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|| Jai Sri Gurudev ||
Sri Adichunchanagiri Shikshana Trust ®
SJB INSTITUTE OF TECHNOLOGY
BGS Health & Education City, Kengeri, Bengaluru-560060.
Department of Computer Science and Engineering
Project Phase-I Synopsis Review on
“Phishing Detection System through hybrid machine learning based on URL”
By
Meghana M B [1JB20CS061]
Nikitha D [1JB20CS071]
Nayana A R [1JB21CS417]
Shanmukha M [1JB21CS422]
Under the Guidance of
Mrs.Manjula H S
Assistant Professor, Dept. of CSE, SJBIT
Abstarct
Introduction
Literature Survey
Challenges
Problem Statement
Objectives
References
Outline
• Phishing is a common method that cybercriminals use to do the fraud activity by creating original looking emails or websites to trick victims into
sharing personal information or financial data.
• Detecting phishing attacks involves various techniques, including email filtering, URL analysis, and user education.
• Most of the non machine learning based existing system is based on black listing and white listing, URLs are manually updated by administrator,
so it is not accurate and time consuming process. Most of the machine learning based existing system is implemented only using URL features.
• Therefore in this project, we're using a Web page phishing detection dataset with information from over 11,000 websites to build a system that can
detect and protect users from dangerous phishing websites.
• This project uses machine learning models such as random forest (RF), naive Bayes (NB), and proposed hybrid LSD model, which is a combination
of logistic regression, support vector machine, and decision tree (LR+SVC+DT).
Abstract
• Phishing attacks often involve fraudulent URLs, which are web addresses designed to mimic the appearance of trusted
websites.
• These URLs can be challenging to distinguish from legitimate ones, making automated detection techniques essential.
• Existing phishing detection methods often rely on limited techniques, leading to insufficient protection.
• Phishing detection systems often employ a Hybrid Machine Learning approach to enhance accuracy.
• By developing an advanced phishing detection system using hybrid machine learning techniques based on URL features
which involves Hybrid Machine Learning concepts to classify phishing URLs.
Introduction
Sl. No. Paper Title Techniques Dataset Metrics Year Limitations
1 PhishSim: Aiding Phishing Website
Detection With a Feature-Free Tool
Furthest point First
algorithm, normalized
compression distance
(NCD)
phishing and
legitimate website
dataset
Accuracy, AUC score, G-
mean, TNR, TPR.
2022 Accuracy is less than
92%.
2 A Deep Learning-Based Framework
for Phishing Website Detection
RNN-LSTM UCI Phishing Dataset Accuracy, Precision, Recall,
F1, FalsePositive rate,
FalseNegative rate.
2021 It consumes more training time.
3 Multilayer Stacked Ensemble
Learning
Model to Detect Phishing
Websites
Multilayered stacked
ensemble learning
technique
UCI phishing Dataset. Accuracy, PrecisIon, Recall,
F1.
2022 Consumes more time in
producing results.
Less Accuracy.
Literature Survey
Contd..
4 PDGAN:Phishing Detection
With Generative Adversarial
Networks
PDGAN KDD99, Accuracy, Precision, Recall,
F1, FalsePositive rate,
FalseNegative rate.
2022 It consumes more training
time.
5 Uncovering the Cloak: A
Systematic Review of
Techniques Used to Conceal
Phishing Websites
Cloaking mechanism
and phishing tool kit.
KDD
Dataset
Accuracy. 2023 It is suitable for static
network configuration
• The problem with phishing is that attackers constantly look for new and creative ways to fool users into believing their
actions involve a original website or email.
• One of the main challenges is ensuring that the signature based algorithms are able to detect new and evolving types of
phishing attacks.
• This requires ongoing updates to the training data and features used by the algorithms.
Challenges
The aim of our work is to design a framework to detect phishing
attacks.
Problem Statement
Objectives
• Effectively analyze and process network traffic using machine learning
techniques.
• To design a framework to detect phishing attacks.
• To compare proposed model with state of art of machine learning.
Methodology
1. N. Z. Harun, N. Jaffar, and P. S. J. Kassim, ‘‘Physical attributes significant in preserving the social sustainability of the
traditional malay settlement,’’ in Reframing the Vernacular: Politics, Semiotics, and Representation. Springer, 2020, pp.
225–238.
2. D. M. Divakaran and A. Oest, ‘‘Phishing detection leveraging machine learning and deep learning: A review,’’ 2022,
arXiv:2205.07411.
3. A. Akanchha, ‘‘Exploring a robust machine learning classifier for detecting phishing domains using SSL certificates,’’
Fac. Comput. Sci., Dalhousie Univ., Halifax, NS, Canada, Tech. Rep. 10222/78875, 2020.
4. H. Shahriar and S. Nimmagadda, ‘‘Network intrusion detection for TCP/IP packets with machine learning techniques,’’
in Machine Intelligence and Big Data Analytics for Cybersecurity Applications. Cham, Switzerland: Springer, 2020, pp.
231–247.
References
5. P. George and P. Vinod, ‘‘Composite email features for spam identification,’’ in Cyber Security. Singapore: Springer,
2018, pp. 281–289.
6. G. Sonowal and K. S. Kuppusamy, ‘‘PhiDMA—A phishing detection model with multi-filter approach,’’ J. King Saud
Univ., Comput. Inf. Sci., vol. 32, no. 1, pp. 99–112, Jan. 2020.
7. R. Prasad and V. Rohokale, ‘‘Cyber threats and attack overview,’’ in Cyber Security: The Lifeline of Information and
Communication Technology. Cham, Switzerland: Springer, 2020, pp. 15–31.
8. R. Ø. Skotnes, ‘‘Management commitment and awareness creation—ICT safety and security in electric power supply
network companies,’’ Inf. Comput. Secur., vol. 23, no.
Contd..
THANK YOU

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Final presentation of diabetic_retinopathy_vascular

  • 1. || Jai Sri Gurudev || Sri Adichunchanagiri Shikshana Trust ® SJB INSTITUTE OF TECHNOLOGY BGS Health & Education City, Kengeri, Bengaluru-560060. Department of Computer Science and Engineering Project Phase-I Synopsis Review on “Phishing Detection System through hybrid machine learning based on URL” By Meghana M B [1JB20CS061] Nikitha D [1JB20CS071] Nayana A R [1JB21CS417] Shanmukha M [1JB21CS422] Under the Guidance of Mrs.Manjula H S Assistant Professor, Dept. of CSE, SJBIT
  • 3. • Phishing is a common method that cybercriminals use to do the fraud activity by creating original looking emails or websites to trick victims into sharing personal information or financial data. • Detecting phishing attacks involves various techniques, including email filtering, URL analysis, and user education. • Most of the non machine learning based existing system is based on black listing and white listing, URLs are manually updated by administrator, so it is not accurate and time consuming process. Most of the machine learning based existing system is implemented only using URL features. • Therefore in this project, we're using a Web page phishing detection dataset with information from over 11,000 websites to build a system that can detect and protect users from dangerous phishing websites. • This project uses machine learning models such as random forest (RF), naive Bayes (NB), and proposed hybrid LSD model, which is a combination of logistic regression, support vector machine, and decision tree (LR+SVC+DT). Abstract
  • 4. • Phishing attacks often involve fraudulent URLs, which are web addresses designed to mimic the appearance of trusted websites. • These URLs can be challenging to distinguish from legitimate ones, making automated detection techniques essential. • Existing phishing detection methods often rely on limited techniques, leading to insufficient protection. • Phishing detection systems often employ a Hybrid Machine Learning approach to enhance accuracy. • By developing an advanced phishing detection system using hybrid machine learning techniques based on URL features which involves Hybrid Machine Learning concepts to classify phishing URLs. Introduction
  • 5. Sl. No. Paper Title Techniques Dataset Metrics Year Limitations 1 PhishSim: Aiding Phishing Website Detection With a Feature-Free Tool Furthest point First algorithm, normalized compression distance (NCD) phishing and legitimate website dataset Accuracy, AUC score, G- mean, TNR, TPR. 2022 Accuracy is less than 92%. 2 A Deep Learning-Based Framework for Phishing Website Detection RNN-LSTM UCI Phishing Dataset Accuracy, Precision, Recall, F1, FalsePositive rate, FalseNegative rate. 2021 It consumes more training time. 3 Multilayer Stacked Ensemble Learning Model to Detect Phishing Websites Multilayered stacked ensemble learning technique UCI phishing Dataset. Accuracy, PrecisIon, Recall, F1. 2022 Consumes more time in producing results. Less Accuracy. Literature Survey
  • 6. Contd.. 4 PDGAN:Phishing Detection With Generative Adversarial Networks PDGAN KDD99, Accuracy, Precision, Recall, F1, FalsePositive rate, FalseNegative rate. 2022 It consumes more training time. 5 Uncovering the Cloak: A Systematic Review of Techniques Used to Conceal Phishing Websites Cloaking mechanism and phishing tool kit. KDD Dataset Accuracy. 2023 It is suitable for static network configuration
  • 7. • The problem with phishing is that attackers constantly look for new and creative ways to fool users into believing their actions involve a original website or email. • One of the main challenges is ensuring that the signature based algorithms are able to detect new and evolving types of phishing attacks. • This requires ongoing updates to the training data and features used by the algorithms. Challenges
  • 8. The aim of our work is to design a framework to detect phishing attacks. Problem Statement
  • 9. Objectives • Effectively analyze and process network traffic using machine learning techniques. • To design a framework to detect phishing attacks. • To compare proposed model with state of art of machine learning.
  • 11. 1. N. Z. Harun, N. Jaffar, and P. S. J. Kassim, ‘‘Physical attributes significant in preserving the social sustainability of the traditional malay settlement,’’ in Reframing the Vernacular: Politics, Semiotics, and Representation. Springer, 2020, pp. 225–238. 2. D. M. Divakaran and A. Oest, ‘‘Phishing detection leveraging machine learning and deep learning: A review,’’ 2022, arXiv:2205.07411. 3. A. Akanchha, ‘‘Exploring a robust machine learning classifier for detecting phishing domains using SSL certificates,’’ Fac. Comput. Sci., Dalhousie Univ., Halifax, NS, Canada, Tech. Rep. 10222/78875, 2020. 4. H. Shahriar and S. Nimmagadda, ‘‘Network intrusion detection for TCP/IP packets with machine learning techniques,’’ in Machine Intelligence and Big Data Analytics for Cybersecurity Applications. Cham, Switzerland: Springer, 2020, pp. 231–247. References
  • 12. 5. P. George and P. Vinod, ‘‘Composite email features for spam identification,’’ in Cyber Security. Singapore: Springer, 2018, pp. 281–289. 6. G. Sonowal and K. S. Kuppusamy, ‘‘PhiDMA—A phishing detection model with multi-filter approach,’’ J. King Saud Univ., Comput. Inf. Sci., vol. 32, no. 1, pp. 99–112, Jan. 2020. 7. R. Prasad and V. Rohokale, ‘‘Cyber threats and attack overview,’’ in Cyber Security: The Lifeline of Information and Communication Technology. Cham, Switzerland: Springer, 2020, pp. 15–31. 8. R. Ø. Skotnes, ‘‘Management commitment and awareness creation—ICT safety and security in electric power supply network companies,’’ Inf. Comput. Secur., vol. 23, no. Contd..