thesis 36 44 Final
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This document discusses cybercrime and phishing attacks. It presents an automated phishing detection method developed by Md. Merazul Islam and Shuvradeb Barman Srijon from Khulna University of Engineering and Technology. The method uses techniques like checking the IP address, URL, and page content to identify phishing sites within seconds with high accuracy ranging from 79% to 100%. The approach aims to reduce the profitability of phishing attacks by automating detection.
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A very common crime in cyber world - Phishing. Its necessary to make people aware of the possible scam/cyber crime. Awareness regarding the same and educating people in times of digitization is a must nowadays.... Its also important too take proper steps regarding the same.
Phishing
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Intro phishing
A very common crime in cyber world - Phishing. Its necessary to make people aware of the possible scam/cyber crime. Awareness regarding the same and educating people in times of digitization is a must nowadays.... Its also important too take proper steps regarding the same.
Phishing
Phishing involves attempting to acquire sensitive information like usernames, passwords, and credit card details by masquerading as a trustworthy entity. Common phishing techniques include email spoofing and creating fake websites that look identical to legitimate ones. Phishing can be prevented by being wary of unsolicited requests for information, verifying website URLs, using security software, and reporting any suspicious activity.
Phishing techniques
Phishing is a method used by hackers to steal personal information through deceptive means such as spam or fake websites. There are many types of phishing techniques, including instant messaging links to fake websites, trojan hosts, key loggers to steal passwords, session hacking to intercept web session information, fake search engine results linking to phishing sites, phone calls asking users to input bank details, and malware attached to emails or downloads to steal data from victims' computers. It is important for users to be aware of these techniques to protect themselves from phishing attacks.
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What threatens us in cyberspace?
Phishing: typology of threats
Phishing protection
What is anti-phishing protection?
Website protection
Company and online fraud protection
Conclusion
Learn more about cyber attacks and find out how to secure yourself - https://hacken.live/2BwYyOo
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The document summarizes a presentation given by Mr. ASIM SOURAV RATH on cyber safety for students of Class XI and XII. The presentation covered topics including what cyber safety is, network security, vulnerabilities, safe web browsing, cookies, identity protection, privacy issues like tracking and digital footprints, and cyber laws in India. Tips for cyber safety such as using strong passwords and antivirus software were also provided.
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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.
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The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.
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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.
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Every day, internet thieves employ new ways to obtain personal identity people and get access
to their personal information. Phishing is a somehow complex method that has recently been
considered by internet thieves. First, the present study aims to explain phishing, and why an
organization should deal with it and its challenges of providing. In addition, different kinds of
this attack and classification of security approaches for organizational and lay users are
addressed in this article. Finally, the CERT strategy – which relies on three principles of
informing, supporting and helping- is presented to deal with phishing and studying some antiphishing.
HIGH ACCURACY PHISHING DETECTION
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.
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
User ignorance towards the use of communication services like Instant Messengers, emails, websites, social networks etc. is becoming the biggest advantage for phishers. It is required to create technical awareness in users by educating them to create a phishing detection application which would generate phishing alerts for the user so that phishing messages are not ignored. The lack of basic security features to detect and prevent phishing has had a profound effect on the IM clients, as they lose their faith in e-banking and e-commerce transactions, which will have a disastrous impact on the corporate and banking sectors and businesses which rely heavily on the internet. Very little research contributions were available in for phishing detection in Instant messengers. A context based, dynamic and intelligent phishing detection methodology in IMs is proposed, to analyze and detect phishing in Instant Messages with relevance to domain ontology (OBIE) and utilizes the Classification based on Association (CBA) for generating phishing rules and alerting the victims. A PDS Monitoring system algorithm is used to identify the phishing activity during exchange of messages in IMs, with high ratio of precision and recall. The results have shown improvement by the increased percentage of precision and recall when compared to the existing methods.
Malicious-URL Detection using Logistic Regression Technique
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
FINAL PROPOSAL PRESENTATION SLIDE.pptx
The document discusses the development of a browser extension to detect phishing URLs. It proposes using machine learning models trained on datasets of legitimate and phishing websites to classify URLs in real time. The extension will analyze features of URLs like those in the address bar, for abnormalities, and in HTML/JavaScript to identify phishing sites. The project uses an iterative development process over 10 weeks to create a Chrome extension that complies with guidelines and notifies users of detected phishing URLs. It concludes the system can handle new attack methods and recommends expanding it to be cross-platform and take online user reports to improve accuracy.
Phishing detection in ims using domain ontology and cba an innovative rule ...
User ignorance towards the use of communication services like Instant Messengers, emails, websites, social networks etc. is becoming the biggest advantage for phishers. It is required to create technical awareness in users by educating them to create a phishing detection application which would generate phishing alerts for the user so that phishing messages are not ignored. The lack of basic security features to detect and prevent phishing has had a profound effect on the IM clients, as they lose their faith in e-banking and e-commerce transactions, which will have a disastrous impact on the corporate and banking sectors and businesses which rely heavily on the internet.Very little research contributions were available in for phishing detection in Instant messengers. A context
based, dynamic and intelligent phishing detection
methodology in IMs is proposed, to analyze and detect phishing in Instant Messages with relevance to domain ontology (OBIE) and utilizes the Classification based on Association (CBA) for generating phishing rules and alerting the victims. A PDS Monitoring system algorithm is used to identify the phishing activity during exchange of messages in IMs, with high ratio of precision and recall. The results have shown improvement by the increased percentage of precision and recall when compared to the existing methods.
Prevention of Phishing Attacks Based on Discriminative Key Point Features of ...
Phishing is the combination of social engineering and technical exploits designed to convince a victim to provide personal information, usually for the monetary gain of the attacker (Phisher). Attempts to stop phishing by preventing a user from interacting with a malicious web site have shown to be ineffective. In this paper, present an effective image-based anti-phishing scheme based on discriminative key point features in WebPages. We use an invariant content descriptor, the Contrast Context Histogram (CCH), to compute the similarity degree between suspicious pages and authentic pages. To determine whether two images are similar, a common approach involves extracting a vector of salient features from each image, and computing the distance between the vectors, which is taken as the degree of visual difference between the two images. The results show that the proposed scheme achieves high accuracy and low error rates.
MULTI-LEVEL PARSING BASED APPROACH AGAINST PHISHING ATTACKS WITH THE HELP OF ...
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.
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.
IRJET - Chrome Extension for Detecting Phishing Websites
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
KNOWLEDGE BASE COMPOUND APPROACH AGAINST PHISHING ATTACKS USING SOME PARSING ...
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
Knowledge base compound approach against phishing attacks using some parsing ...
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
A literature survey on anti phishing
Phishing is the fraudulent acquisition of personal information like username, password, credit card information, etc. by tricking an individual into believing that the attacker is a trustworthy entity. It is affecting all the major sector of industry day by day with lots of misuse of user’s credentials. So in today
online environment we need to protect the data from phishing and safeguard our information, which can be done through anti-phishing tools. Currently there are many freely available anti-phishing browser extensions tools that warns user when they are browsing a suspected phishing site. In this paper we did a literature survey of some of the commonly and popularly used anti-phishing browser extensions by reviewing the existing anti-phishing techniques along with their merits and demerits.
Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...
This document summarizes a research paper on assessing whether spreading awareness about phishing attacks is effective in reducing attacks. Key points:
1. Phishing attacks are increasing and allow criminals to deceive users and steal important data. Spreading phishing awareness through training may help reduce attacks by empowering users to identify phishing emails and avoid risks.
2. Phishing awareness training can help organizations meet regulatory compliance requirements and make employees the first line of defense against cyberattacks.
3. Studies show that most data breaches are caused by phishing and losses from business email compromise attacks are increasing, demonstrating the need to minimize phishing attacks through awareness training.
4. A survey found that while most people
Similar to thesis 36 44 Final (20)
Review of the machine learning methods in the classification of phishing attack
Review of the machine learning methods in the classification of phishing attack
Multi level parsing based approach against phishing attacks with the help of ...
Multi level parsing based approach against phishing attacks with the help of ...
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
Malicious-URL Detection using Logistic Regression Technique
Malicious-URL Detection using Logistic Regression Technique
Phishing detection in ims using domain ontology and cba an innovative rule ...
Phishing detection in ims using domain ontology and cba an innovative rule ...
Prevention of Phishing Attacks Based on Discriminative Key Point Features of ...
Prevention of Phishing Attacks Based on Discriminative Key Point Features of ...
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MULTI-LEVEL PARSING BASED APPROACH AGAINST PHISHING ATTACKS WITH THE HELP OF ...
IRJET - Chrome Extension for Detecting Phishing Websites
IRJET - Chrome Extension for Detecting Phishing Websites
KNOWLEDGE BASE COMPOUND APPROACH AGAINST PHISHING ATTACKS USING SOME PARSING ...
KNOWLEDGE BASE COMPOUND APPROACH AGAINST PHISHING ATTACKS USING SOME PARSING ...
Knowledge base compound approach against phishing attacks using some parsing ...
Knowledge base compound approach against phishing attacks using some parsing ...
Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...
Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...
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thesis 36 44 Final
- 1. Presented by, Md. Merazul Islam (0507036) & Shuvradeb Barman Srijon (0507044) Supervised by, Mr. Muhammad Sheikh Sadi Assistant Professor Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna 9203, Bangladesh.
- 12. Approach Accuracy Time (second) IP based URL 100% 17 Exists in phishing database 97% 59 Matching source content 81% 134 Abnormal condition 79% 51