EAGLE AI
NETWORK TRAFFIC ANALYZER
FYP Team
Member 1
Name: Asif Mujeeb
Roll No. 19I-1991
Member 2
Name: Malik Touseef Husnain
Roll No. 19I-2028
Supervisor: Sir Jawad Hassan
Co-Supervisor: Sir Shoaib Saleem Khattak
S
INTRODUCTION
•
Cyber attacks, including Distributed Denial of
Service (DDoS) and crypto jacking, are
escalating daily
•
We introduce an Network Traffic Analyzer with
AutoML to detect attacks in short time
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MOTIVATION & CASE STUDY
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BACKGROUND
In 2017, Equifax, one of the largest credit agencies in the U.S.,
experienced a massive data breach. Hackers exploited a vulnerability in
the Apache Struts framework, compromising the personal information of
approximately 147 million people.
The breach was exacerbated by delayed detection and response,
allowing unauthorized access to sensitive data for over two months.
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COMPARATIVE ANALYSIS
Without Our Solution
With Our Solution
The delayed detection led to
significant financial losses, legal
repercussions, and damage to
Equifax's reputation.
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The hypothetical early detection
and alerting system would have
enabled a swift containment
response, minimizing the
breach's impact and preserving
customer trust.
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PROBLEM STATEMENT
Traditional threat detection systems struggle with the
volume, variety, and evolving nature of cyber
threats.
This leads to high false positives and missed
detections, compromising security.
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SOLUTION
Our solution provides a faster Network Analyzer
based on AI algorithms. Which is a more
accurate, scalable and evolving solution for the
Network. Making it a classification type solution.
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SCOPE
Network Based Analysis
Machine Learning Algorithms
Lambda Architecture
Automatic Model Selection
Data Processing Frameworks
User-Friendly Frontend
.
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OBJECTIVES
Scalable
Accurate
Automation
Self-Learning
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HIGH-END FEATURES
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Real-Time Threat Detection
AutoML for Dynamic Model Selection
Scalable Lambda Architecture
Advanced Anomaly Detection
User-Friendly Interface
Self-Optimizing System
Comprehensive Data Analysis
Customizable Alert System
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MODEL DIAGRAM
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TECHNOLOGIES USED
TensorFlow
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Google’s AutoML
MongoDB
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Apache Spark
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TECHNOLOGIES USED
Python
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HYPOTHETICAL IMPLEMENTATION
Application
Outcome
Had the AutoML Network Traffic Analyzer
been deployed within Equifax's network
infrastructure, its advanced machine
learning algorithms and real-time
processing capabilities could have identified
unusual data access patterns early on.
The system's ability to rapidly detect and
alert on such anomalies could have
significantly reduced the breach's window,
potentially preventing the extensive
exfiltration of personal data.
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TIMELINE
EagleAi
2024
February
March
May
September
Capturing Packets
Training ML
Implementing AutoML
Data Processing
Implementing ML
GUI Implementation
December
Reporting, Testing &
Validation
Feature Extracting for
ML
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WORK DISTRIBUTION
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Asif Mujeeb
Malik Touseef Husnain
Capturing Packets
Data Processing
Implementing AutoML
GUI Implementation
Feature Extraction for ML
Training ML
Implementing ML
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CONCLUSION
Our AutoML Network Traffic Analyzer
represents a significant leap forward in
cybersecurity, offering a smarter, more
adaptable solution to the ever-growing
challenge of cyber threats. We
welcome any questions.
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THANK YOU
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