intrusion detection system for the hackathon.pptx

about Hackathon

1. Enhanced Intrusion Detection System with AI/ML for Real-Time Threat Detection

2. Problem statement In the modern digital landscape, organizations face an increasing number of cyber-attacks , with network intrusions being a significant threat to data integrity, privacy, and overall security. Traditional network security solutions, such as firewalls and signature-based detection systems, are limited in their ability to detect novel and evolving threats, especially zero-day attacks and advanced persistent threats (APTs).

3. Key Challenges 1.Accurately detect malicious activities in real-time , including both known and unknown (zero-day) threats. 2.Learn and adapt from new attack patterns using machine learning techniques. 3. Handle large volumes of network traffic efficiently , without degrading network performance. 4.Provide actionable insights and automatic responses to neutralize or mitigate detected threats.

4. ABSTRACT This project presents an  Enhanced Intrusion Detection System (IDS) integrated with AI/ML algorithms for real-time detection of both known and emerging cyber threats. By analyzing behavior patterns and identifying anomalies, the system improves on traditional signature-based methods , offering proactive security. It provides adaptive threat recognition, automated response, and enhanced network protection for modern digital environments.

5. Why we are doing this project 1. Detecting Unknown Threats . 2.Real-Time Response 3.Adaptability to Evolving Threats 4.Enhanced Security for Modern Networks 5.Advantages for new website developers 6. Become more effective

6. Flowchart

7. Various attacks that can be saved through this model 1.Denial of Service (DoS): An attacker attempts to prevent authorized users from using a service. For example, SYN flood, smurf , and teardrop. 2.User-to-Root (U2R): An attacker has local access to the victim's computer and tries to gain super-user privilege. For example, buffer overflow attacks. 3.Remote to Local (R2L): An attacker tries to gain access to the victim's machine without having an account on it. For example, a password-guessing attack.

8. THANK YOU