed phishing URL detection by using machine learning
algorithm called logistic regression, it obtains maximum
learning accuracy comparing to other algorithms such as
naïve bays, random forest. In future there is an idea to
increase training and testing data and to find vary of
accuracy, and can deploy as web content for all the network
connected devices. In addition to that adding some more
feature like host based (WHOIS) features makes our model
more accurate.
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