SlideShare a Scribd company logo
1 of 14
TOWARDS DETECTING PHISHING WEB-PAGES 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.
Introduction Cyber Crime- the major concern. Internet fraud affects the rapidly growing online services. E-commerce is the main target. Social communication sites and mail service are also attack of them. Technical steps needed to defend them.
Phishing? A criminal trick of stealing sensitive personal information. Fooled user and push them to fall in the trick. Use social engineering and technical strategy. Mainly, duplicate original web-pages. First describe in 1987.
Problem Statement Phishing attacks succeed if users fail to detect phishing sites. Previous anti-phishing falls into four categories: Study on phishing Training people User interface Detection tools Precious works deals with limited service. Our approach- Development of an automated phishing detection method.
Attributes of Phishing Similar appearance of web-page. IP based URL & Non Matching URL. URL contain abnormal characters. Mis-spelled URL. Using script or add-in to web browser to cover the address bar.
Phishing Stats According to APWG According to PhishTank Total phishing attack. (Up to 6th April 2010)
Anti-phishing Social response Educating people. Changing habit. Technical support Identify phishing site. Implementation of secure model. Browser alert. Eliminating phishing mails. Monitoring and Takedown.
Methodology
Methodology
Methodology
results
Experimental analysis
Discussion Our approach reduces the ability of attackers to automate their attacks, cutting into their profitability.  By using the minimal knowledge base provided by the user-selected web-page, our system is able to compare potential phishing sites with real sites. Performance and accuracy can be improved by using an image segmentation. Flash contents can’t be validated whether phishing threat or not in our system.
Thank You ?

More Related Content

What's hot

PHISHING DETECTION
PHISHING DETECTIONPHISHING DETECTION
PHISHING DETECTION
umme ayesha
 
Internet safety v 4 slides and notes
Internet safety v 4  slides and notesInternet safety v 4  slides and notes
Internet safety v 4 slides and notes
Linda Barron
 

What's hot (18)

How To Catch a Phish: User Awareness and Training
How To Catch a Phish: User Awareness and TrainingHow To Catch a Phish: User Awareness and Training
How To Catch a Phish: User Awareness and Training
 
How to Spot and Combat a Phishing Attack - Cyber Security Webinar | ControlScan
How to Spot and Combat a Phishing Attack - Cyber Security Webinar | ControlScanHow to Spot and Combat a Phishing Attack - Cyber Security Webinar | ControlScan
How to Spot and Combat a Phishing Attack - Cyber Security Webinar | ControlScan
 
Phishing
PhishingPhishing
Phishing
 
Phishing Scams: 8 Helpful Tips to Keep You Safe
Phishing Scams: 8 Helpful Tips to Keep You SafePhishing Scams: 8 Helpful Tips to Keep You Safe
Phishing Scams: 8 Helpful Tips to Keep You Safe
 
Cyber security
Cyber securityCyber security
Cyber security
 
P H I S H I N G
P H I S H I N GP H I S H I N G
P H I S H I N G
 
PHISHING DETECTION
PHISHING DETECTIONPHISHING DETECTION
PHISHING DETECTION
 
Phishing Seminar By M Nadeem Qazi(MnQazi) pptx
Phishing Seminar By M Nadeem Qazi(MnQazi) pptxPhishing Seminar By M Nadeem Qazi(MnQazi) pptx
Phishing Seminar By M Nadeem Qazi(MnQazi) pptx
 
10 Steps to Creating a Corporate Phishing Awareness Program
10 Steps to Creating a Corporate Phishing Awareness Program10 Steps to Creating a Corporate Phishing Awareness Program
10 Steps to Creating a Corporate Phishing Awareness Program
 
Phishing
PhishingPhishing
Phishing
 
Phishing techniques
Phishing techniquesPhishing techniques
Phishing techniques
 
PHISHING PROTECTION
 PHISHING PROTECTION PHISHING PROTECTION
PHISHING PROTECTION
 
Cyber Safety
Cyber Safety Cyber Safety
Cyber Safety
 
Internet safety v 4 slides and notes
Internet safety v 4  slides and notesInternet safety v 4  slides and notes
Internet safety v 4 slides and notes
 
Strategies to handle Phishing attacks
Strategies to handle Phishing attacksStrategies to handle Phishing attacks
Strategies to handle Phishing attacks
 
Β. Hucking
Β. Hucking Β. Hucking
Β. Hucking
 
Cyber security.docx
Cyber security.docxCyber security.docx
Cyber security.docx
 
Prevent phishing scams
Prevent phishing scamsPrevent phishing scams
Prevent phishing scams
 

Viewers also liked (8)

0507036 (2)
0507036 (2)0507036 (2)
0507036 (2)
 
0507036
05070360507036
0507036
 
36+44
36+4436+44
36+44
 
36.44.final
36.44.final36.44.final
36.44.final
 
thesis 36 44 Final
thesis 36 44 Finalthesis 36 44 Final
thesis 36 44 Final
 
Cv.meraz rizel.1.0
Cv.meraz rizel.1.0Cv.meraz rizel.1.0
Cv.meraz rizel.1.0
 
Cv
CvCv
Cv
 
Jakir khan CV
Jakir khan CVJakir khan CV
Jakir khan CV
 

Similar to 36 44 Final

Artificial intelligence presentation slides.pptx
Artificial intelligence presentation slides.pptxArtificial intelligence presentation slides.pptx
Artificial intelligence presentation slides.pptx
rakhicse
 
Phishing detection in ims using domain ontology and cba an innovative rule ...
Phishing detection in ims using domain ontology and cba   an innovative rule ...Phishing detection in ims using domain ontology and cba   an innovative rule ...
Phishing detection in ims using domain ontology and cba an innovative rule ...
ijistjournal
 
Multi level parsing based approach against phishing attacks with the help of ...
Multi level parsing based approach against phishing attacks with the help of ...Multi level parsing based approach against phishing attacks with the help of ...
Multi level parsing based approach against phishing attacks with the help of ...
IJNSA Journal
 
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
ijistjournal
 
phishingppt-160209144204.pdf
phishingppt-160209144204.pdfphishingppt-160209144204.pdf
phishingppt-160209144204.pdf
vinayakjadhav94
 

Similar to 36 44 Final (20)

Major Prc.pptx
Major Prc.pptxMajor Prc.pptx
Major Prc.pptx
 
CERT STRATEGY TO DEAL WITH PHISHING ATTACKS
CERT STRATEGY TO DEAL WITH PHISHING ATTACKSCERT STRATEGY TO DEAL WITH PHISHING ATTACKS
CERT STRATEGY TO DEAL WITH PHISHING ATTACKS
 
Phishing ppt
Phishing pptPhishing ppt
Phishing ppt
 
Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...
Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...
Research Paper on Spreading Awareness About Phishing Attack Is Effective In R...
 
Review of the machine learning methods in the classification of phishing attack
Review of the machine learning methods in the classification of phishing attackReview of the machine learning methods in the classification of phishing attack
Review of the machine learning methods in the classification of phishing attack
 
Two unethical isuue related engineering
Two unethical isuue related engineeringTwo unethical isuue related engineering
Two unethical isuue related engineering
 
Artificial intelligence presentation slides.pptx
Artificial intelligence presentation slides.pptxArtificial intelligence presentation slides.pptx
Artificial intelligence presentation slides.pptx
 
Best Cyber Security Courses In Bangladesh.docx
Best Cyber Security Courses In Bangladesh.docxBest Cyber Security Courses In Bangladesh.docx
Best Cyber Security Courses In Bangladesh.docx
 
A LITERATURE REVIEW ON PHISHING EMAIL DETECTION USING DATA MINING
A LITERATURE REVIEW ON PHISHING EMAIL DETECTION USING DATA MININGA LITERATURE REVIEW ON PHISHING EMAIL DETECTION USING DATA MINING
A LITERATURE REVIEW ON PHISHING EMAIL DETECTION USING DATA MINING
 
Prevention of Phishing Attacks Based on Discriminative Key Point Features of ...
Prevention of Phishing Attacks Based on Discriminative Key Point Features of ...Prevention of Phishing Attacks Based on Discriminative Key Point Features of ...
Prevention of Phishing Attacks Based on Discriminative Key Point Features 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 ...Phishing detection in ims using domain ontology and cba   an innovative rule ...
Phishing detection in ims using domain ontology and cba an innovative rule ...
 
A FRAMEWORK FOR SECURING EMAIL ENTRANCES AND MITIGATING PHISHING IMPERSONATIO...
A FRAMEWORK FOR SECURING EMAIL ENTRANCES AND MITIGATING PHISHING IMPERSONATIO...A FRAMEWORK FOR SECURING EMAIL ENTRANCES AND MITIGATING PHISHING IMPERSONATIO...
A FRAMEWORK FOR SECURING EMAIL ENTRANCES AND MITIGATING PHISHING IMPERSONATIO...
 
8Cyber security courses in Bangladesh.docx
8Cyber security courses in Bangladesh.docx8Cyber security courses in Bangladesh.docx
8Cyber security courses in Bangladesh.docx
 
Multi level parsing based approach against phishing attacks with the help of ...
Multi level parsing based approach against phishing attacks with the help of ...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 ...PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
PHISHING DETECTION IN IMS USING DOMAIN ONTOLOGY AND CBA – AN INNOVATIVE RULE ...
 
6356152.pdf
6356152.pdf6356152.pdf
6356152.pdf
 
phishingppt-160209144204.pdf
phishingppt-160209144204.pdfphishingppt-160209144204.pdf
phishingppt-160209144204.pdf
 
Phishing ppt
Phishing pptPhishing ppt
Phishing ppt
 
IRJET- Detecting Malicious URLS using Machine Learning Techniques: A Comp...
IRJET-  	  Detecting Malicious URLS using Machine Learning Techniques: A Comp...IRJET-  	  Detecting Malicious URLS using Machine Learning Techniques: A Comp...
IRJET- Detecting Malicious URLS using Machine Learning Techniques: A Comp...
 
Report on Human factor in the financial industry
Report on Human factor in the financial industryReport on Human factor in the financial industry
Report on Human factor in the financial industry
 

Recently uploaded

Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
kauryashika82
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
negromaestrong
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
Chris Hunter
 

Recently uploaded (20)

Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
 
Asian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptxAsian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701
 
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
 
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural ResourcesEnergy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
 
General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual  Proper...General Principles of Intellectual Property: Concepts of Intellectual  Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIFood Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
 
Seal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptxSeal of Good Local Governance (SGLG) 2024Final.pptx
Seal of Good Local Governance (SGLG) 2024Final.pptx
 
Role Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptxRole Of Transgenic Animal In Target Validation-1.pptx
Role Of Transgenic Animal In Target Validation-1.pptx
 
Making and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdfMaking and Justifying Mathematical Decisions.pdf
Making and Justifying Mathematical Decisions.pdf
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning ExhibitSociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 

36 44 Final

  • 1. TOWARDS DETECTING PHISHING WEB-PAGES 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.
  • 2. Introduction Cyber Crime- the major concern. Internet fraud affects the rapidly growing online services. E-commerce is the main target. Social communication sites and mail service are also attack of them. Technical steps needed to defend them.
  • 3. Phishing? A criminal trick of stealing sensitive personal information. Fooled user and push them to fall in the trick. Use social engineering and technical strategy. Mainly, duplicate original web-pages. First describe in 1987.
  • 4. Problem Statement Phishing attacks succeed if users fail to detect phishing sites. Previous anti-phishing falls into four categories: Study on phishing Training people User interface Detection tools Precious works deals with limited service. Our approach- Development of an automated phishing detection method.
  • 5. Attributes of Phishing Similar appearance of web-page. IP based URL & Non Matching URL. URL contain abnormal characters. Mis-spelled URL. Using script or add-in to web browser to cover the address bar.
  • 6. Phishing Stats According to APWG According to PhishTank Total phishing attack. (Up to 6th April 2010)
  • 7. Anti-phishing Social response Educating people. Changing habit. Technical support Identify phishing site. Implementation of secure model. Browser alert. Eliminating phishing mails. Monitoring and Takedown.
  • 13. Discussion Our approach reduces the ability of attackers to automate their attacks, cutting into their profitability. By using the minimal knowledge base provided by the user-selected web-page, our system is able to compare potential phishing sites with real sites. Performance and accuracy can be improved by using an image segmentation. Flash contents can’t be validated whether phishing threat or not in our system.