TEAM.MAJOR[1] project based on the .pptx

major project

1. Department of Computer Science and Engineering ENSEMBLE MACHINE LEARNING MODEL FOR PHISHING INTRUSION DETECTION AND CLASSIFICATION FROM URLs Batch number : 10 Team Members : E. Kavya Kusuma G. Manoj V. Mahendra 20RA1A 0535 20RA1A 0544 20RA1A 0542 Guide by : P. Vyshali (Asst.Professor)

2. INDEX Abstract Introduction Problem Statement Literature Survey Existing System Drawbacks of Existing Systems Proposed Systems Advantage Applications Software & Hardware Requirements Packages Conclusion Future Scope References

3. ABSTRACT The research focuses on using machine learning classification models to predict whether a given URL is legitimate or a phishing URL. A legitimate URL directs users to an authentic webpage, while a phishing URL directs users to a fraudulent website impersonating another entity. The aim is to take an adaptive approach to detect phishing URLs on the client-side to protect users from cyber-attacks and stealing of personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe online and assist security researchers in identifying patterns and relations related to phishing attacks. The goal is to maintain high-security standards for everyday internet users by providing a solution to detect phishing URLs.

4. Overview of phishing attacks and their prevalence as a cybersecurity threat targeting individuals and organizations. Importance of developing effective detection mechanisms to identify phishing URLs and prevent users from falling victim to these attacks. Brief explanation of ensemble learning techniques, such as bagging, boosting, and stacking. The significance of this research lies in its potential to enhance cybersecurity measures by developing an effective solution to detect and prevent phishing attacks, thereby protecting users from financial losses and data breaches caused by these malicious activities. INTRODUCTION

5. Phishing attacks pose a severe threat to internet users and organizations, leading to financial losses, data breaches, and compromised sensitive information. Traditional methods of detecting phishing URLs, such as blacklists and heuristic-based approaches, have limitations in accurately identifying evolving phishing techniques and new attack vectors. The dynamic nature of phishing campaigns necessitates a robust and adaptive solution that can effectively classify URLs as legitimate or phishing with high accuracy. The proposed research aims to develop an ensemble machine learning model that combines multiple base classifiers to achieve superior performance in detecting phishing intrusions from URLs. PROBLEM ST ATEMENT

6. LITERATURE SURVEY Phishing attacks have been a persistent cybersecurity threat, and numerous studies have been conducted to develop effective detection techniques. Traditional approaches like blacklists and heuristic-based methods have limitations in accurately identifying evolving phishing strategies. Machine learning techniques have gained popularity for phishing detection due to their ability to learn patterns from data and adapt to new attack vectors . Several studies have explored the use of individual machine learning models for phishing URL classification. Abdelhamid et al proposed a system using Random Forest and achieved an accuracy of 96.8%. Rao and Pais utilized Support Vector Machines (SVMs) and reported an accuracy of 95.6%. However, these single models may suffer from bias, overfitting, or limitations in handling complex phishing patterns. To overcome these challenges, researchers have explored ensemble learning techniques that combine multiple base models. Bambra et al proposed a boosting-based approach using decision trees and achieved an accuracy of 98.2%. Le et al used stacking with Random Forest, Logistic Regression, and SVMs, achieving an accuracy of 98.7% in phishing URL detection.

7. EXISTING SYSTEM Ensemble of random forest, logistic regression, and decision tree models . Uses URL-based features like domain age, URL length, and IP-based features . Reported accuracy of 95.6% on a phishing dataset. Combines static and dynamic analysis features from URLs . Ensemble of logistic regression, support vector machines, and decision trees . Utilizes boosting and stacking techniques. Achieved an accuracy of 97.8% on a phishing dataset. Deep learning ensemble model using convolutional neural networks (CNNs) . Processes URLs as character-level sequences . Combines predictions from multiple CNN models using stacking . Achieved an accuracy of 98.1% on a phishing dataset These existing systems highlight the potential of ensemble machine learning techniques for phishing URL detection. However, they may have limitations in terms of feature selection, model diversity, or adaptability to new phishing strategie.

8. DRAWBACKS OF EXISTING SYSTEM Limited to Two Choices : Logistic regression is best for problems where we have only two choices for the outcome. For example, yes or no, pass or fail Lack of adaptability Not Very Clever : Compared to some other methods, logistic regression is not very good at capturing complex patterns in our data. Not Always Sure : If we're trying to predict multiple choices (not just two), logistic regression doesn't directly tell us how confident it is in each choice. It's like making guesses without knowing how sure we are about them. Can Get Too Excited : Sometimes, logistic regression can get too excited about the data and make predictions that are too specific to the training data.

9. PROPOSED SYSTEM XGBoost (Extreme Gradient Boosting) is an optimized gradient boosting algorithm that is widely used in machine learning for both regression and classification tasks. It builds upon the strengths of traditional gradient boosting methods while addressing some of their limitations. Here's how the XGBoost algorithm works Gradient Boosting Framework: XGBoost belongs to the family of ensemble learning methods known as boosting. Boosting algorithms combine weak learners (simple models) sequentially to create a strong learner. The primary idea is to correct the errors made by previous models. Tree Pruning: Sometimes, our treehouse gets too big with unnecessary branches. Pruning means trimming those branches that don't help much. It makes our treehouse simpler and easier to understand. XGBoost is like a group of friends building a treehouse (model) together. They learn from each other, fix mistakes, focus on important parts, and work efficiently to create the best possible treehouse.

10. A D V AN T A G E S High Performance : XGBoost is known for its efficiency and speed. It's optimized for performance and can handle large datasets with millions of instances and features Scalability and Efficiency : More scalable and efficient compared to other algorithms. Flexibility and Modularity : More flexible and Modular compared to the algorithms Handling Missing Values : XGBoost has built-in capabilities to handle missing values in the dataset.

11. APPLICATIONS Web Browser Extensions Email Filters Online Payment and Banking Systems Mobile Security Applications Cybersecurity Training and Awareness Programs

12. SOFTWARE & PACKAGES SOFTWARE:- Python IDLE 3.7 version Operating system Processor Ram Hard disk : : : : Windows, Linux minimum intel i3 minimum 4 GB minimum 250GB HARDWARE:-

13. NumPy Pandas Matplotlib Scikit – learn Seaborn PACKAGES

14. CONCLUSION In conclusion, phishing attacks pose a significant threat to individuals and organizations, as they attempt to fraudulently obtain sensitive information through deceptive means, such as sending emails with malicious links or impersonating legitimate entities. This research focuses on developing an adaptive and robust machine learning-powered tool to detect phishing URLs, which are used to direct users to fraudulent websites. Overall, this research represents a promising step towards combating the growing threat of phishing attacks by leveraging the power of machine learning and ensemble models. By providing an adaptive and accurate solution for detecting phishing URLs, the proposed approach has the potential to enhance online safety and protect users from the detrimental consequences of falling victim to these malicious activities.

15. FUTURE SCOPE Continuous Learning and Adaptation: As phishing attacks evolve and new techniques emerge, the machine learning models employed in the proposed tool will need to be continuously updated and retrained with the latest data. Implementing mechanisms for ongoing learning and adaptation will be crucial to ensure the tool's effectiveness against emerging phishing strategies. Integration with Existing Security Solutions: While the proposed approach aims to detect phishing URLs on the user's side (client-side), it could be beneficial to explore ways to combine or connect the machine learning-powered tool with other security solutions that already exist. For example, integrating it with web browsers, email applications, or security systems used by organizations. By doing this, the tool's ability to identify and protect against phishing attacks can be expanded and have a greater impact on a larger number of users.

16. REFERENCES Mahmoud, T.M. and Mahfouz, A.M., 2012. Ensemble clustered classifiers for phishing email detection. In 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA) (pp. 176-181). IEEE. Hadi, W., Aburub , F. and Alhawari, S., 2016. A new fast associative classification algorithm for detecting phishing websites. Applied Soft Computing, 48, pp.729-734. Sahingoz , O.K., Buber, E., Demir, O. and Diri , B., 2019. Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, pp.345-357. Feng, Y., Anand, V., Dillig , I. and Aiken, A., 2021. Densely exploiting term correlations for phishing URL detection with deep learning architectures. ACM Transactions on Privacy and Security (TOPS), 24(4), pp.1-34. Mao, J., Tian, J., Li, W., Li, J. and Liang, X., 2021. Phishing WebSite detection based on ensemble learning. IEEE Access, 9, pp.48231-48254. Selvamani , K., Jeevan, A.P. and Duraipandian, M., 2022. An ensemble machine learning model for phishing URL detection using URL-based features. Journal of King Saud University-Computer and Information Sciences, 34(1), pp.538-547.