FYP PROJECT

1. Intrusion Detection System Enhancing Cyber
Security Through AI and ML-Based
DEPARTMENT OF
TELECOMMUNICATION, MUET BATCH
20TL

2. FYP Presentation
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL
Naimtaullah (member)
20TL018
Zahid Ab Kunbhar (GL)
20TL045
Mirza Sufiyan(member)
20-19TL79
Dr Aftab Ahmed Memon (Supervisor) Sir Talha Qaimkhani (Co-supervisor)

3. 1. Problem Statement
2. Background and Motivation
3. Literature Review
4. Aim and Objectives
5. Methodology
6. Timeline
7. Social Impacts
8. References
CONTENTS
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL

4. PROBLEM STATEMENT
Traditional cyber-security struggles against evolving cyber
threats.
This project deploys AI and ML for a dynamic, preemptive
approach, setting new benchmarks for rapid threat response.
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL

5. BACKGROUND & MOTIVATION
In the current tech era, global cybercrime escalation calls for proactive
measures. Committed to addressing this surge
 our focus is on leveraging cutting-edge technologies
 Artificial Intelligence (AI) and Machine Learning (ML).
 By harnessing AI and ML algorithms, we aim to fortify defenses against
cyber threats,
developing advanced systems to counteract attacks and set a benchmark
for enhanced cyber-security in this dynamic landscape.
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL

6. LITERATURE REVIIEW
REF TITLE SUMMARY REMARKS
01
Intrusion Detection Systems using
Supervised Machine Learning Techniques:
A survey
In this paper the Detecting unique attacks, especially anomalies,
poses a significant challenge.
Our study highlights the
pivotal role of Intrusion
Detection Systems (IDS)
and supervised learning
algorithms in countering
cyber threats, emphasizing
optimal performance for
effective anomaly
detection.
02
Survey of intrusion detection systems:
techniques, datasets and challenges
The paper provides a current taxonomy, reviews IDS research, and classifies
proposed systems. It outlines existing IDSs, surveys data-mining techniques, and
explores signature-based and anomaly-based methods.
Challenges in building IDS,
varying effectiveness of
data mining techniques,
and critical evaluation
factors like time are
discussed, highlighting gaps
in existing research.
03
Enhancement of Intrusion Detection
System using Machine Learning
This research explores the surge in network attacks in the digital age. Utilizing
Intrusion Detection Systems (IDS), as depicted enhance security by closely
monitoring firewall and router functions.
The research delves into the
rising challenges of digital-era
network attacks. Utilizing IDS
and machine learning it
bolsters cyber security by
enhancing threat detection
precision, minimizing false
alarms.
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL

7. AIMS AND OBJECTIVES
AIM OF THE PROJECT
• Develop an Intrusion Detection System (IDS) leveraging AI and ML for enhanced cyber security.
• Achieve real-time identification, minimizing false positives/negatives.
OBJECTIVES
• Implement an autonomous response system for threat mitigation.
• Achieve precise threat detection through optimized feature extraction.
• Ensure real-time responsiveness to counter evolving threats promptly.
• Evaluate IDS with emphasis on accuracy and minimal false positives. Contribute to cyber
security knowledge by addressing limitations and suggesting future research for
enhanced intrusion detection source use, and provide comprehensive documentation.
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL

8. TOOLS AND SOCIAL IMPACTS:
TOOLS
 Python for AI/ML development
• TensorFlow , Pandas , Keras , Wireshark , Jupyter
Notebooks , Docker , Seaborn , HashLib , SQLite
we will utilize these tools for our project implementations
SOCIAL IMPACTS (SDG’s)
• Industry, Innovation, and Infrastructure
• Peace, Justice, and Strong Institutions
• Quality Education
• Decent Work and Economic Growth
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL

9. TIMELINE
DEPARTMENT OF TELECOMMUNICATION, MUET BATCH 20TL
Working on PPT
07/12/2023
15/12/2023
12/04/2023
10/01/2024
22/01/2023
Get Familiar With Tools
Literature Review
Finalize the PPT work
Getting the stuff

10. REFRENCES:
1. Khraisat et al. Cybersecurity (2019) 2:20 Survey of intrusion detection systems: techniques,
datasets and challenges Ansam Khraisat* , Iqbal Gondal, Peter Vamplew and Joarder Kamruzzaman.
2. A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
P.; Newe, T.; Dhirani, L.L.; O’Connell, E.; O’Shea, D.; Lee, B.; Rao, M. A Study of Network Intrusion Detection
Systems Using Artificial Intelligence/Machine Learning. Appl. Sci. 2022, 12, 11752.
3. A comprehensive review of AI based intrusion detection system Sowmya T.a,* , Mary Anita E.A.b d 30
October 2022

11. REFRENCES:
4. The 13th International Conference on Ambient Systems, Networks and Technologies (ANT)
March 22-25, 2022, Porto, Portugal Intrusion Detection Systems using Supervised Machine
Learning Techniques: A survey Emad E. Abdallah*, Wafa’ Eleisah, Ahmed Fawzi Otoom
5. Hammad, M., El-medany , W., & Ismail, Y. (2020, December). Intrusion Detection System using
Feature Selection With Clustering and Classification Machine Learning Algorithms on the
UNSW-NB15 dataset. In 2020 International Conference on Innovation and Intelligence for
Informatics, Computing and Technologies (3ICT) (pp. 1-6). IEEE

12. Any Query you may ask.
THANK YOU!