almost everything is done online, such as banking, shopping, studying, and communication. Because of this, cyber attacks are increasing day by day. Hackers try to steal data, damage systems, or block services using different methods. Traditional security systems work based on fixed rules, so they cannot easily detect new types of attacks. This creates a need for smarter security systems. There are different types of cyber attacks. Malware is harmful software that can damage computers or steal information. Phishing attacks trick people into sharing personal details through fake emails or messages. Denial-of-service attacks overload a system so that normal users cannot access it. Insider threats happen when someone inside an organization misuses their access. These attacks are difficult to stop using traditional methods.

1. Abstract
• Cybersecurity threats have increased rapidly
due to digital transformation, cloud
computing, and IoT.
• Traditional rule-based systems fail to detect
modern and zero-day attacks.
• This work proposes a Machine Learning-based
threat detection system.
• The model analyzes network traffic to identify
malicious behavior with higher accuracy.

2. Introduction
• Cybersecurity protects systems, networks, and
data from cyber attacks.
• Attacks include malware, phishing, DDoS, and
insider threats.
• Machine Learning enables intelligent and
automated detection.

3. Problem Statement
• Increasing cyber attacks.
• High false positives in traditional IDS.
• Inability to detect unknown threats.
• Need for automated and scalable detection.

4. Literature Review
• ML-based IDS outperform traditional systems.
• Algorithms like SVM and Random Forest are
widely used.
• Challenges include data imbalance and real-
time detection.

5. Existing vs Proposed System
• Existing:
• - Rule-based IDS
• - Manual updates
• - Poor detection
• Proposed:
• - ML-based system
• - Automated learning
• - Detects unknown attacks

6. Architecture & Methodology
• Data Collection → Preprocessing → Feature
Selection → ML Training → Detection
• Algorithms: SVM, Random Forest

7. Results & Analysis
• Improved detection accuracy.
• Reduced false positives.
• Better performance than traditional methods.

8. Applications & Future Scope
• Applications:
• - Enterprise networks
• - Cloud & banking
• - IoT systems
• Future Scope:
• - Deep learning
• - Real-time detection