UNDERCODE COMMUNITY
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πŸ¦‘ Undercode Cyber World!
@UndercodeCommunity


1️⃣ World first platform which Collect & Analyzes every New hacking method.
+ AI Pratice
@Undercode_Testing

2️⃣ Cyber & Tech NEWS:
@Undercode_News

3️⃣ CVE @Daily_CVE

✨ Web & Services:
β†’ Undercode.help
Download Telegram
Forwarded from UNDERCODE TESTING
πŸ¦‘ AI models and tools for cybersecurity and hacking research

1. Code Analysis and Security Tools
- CodeBERT: AI model for secure code analysis.
URL: [https://huggingface.co/microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base)
- DeepCode by Snyk: Detects vulnerabilities in codebases.
URL: [https://www.deepcode.ai/](https://www.deepcode.ai/)
- Joern: Open-source code analysis tool designed for vulnerability discovery.
URL: [https://joern.io/](https://joern.io/)

2. Adversarial Models and Simulations
- DeepExploit: Automates penetration testing with AI-driven attacks.
URL: [https://github.com/13o-bbr-bbq/machine_learning_security](https://github.com/13o-bbr-bbq/machine_learning_security)
- TextAttack: Framework for generating adversarial text for NLP systems.
URL: [https://github.com/QData/TextAttack](https://github.com/QData/TextAttack)
- Foolbox: Test the robustness of AI models with adversarial inputs.
URL: [https://foolbox.readthedocs.io/](https://foolbox.readthedocs.io/)

3. Malware Detection and Threat Intelligence
- MalConv: Neural network for malware detection from raw binaries.
URL: [https://github.com/Endermanch/MalwareDatabase](https://github.com/Endermanch/MalwareDatabase)
- Cuckoo Sandbox AI: Analyze malware behavior with AI integration.
URL: [https://cuckoosandbox.org/](https://cuckoosandbox.org/)
- Viper Framework: Malware and binary analysis toolset.
URL: [https://github.com/viper-framework/viper](https://github.com/viper-framework/viper)

4. Image and Data Analysis Tools
- YOLO (You Only Look Once): Detect anomalies or objects in visual data.
URL: [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
- AutoML Vision by Google: Train custom vision models for detecting tampering.
URL: [https://cloud.google.com/automl](https://cloud.google.com/automl)

5. Network and System Monitoring Tools
- SnortAI: Intrusion detection with machine learning enhancements.
URL: [https://www.snort.org/](https://www.snort.org/)
- Zeek AI (Bro): Network analysis framework extended with AI.
URL: [https://zeek.org/](https://zeek.org/)
- DeepPacket: AI-driven analysis of network packet data.
URL: [https://github.com/xiaokexiang/DeepPacket](https://github.com/xiaokexiang/DeepPacket)

6. General Security Tools with AI Features
- TriageML: Machine learning for automating malware triage.
URL: [https://github.com/fireeye/Triage](https://github.com/fireeye/Triage)
- AI-Hunter: AI-driven platform for detecting and investigating cyber threats.
URL: [https://www.criticalstart.com/ai-hunter/](https://www.criticalstart.com/ai-hunter/)
- ELK Stack with AI: Integrate AI plugins with the ELK Stack for advanced threat detection.
URL: [https://www.elastic.co/](https://www.elastic.co/)
Forwarded from UNDERCODE NEWS (Copyright & Fact Checker)
⚑️ #AI-Powered Identity Access Management: A New Cybersecurity

https://undercodenews.com/ai-powered-identity-access-management-a-new-cybersecurity/

@Undercode_News
Forwarded from UNDERCODE NEWS (Copyright & Fact Checker)
Forwarded from Exploiting Crew (Pr1vAt3)
πŸ¦‘ Email Tracking:


### πŸ” About Zehef
Zehef specializes in:
- Locating public data associated with a specific email address.
- Providing insights into email breaches, pastes, and social media accounts.

---

### 🌠 Key Features
1. Breached Data Check:
- Identify if the email has appeared in any public data breaches using sources like Pastebin or others.
2. HudsonRock Integration:
- Leverage HudsonRock to analyze leaks and identify potential compromises.
3. Social Media Account Discovery:
- Check for linked accounts across platforms such as:
- Instagram
- Spotify
- Deezer
- Adobe
- 𝕏 (formerly Twitter), etc.
4. Email Combination Generation:
- Create possible variations of the target email for further testing or OSINT purposes.

---

### πŸ“¦ Installation
#### Prerequisites
- Python: Version 3.10 or later.
- Git: Installed and configured.

#### Steps:
1. Clone the Zehef repository:
   git clone https://github.com/N0rz3/Zehef.git
cd Zehef

2. Install the dependencies:
   pip3 install -r requirements.txt


---

### 🎲 Usage
Run the tool with the following syntax:
python3 zehef.py [email]


#### Example:
python3 zehef.py target@example.com


#### Available Options:
- email: The email address for which you want to retrieve information.
- -h, --help: Displays help information.

---

### πŸ’‘ Notes
Zehef is an advanced tool for ethical OSINT practices and should only be used with proper authorization. Misuse could violate laws or terms of service on various platforms.

πŸ”— GitHub Repository: [Zehef on GitHub](https://github.com/N0rz3/Zehef)
Forwarded from Exploiting Crew (Pr1vAt3)
πŸ¦‘ To "run a script" on a quantum computer, you typically use a quantum programming language like Qiskit (for IBM Quantum), Cirq (for Google's quantum devices), or others like Braket (Amazon)

Quantum Script Using Qiskit

# Import necessary libraries
from qiskit import QuantumCircuit, Aer, execute

# Step 1: Create a quantum circuit with one qubit and one classical bit
qc = QuantumCircuit(1, 1)

# Step 2: Apply a Hadamard gate to put the qubit in superposition
qc.h(0)

# Step 3: Measure the qubit
qc.measure(0, 0)

# Step 4: Simulate the quantum circuit
simulator = Aer.get_backend('qasm_simulator') # Classical simulation of a quantum computer
result = execute(qc, simulator, shots=1024).result()

# Step 5: Retrieve and display results
counts = result.get_counts()
print("Measurement Results:", counts)

# Optional: Visualize the circuit
print(qc)


---

### What It Does:
1. Hadamard Gate (H): Places the qubit in a superposition, meaning it's in a mix of |0⟩ and |1⟩.
2. Measurement: Collapses the qubit to either |0⟩ or |1⟩ probabilistically upon measurement.
3. Simulation: Runs the quantum program multiple times (e.g., 1024 shots) on a classical simulator to emulate quantum results.

---

### Output Example:
After running, you might see:
Measurement Results: {'0': 511, '1': 513}
β”Œβ”€β”€β”€β” β–‘ β”Œβ”€β”
q_0: ─ H β”œβ”€β–‘β”€β”€Mβ”œ
β””β”€β”€β”€β”˜ β–‘ β””β•₯β”˜
c_0: ────────╨─


---

### Running on a Real Quantum Computer:
If you have access to IBM Quantum or a similar quantum platform:
1. Replace the simulator with an actual quantum backend:
   from qiskit import IBMQ
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
backend = provider.get_backend('ibmq_qasm_simulator') # Use an actual quantum device here
result = execute(qc, backend, shots=1024).result()


Quantum computing is still in its infancy, and scripts generally focus on demonstrating concepts or solving specific problems (e.g., optimization or quantum chemistry simulations). Let me know if you'd like to explore further!