Scope: This problem involves creating a framework to crawl and collect cyber incident data from various platforms (e.g., news sites, forums, social media) and analyze it to provide real-time feeds for monitoring and response.

Expected Outcome: A working prototype of a cyber incident crawler that collects data, analyzes it for trends, and provides a real-time feed or dashboard with actionable insights.

What to Do:

• Identify platforms to crawl for cyber incident data (e.g., Twitter, Reddit, security blogs).
• Develop a web crawler using a language like Python (e.g., with Scrapy or BeautifulSoup).
• Implement a basic analysis pipeline to categorize incidents (e.g., by type, severity).
• Create a simple dashboard to display real-time feeds of incidents.

Scope: This problem focuses on designing a digital forensics tool that automates evidence collection, analysis, and reporting, using AI/ML for anomaly detection and pattern recognition in forensic data.

Expected Outcome: A functional digital forensics tool that can collect evidence (e.g., logs, files), analyze it using AI/ML, and generate a report with detected anomalies or patterns.

What to Do:

• Choose a programming language and framework for the tool (e.g., Python with TensorFlow for ML).
• Develop a module to collect evidence from a system (e.g., file system logs, network traffic).
• Implement an AI/ML model to detect anomalies (e.g., unusual file access patterns).
• Create a reporting module to summarize findings in a user-friendly format.

Scope: This problem involves developing techniques to recover deleted data and metadata from XFS and Btrfs filesystems, ensuring the integrity of the recovered data for forensic investigations.

Expected Outcome: A tool or script that can recover deleted files and metadata from XFS and Btrfs filesystems, with a report on the integrity of the recovered data.

What to Do:

• Research the structure of XFS and Btrfs filesystems and their data recovery challenges.
• Use existing tools (e.g., TestDisk, PhotoRec) or develop a custom script in a language like Python or C.
• Test the recovery process on a sample filesystem with deleted files.
• Verify the integrity of recovered data and document the process.

Scope: This problem focuses on building an agent-less vulnerability and network scanner for Windows and Linux systems to identify security weaknesses without requiring software installation on target systems.

Expected Outcome: A working scanner that can identify vulnerabilities and network issues on Windows/Linux systems without installing agents, with a report of findings.

What to Do:

• Research agent-less scanning techniques (e.g., using Nmap, OpenVAS).
• Develop a script or tool to scan for vulnerabilities (e.g., open ports, outdated software).
• Test the scanner on a virtualized Windows/Linux environment.
• Generate a report summarizing the vulnerabilities and network issues found.

Scope: This problem involves developing a browser extension for phishing and vulnerability detection to protect users from malicious websites and attacks.

Expected Outcome: A functional browser extension that can detect phishing attempts and vulnerabilities in websites, alerting users to potential threats.

What to Do:

• Choose a browser to develop the extension for (e.g., Chrome, Firefox).
• Develop a basic extension using JavaScript to analyze website URLs and content.
• Implement a phishing detection mechanism (e.g., using a blacklist or ML model).
• Test the extension on known phishing sites and document its effectiveness.

Scope: This problem focuses on creating a tool for bug hunting and building an API to automate the process of identifying and reporting vulnerabilities in applications.

Expected Outcome: A tool that can identify bugs in a given application and an API that allows users to query and report vulnerabilities programmatically.

What to Do:

• Choose a target application or website to test for bugs.
• Develop a bug hunting tool using a language like Python (e.g., with libraries like requests).
• Build a simple API (e.g., using Flask or Node.js) to report vulnerabilities.
• Test the tool and API with a sample application and document the results.

Scope: This problem involves designing an Intrusion Detection System (IDS) specifically for IoT devices to detect and mitigate security threats in IoT environments.

Expected Outcome: A working IDS prototype for IoT devices that can detect common threats (e.g., unauthorized access, DDoS) and alert users.

What to Do:

• Research common IoT security threats and IDS techniques.
• Choose a platform to simulate IoT devices (e.g., Raspberry Pi, Arduino).
• Develop a basic IDS using a language like Python to monitor network traffic.
• Test the IDS with simulated attacks and document its performance.

Scope: This problem focuses on building an API to simulate and detect DNS spoofing and Man-in-the-Middle (MITM) attacks for educational and defensive purposes.

Expected Outcome: A functional API that can simulate DNS spoofing and MITM attacks and provide detection mechanisms, with a demo of its capabilities.

What to Do:

• Research DNS spoofing and MITM attack techniques.
• Develop an API using a framework like Flask or Node.js to simulate these attacks.
• Implement detection mechanisms (e.g., checking for DNS inconsistencies).
• Test the API in a controlled environment and document the results.

Scope: This problem involves developing a local file encryption and sharing system that demonstrates encryption processes and securely shares files between users.

Expected Outcome: A working prototype of a file encryption and sharing system that encrypts files, shares them locally, and displays the encryption process to users.

What to Do:

• Choose an encryption algorithm (e.g., AES) and a programming language (e.g., Python).
• Develop a script to encrypt and decrypt files using the chosen algorithm.
• Create a simple interface to share encrypted files locally (e.g., via a local server).
• Display the encryption process (e.g., key generation, encrypted data) to users.

Scope: This problem focuses on creating a system that monitors the dark web for leaked databases, passwords, illegal threats, etc., and provides a summary dashboard for cyber intelligence (e.g., for law enforcement).

Expected Outcome: A functional dark web monitoring system that collects data and presents it in a dashboard with summaries of threats, leaks, and other illegal activities.

What to Do:

• Research dark web access methods (e.g., using Tor) and ethical considerations.
• Develop a script to crawl dark web sites for specific data (e.g., using Python with Tor).
• Analyze the collected data to identify threats, leaks, or illegal activities.
• Create a dashboard (e.g., using Flask or Django) to display the summarized data.

Scope: This problem involves developing an AI-powered system for real-time DDoS threat detection and automated defense to protect networks from distributed denial-of-service attacks.

Expected Outcome: A working prototype of an AI-powered DDoS protection system that can detect attacks in real-time and implement automated defense mechanisms.

What to Do:

• Research DDoS attack patterns and AI-based detection techniques.
• Develop an AI model (e.g., using Python with TensorFlow) to detect DDoS attacks.
• Implement a defense mechanism (e.g., rate limiting, IP blocking) triggered by the AI model.
• Test the system with simulated DDoS attacks and document its effectiveness.

Scope: This problem involves designing a chatbot (CopBotChatbox) to bridge the gap between citizens and police, facilitating communication, reporting, and assistance in a user-friendly manner.

Expected Outcome: A working chatbot prototype that allows citizens to report incidents, ask questions, or seek assistance from the police, with basic natural language processing capabilities.

What to Do:

• Choose a chatbot development platform (e.g., Dialogflow, Microsoft Bot Framework).
• Design conversation flows for common scenarios (e.g., reporting a crime, asking for emergency contacts).
• Implement basic NLP to understand user queries and provide relevant responses.
• Test the chatbot with sample interactions and refine the conversation flows.

Scope: This problem involves simulating quantum machine learning techniques to perform simple classification tasks, exploring the potential of quantum computing in ML.

Expected Outcome: A working simulation of a quantum machine learning model that can perform a simple classification task (e.g., binary classification) using a quantum framework.

What to Do:

• Choose a quantum programming framework (e.g., Qiskit, Cirq).
• Implement a simple quantum circuit for a classification task (e.g., using a variational quantum classifier).
• Train the model on a small dataset (e.g., Iris dataset with two classes).
• Evaluate the model's performance and compare it to classical ML methods.

Scope: This problem focuses on implementing a basic quantum error detection mechanism to identify and correct errors in quantum computations, addressing the challenges of noise and decoherence in quantum systems.

Expected Outcome: A working prototype or simulation of a quantum error detection system that can identify and correct at least one type of quantum error (e.g., bit-flip or phase-flip errors) using a quantum programming framework like Qiskit or Cirq.

What to Do:

• Research common quantum errors and error detection codes (e.g., Shor code, Steane code).
• Choose a quantum programming framework to simulate a quantum circuit.
• Implement a simple error detection mechanism and test it with a small quantum circuit.
• Document the results and demonstrate the effectiveness of the error detection.

Scope: This problem involves developing a system for quantum key distribution (QKD) or quantum error correction to enhance the security and reliability of quantum communications, focusing on practical implementation challenges.

Expected Outcome: A functional QKD system (e.g., BB84 protocol) or an error correction system implemented in a quantum programming framework, with a demonstration of secure key exchange or error correction.

What to Do:

• Choose between QKD or error correction based on your team's expertise.
• For QKD: Implement the BB84 protocol using a quantum programming framework.
• For error correction: Implement a quantum error correction code and simulate its performance.
• Test the system with simulated noise and document the results.

Scope: This problem involves creating a blockchain-based course platform where students can earn tokens by completing courses or challenges, incorporating a play-to-earn reward system to incentivize learning.

Expected Outcome: A working prototype of a blockchain-based course platform with a token reward system, where students can earn tokens by completing courses or participating in gamified challenges.

What to Do:

• Choose a blockchain platform (e.g., Ethereum, Binance Smart Chain) for token implementation.
• Design a simple course platform with a few sample courses or challenges.
• Implement a smart contract to manage token rewards for course completion or challenge participation.
• Create a user interface for students to interact with the platform and view their token balance.

Scope: This problem focuses on developing a blockchain-based system for insurance claims, ensuring transparency, security, and efficiency in the claiming process using smart contracts.

Expected Outcome: A functional prototype of a blockchain-based insurance claiming system where users can submit claims, and the system processes them automatically using smart contracts.

What to Do:

• Choose a blockchain platform for smart contract deployment (e.g., Ethereum).
• Design a simple insurance claiming process (e.g., user submits a claim, smart contract verifies, and disburses funds).
• Implement the smart contract to automate the claim verification and payout process.
• Create a user interface for users to submit claims and view the status.