Project Background:
Network anomaly detection addresses the increasingly complex and evolving nature of cyber threats. Traditional methods of detecting network anomalies often struggle to keep pace with the sophistication of modern attacks can evade detection by mimicking normal network behavior or employing novel evasion techniques. In this context, the project aims to harness the power of deep learning algorithms to improve the accuracy and efficiency of network anomaly detection systems. By training deep neural networks on vast datasets containing both normal and anomalous network traffic patterns, the system can learn to distinguish between benign and malicious activities with a higher degree of precision.
Additionally, deep learning models have the capability to adapt and evolve, enabling them to detect previously unseen anomalies and adapt to changing attack methodologies. It considers the scalability and real-time processing requirements essential for effective anomaly detection in large-scale networks. Deep learning frameworks offer the advantage of parallel computation, allowing for efficient processing of massive network traffic data in near real-time. This capability is crucial for promptly identifying and mitigating potential security threats before they cause significant harm or damage to network infrastructure and sensitive information.
Problem Statement
- Traditional methods struggle to detect sophisticated cyber threats that mimic normal behavior or employ novel evasion techniques.
- Demand for more accurate anomaly detection systems to effectively distinguish between benign and malicious activities.
- Systems must handle large-scale network traffic efficiently to detect real-time anomalies without compromising performance.
- Anomaly detection systems must evolve and adapt to new attack methodologies and previously unseen anomalies.
- Timely identification and mitigation of security threats are essential to prevent significant damage to network infrastructure and sensitive data.
Aim and Objectives
- To develop a robust network anomaly detection system using deep learning techniques.
- Train deep neural networks to distinguish between normal and anomalous network traffic patterns accurately.
- Enhance scalability to process large volumes of network data in real-time efficiently.
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Improve adaptability to detect and mitigate new and evolving cyber threats.
- Validate the system performance through rigorous testing and evaluation against diverse datasets.
- Implement the developed solution in practical network environments to assess its effectiveness in real-world scenarios.
Contributions to Network Anomaly Detection using Deep Learning
- Improved the precision of anomaly detection by leveraging deep learning algorithms to recognize subtle patterns indicative of malicious activities.
- Developed methods for efficiently processing large-scale network traffic data in real-time, facilitating the detection of anomalies across network infrastructures.
- Equipped anomaly detection systems with the ability to adapt and evolve to identify and respond to new and evolving cyber threats effectively.
- Provided practical solutions in real-world network environments, offering tangible benefits in enhancing cybersecurity defenses against sophisticated attacks.
- Validated the effectiveness of proposed methods through rigorous testing and evaluation against diverse datasets, establishing their reliability and robustness in detecting anomalies.
Deep Learning Algorithms for Network Anomaly Detection
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory networks (LSTMs)
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Autoencoders
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Generative Adversarial Networks (GANs)
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Variational Autoencoders (VAEs)
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Deep Belief Networks (DBNs)
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Deep Q-Networks (DQNs)
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Graph Convolutional Networks (GCNs)
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Siamese Networks
Datasets for Network Anomaly Detection using Deep Learning
- NSL-KDD
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UNSW-NB15
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CICIDS 2017
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KDD Cup 1999
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DARPA Intrusion Detection Evaluation Dataset (1998)
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ADFA Intrusion Detection Datasets
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Kyoto 2006+ Dataset
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ISCX-IDS 2012
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AWID (AISec Wi-Fi Intrusion Dataset)
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CTU-13 Dataset
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
