This document proposes a machine learning approach to detect phishing websites using internal organizational data. It discusses extracting features from URLs and using those features to train a classification model to identify legitimate versus phishing websites. The model would be deployed as a REST API and used to analyze unknown web traffic in real-time. Some benefits include reduced reliance on third parties, tailored insights using internal data, and proactive rather than reactive protection against phishing. Challenges include collecting sufficient baseline data and balancing accuracy with user experience.

1. THE FIFTH ELEPHANT
- ARJUN B.M.
MUDPIPE
MaliciousURLDetectionfor
PhishingIdentificationandPrevention

2. PHISHING INTRODUCTION
The fraudulent practice of sending emails purporting to be from reputable
companies in order to induce individuals to reveal personal information, such
as passwords and credit card numbers
MOTIVES: Financial gain, damage reputation, identity theft, fame & notoriety
Phishing websites indicators:
• Visually appears like the original website
• Email creates a sense of urgency to force user action
• Fake HTTPS certificate & domain name
• Provides attractive offers which tempts the user to respond

3. PROBLEM STATEMENT
For Employees:
• Accessing malicious sites by being victims of phishing emails
• No mechanism to check bad sites by employees through self-service
• Lack of awareness and training for employees
For Security Teams
• Manual time & effort spent to block sites by Security Operations team
• Lack of internal ML solution insights on phishing data; current solutions maybe rule-based
• Different teams/networks may have different requirements for site access, which cannot be
served by external commercial solutions
For Business:
• 91% of all cyber-attacks are via phishing and they have devastating consequences
• Licensing cost for commercial based solutions to detect phishing sites
• Dependency on external solution/product

4. APPROACH &METHODOLOGY
MACHINE LEARNING APPROACH
• Data Collection & Validation
• Parameter Determination (Address Bar based
Features, HTML and JavaScript based Features, Domain
based Features, Abnormal Based Features, URL Blacklist
Features)
• Feature Extraction from unknown, incoming
data (test data)
• Create baseline model with initial dataset
• Evaluate performance of model and fine-tuning
• Apply test data on pre-trained baseline model &
make prediction
• Compare with known data sources & further
fine-tune results
• Retrain model on frequent intervals for better
accuracy, context and relevancy
• Classification model pickled and exposed as
REST API
WHITELIST / BLACKLIST APPROACH
• Identify data sources which provide
info on phishing sites
• Scrape data from data sources
• Create whitelist / blacklist and
compare URLs
CONS
• Lack of updated data sources
• Lack of real-time intelligence
• Data not comprehensive enough
• Extensive effort for data scraping
RULE BASED APPROACH
• Determine phishing indicators
• Define rules using combination of
indicators
• Compare & match URLs against
rules to deny/allow
CONS
• Complex rule set definitions
• Overhead in managing and
updating rules
• High False Positive and False
Negative rates

5. ARCHITECTURE /WORKFLOW
BASELINE
DATA SET
BASELINE
ML MODEL
PREDICTION
TRAIN
EXPOSE AS
REST API
FEATURE
EXTRACTION
TEST DATA
OUTPUT
RETRAIN
WEB TRAFFIC
(UNKNOWN DATA)
INPUT
• Address Bar based Features
• HTML and JavaScript based
Features
• Domain based Features
• Abnormal Based Features
• URL Blacklist Features
• Total of 30 features
• CLASSIFICATION = 0: LEGITIMATE
• CLASSIFICATION = 1: PHISHING
• COMPARE WITH KNOWN SOURCES
• PROBABILITY OF PREDICTION
SECURITY
ACTION
(BLOCK / ALLOW)

6. FEATURE EXTRACTION
1. having_IP_Address
2. URL_Length
3. Shortening_Service
4. having_At_Symbol
5. double_slash_redirecting
6. Prefix_Suffix
7. having_Sub_Domain
8. SSL_State
25. DNS_Record
26. web_traffic_rank
27. Page_Rank
28. Google_Index
29. Links_pointing_to_page
30. Statistical_report - top
phishing domains
Classification output: 0 = legitimate, 1 = phishing
9. Domain_registeration_length
10. Favicon
11. Open_ports
12. HTTPS_token_in_URL
13. Request_URL
14. URL_of_Anchor
15. Links_in_tags
16. Server_Form_Handler
17. Submitting_to_email
18. Abnormal_URL
19. Site_Redirect
20. on_mouseover_changes
21. RightClick_Disabled
22. popUpWindow
23. Iframe_redirection
24. age_of_domain

7. DEPLOYING TOPRODUCTION
• Context specific use-cases:
• Certain sub-nets within the org might require access to certain websites to support business functionality
• Org might want to block access to sites even though they are classified as “suspicious” by commercial softwares
• Infrastructural & capacity planning considerations: client, load balancer, web server, queues, etc
• REST-API approach: train, retrain & predictions
• Develop automation test cases for your model (especially on feature engineering side)
• Automate evaluation of the production model, which allows to efficiently back-test changes to the model on historical data and determine if
improvements have been made or not
• Possibly have different ML models / end-points exposed for different sections of the network or for different departments
• Have a fall-back or set-default-value for parameters which fail to get processed by the Feature Engineering module (exception handling)
• Decouple the input and the output for the model; model should still work if parameters are added, modified or deleted in feature engineering
• Single egress point for web traffic, where the ML model can be plugged-in with the REST API
• Have a fail-open or kill-switch mechanism for traffic to flow through if model processing fails
• Place model operation in “monitoring” or “non-blocking” mode initially, which allows the ML model to get additional data and allows for fine-
tuning and prevents errors
• Supplement with existing controls like spam filtering, black-listing, etc
• Model should refer to other data sources as well for fine-tuning in the initial stages
• Baselining and retraining the model at frequent internals; also maintaining model versions
• Provide security analysts with an option to tweak/edit input data for contextual representation
• Deploying the MODEL client-side versus server-side

8. PROS&CONS
PROS
• Reduce dependency, cost & license on third-party external software
• Re-use of in-house org’s data rather than contribute towards improving
commercial software
• Better insights into online behavior of employees
• Real-time protection for employees who access malicious websites or
click on phishing links
• Detect and prevent against unknown phishing attacks, as new patterns
are created by attackers
• Next level of intelligence on top of signature-based prevention
techniques & blacklists
• Email filtering solutions help in filtering phishing/spam emails, but this
provides holistic protection for all outgoing internet traffic
• Centralized solution implemented org-wide and no dependency on client-
side agents/software
• Anti-phishing: move from offline to real-time; move from reactive to
proactive
CONS
• Data collection & building data repository
• Initial baseline dataset has too few records
• Cost / Maintenance of solution/product
• Fine-tuning of rules & predictions to meet
changing threat vectors
• False positive rate could cause bad user
experience
• Needs to be supplemented with Cyber Threat
Intel
• Solution works only when users are connected to
org network, since there is no client-side agent

9. AUDIENCE TAKE-AWAYS
• Opportunity for engineers and analysts to collaborate and work
together to build tailored intelligent security solutions / products
• Learn the various considerations in designing and deploying a
ML solution in the InfoSec domain

10. EMAIL : arjun.job14@gmail.com
LINKEDIN: https://www.linkedin.com/in/arjunbm
FURTHERREADING
LINKS & REFERENCES
• https://www.researchgate.net/publication/226420039_Detection_of_Phishing_Attacks_
A_Machine_Learning_Approach
• https://ieeexplore.ieee.org/document/8004877
• https://pdfs.semanticscholar.org/188f/3bde688d5a47ce86bc0a8eca03aeb1bb9dfc.pdf