Identify key challenges and limitations in current systems

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Reference no: EM133841979 , Length: word count:4000

Question: Designing an Intrusion Detection System for Communication Networks.

A. Instructions:
1. Write a 3000-4000 word proposal. The focus should be on system design and its practical application.
2. The communication system can be IoT network, wireless network, cellular network, SDN based network. You can choose one of these networks as your problem domain. Get in touch with us for low-cost assignment help!
B. Structure your proposal as follows:

1. Abstract (200-300 words): Summarize the proposed design, highlighting key features and objectives.

2. Introduction (300-500 words):
i. Explain the importance of IDS in communication networks.
ii. Identify key challenges and limitations in current systems.

3. System Requirements (400-600 words):
i. Define functional and non-functional requirements of the proposed IDS.
ii. Highlight the importance of real-time detection, scalability, and low false-positive rates.

4. System Architecture (800-1000 words):
i. Design a comprehensive architecture for the IDS, including:
1. Data collection modules.
2. Detection engines (e.g., signature-based, anomaly-based).
3. Alerting and reporting systems.
ii. Use diagrams or flowcharts for clarity.

5. Integration of Machine Learning (500-800 words):
i. Explain how machine learning models (e.g., deep learning, clustering) will enhance the system.
ii. Provide examples of datasets and evaluation metrics to be used.

6. Implementation Challenges and Mitigation Strategies (400-600 words):
i. Discuss challenges such as computational overhead, encryption, and evolving threats.
ii. Propose solutions or trade-offs to address these issues.

7. Future Scope (300-500 words):
i. Highlight potential extensions or upgrades to the system.

8. Conclusion (200-300 words):
i. Summarize the design and its significance in securing communication networks.
C. Submission Format:
• Use IEEE format for citations and references.
• Submit the proposal as a PDF document.

Evaluation Criteria
1. Abstract and Introduction
2. Clarity and feasibility of system requirements
3. Detailed architecture and use of diagrams
4. Integration of machine learning and justification
5. Discussion of challenges and solutions
6. Formatting, originality, and references

Reference no: EM133841979

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