Project Background:
Cybersecurity using deep learning revolves around an imperative need to enhance the defenses against increasingly sophisticated and evolving cyber threats. Traditional cybersecurity measures have proven insufficient in countering the dynamic nature of these attacks. Deep learning has emerged as a promising solution due to its ability to analyze vast datasets and recognize intricate patterns in network traffic, malware, and user behavior. Cybersecurity experts can develop robust intrusion detection systems, malware classifiers, and predictive threat models by leveraging deep neural networks. This project aims to harness the power of deep learning models to mitigate and detect cyber threats in real-time, enabling organizations to proactively safeguard digital assets and infrastructure against the ever-growing landscape of cyber risks. Ultimately, this initiative seeks to significantly bolster the security posture of systems and networks in an era where cybersecurity has become a paramount concern.
Problem Statement
- In the digital age, the proliferation of sophisticated attacks and advanced persistent threats pose a constant risk to organizations and individuals.
- The specific problem addressed by this cybersecurity using deep learning project is the need for more advanced, adaptive, and proactive cybersecurity solutions.
- Deep learning technologies like neural networks can significantly improve threat detection and vulnerability assessments by autonomously learning and adapting new patterns and attack techniques.
- This focuses on developing, implementing, and fine-tuning deep learning algorithms and models to identify and mitigate cyber threats from malware, intrusion attempts, insider threats, and zero-day vulnerabilities.
Aim and Objectives
- Develop real-time intrusion detection systems for rapid threat identification.
- Create accurate malware classifiers to detect and mitigate malicious software.
- Enhance user behavior analysis to identify abnormal activities.
- Build predictive threat models to forecast potential cyber threats and vulnerabilities.
- Improve network anomaly detection for early threat recognition.
- Implement deep learning-based authentication and access control mechanisms.
- Reduce false positive rates to focus on genuine threats.
- Enhance the overall cybersecurity resilience in the face of evolving threats.
Contributions to Cyber Security using Deep Learning
1. In this project, an enhanced zero-day threat detection method improves the capacity to identify and defend against previously unknown cyber threats in real-time.
2. Advancements in deep learning aim to make AI-driven security more transparent and interpretable, aiding in understanding and countering emerging threats.
Deep Learning Algorithms for Cyber Security using Deep Learning
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Generative Adversarial Networks (GANs)
- Radial Basis Function Networks (RBFNs)
- Deep Belief Networks (DBN)
- Deep Q-Networks (DQN)
- Autoencoders
- Self-Organizing Maps (SOM)
- Restricted Boltzmann Machines (RBMs)
- Variational Autoencoders (VAE)
- Attention Mechanisms
- Transformer Models
- Capsule Networks
- Siamese Networks
Datasets for Cyber Security using Deep Learning
- NSL-KDD
- CICIDS2017
- UNSW-NB15
- DARPA IDS
- KDD Cup 1999
- ISCX
- CTU-13
- ADFA-IDS
- Kyoto 2006+ dataset
- BoT-IoT Dataset
- SANA Dataset
- AWID Dataset
- IDS 2018 Dataset
- APT Simulation Dataset
- Stratosphere IPS Dataset
- AEDS Dataset
Performance Metrics
- Accuracy (ACC)
- Precision
- Recall
- F1-Score
- True Positive (TP)
- True Negative (TN)
- False Positive (FP)
- False Negative (FN)
- Area Under the Receiver Operating Characteristic (ROC-AUC)
- Area Under the Precision-Recall Curve (PR-AUC)
- Matthews Correlation Coefficient (MCC)
- Detection Rate (DR)
- False Alarm Rate (FAR)
- Specificity (SPC)
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
