In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
-By Nilanjan Chakravortty, Hitachi Research & Debdipta Halder, FireCompass & Arnab Chattopadhayay, FireCompass
The Inspiration of ANN - the Biology behind it
Interesting Facts about the Human Brain:
- Contains over 100 billion neurons and 100 trillion synapses.
- More than 10,000 specific types of neurons.
- A small piece of the human brain used in experiments has around 4000 nerve fibers connected to a single neuron and holds about 1.4 petabytes of data.
Neuron Overview:
- Neurons transfer information around the body.
- Similar to other cells but with unique features for transferring "action potentials."
Key Components of a Neuron:
- Dendrite: Receives signals from neighboring neurons.
- Soma: Signal processing, protein synthesis, metabolic activities.
- Axon: Transmits signals over a distance.
- Axon Terminal: Transmits signals to other neurons or tissues.
Action Potentials:
- Electrical impulses that send signals around the body, dependent on concentration gradient and resting membrane potential.
How Action Potentials Work:
- Temporary shift in the neuron's membrane potential caused by ion flow.
- Voltage-gated channels open and close based on voltage difference across the cell membrane.
- Gates m and h for sodium channels and gate n for potassium channels regulate the action potentials.
History of ANN Development
ANN development has evolved through various stages, from simple perceptrons to complex deep learning models.
Structure of ANN
Typical ANN Structure:
- Input Layer: Receives the input data.
- Hidden Layers: Extract features and learn patterns.
- Output Layer: Generates final predictions.
Neural Network Zoo
- ANNs come in various forms, each suited to specific tasks and applications.
Mathematics of ANN
Single Perceptron:
- Receives input values, calculates a weighted average, adds bias, and passes through a non-linear activation function.
Multi-layer Perceptron:
- Cascade of layers where each layer's output serves as input to the next.
Build an ANN from Scratch
Deep Neural Network:
- Deep learning involves multi-layered neural networks that can extract complex patterns from large datasets.
Convolutional Neural Network (CNN)
Introducing CNN:
- CNNs are deep neural networks specialized in recognizing and classifying features from images.
Architecture of CNN:
- Convolutional Layer: Extracts features from input images.
- Pooling Layer: Reduces the size of the convolved feature map to reduce computational costs.
- Fully Connected Layer: Connects neurons between layers and aids in the classification process.
Additional Mathematics for CNN:
- Convolution operations and pooling methods (max pooling, average pooling) play a crucial role in feature extraction.
Build a Malware Detection System using CNN
Logical Flow:
- Convert malware binary to grayscale image.
- Preprocess the image through CNN.
- CNN architecture includes convolutional layers, pooling layers, dropout layers, flatten layer, and dense layers with specific configurations for optimal performance.
- Classify malware into predefined categories based on extracted features.
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