Sample:

Network Intrusion Detection Using Deception Nodes and Machine Learning

1. Introduction

Large academic and organizational networks are prime targets for cyberattacks due to their rich resources. Proactive measures are crucial to secure these networks. This project proposes a system that utilizes deception nodes to gather malware data, analyzes it using machine learning, and implements an intrusion detection system (IDS) for enhanced network security.

2. Problem Statement

Existing security mechanisms like firewalls and IDS offer limited protection against evolving cyberattacks. Selecting specific security measures is difficult due to unpredictable attack types. A centralized security solution is needed to identify, monitor, and collect evidence of various attacks. This information is vital for proactive and reactive (forensic) security measures.

3. Objectives

• To develop a framework to collect malware artifacts, network packets, and system data.

• To investigate existing cyberattack prevention mechanisms and their effectiveness.

• To analyze potential cyberattack patterns to predict future behavior.

• To utilize open-source security tools to improve information system security.

• To apply data analysis and machine learning techniques to identify attack patterns from honeypot data.

• To provide network administrators with critical information about attackers' motives, communication methods, and actions.

4. Methodology

The proposed system involves deploying virtual machine-based deception nodes across various organizational departments. These nodes will utilize sandboxes to analyze attacker and malware activities. Data collected from the nodes, including network packets, memory dumps, system calls, and logs, will be sent to a central command and control server. The server will clean and analyze the streaming data using big data analytics and machine learning models to identify attack patterns. This information will be fed into the proposed IDS for effective intrusion analysis.

The system will handle various heterogeneous datasets, including network data, system logs, operating system calls, and firewall logs. Traditional and custom anomaly and signature-based intrusion detection approaches will be evaluated for optimal system performance.

5. Expected Outcomes

Organizations are vulnerable to large-scale cyberattacks. This project aims to develop a prototype for incident identification within organizations. The system will provide critical insights to network administrators, such as attacker motives, communication methods, and post-compromise actions, aiding in informed decision-making.

6. Work Plan

The project will follow the architecture depicted in Figure 1 (not provided). The workflow involves:

• Collecting malware data from network devices.

• Cleaning and filtering the collected data.

• Creating machine learning models based on the data.

• Optimizing the models for anomaly and signature detection.

• Deploying the models for real-time network monitoring.

7. Summary

The system will collect data (logs, system calls, network traffic, and binaries) from network devices and send it to a central database server. The malware analysis server will perform static analysis on the data, classifying it as clean or malicious (malware signatures and anomalies). The streaming data analytics server will be updated with the results, triggering an IDS alert if malicious activity is detected. The analytics server will continuously monitor the streaming data and report to the administrator periodically.

8. Conclusion

This project proposes a novel approach to network intrusion detection using deception nodes and machine learning. By proactively gathering and analyzing data, the system aims to offer a comprehensive security solution for academic and organizational networks.

System Requirements Specification (SRS)

Functional Requirements

• Deploy deception nodes to capture data from malware activities.

• Collect network packets, system calls, logs, and malware artifacts from deception nodes.

• Implement a centralized server for data collection, cleaning, and analysis.

• Utilize machine learning models to identify attack patterns from collected data.

• Integrate with an intrusion detection system (IDS) for real-time threat detection.

• Provide network administrators with reports on attacker motives, communication methods, and post-compromise actions.

Non-Functional Requirements

• Scalability to accommodate a growing number of deception nodes and data volume.

• High availability to ensure continuous threat detection capabilities.

• Compliance with relevant security standards and regulations.

• User-friendly interface providing clear reports and visualizations for network administrators.