Exploring neural networks for digital forensics.

May 13, 2024

Neural networks have become essential to technologies for their ability to learn complex patterns from data, adapt to different domains, and achieve state-of-the-art performance in various machine learning task such as classification, regression, clustering, and generation. Examples of technologies that benefit from neural networks include AI chatbots, voice assistants such as Siri, Alexa, and Google Assistant, self-driving cars, smart home devices, and gaming. The speed and automation capabilities provided by neural networks can also boost the effectiveness and productivity of digital forensic investigations.

What is a neural network?

Neural networks are a class of machine learning algorithms “inspired” by the structure and functioning of the human brain. Neural networks typically are composed of:

Neurons – The basic computational units in a neural network receive input signals from other neurons or external sources, process them, and produce an output signal.

Layers – Neural networks are organized into input and output layers. Each layer contains one or more neurons. The input layer receives input data, and the output layer produces the network’s output. Layers between the input and output layers are called hidden layers.

Weights and biases – Connections between neurons in adjacent layers are categorized by weights, determined by the strength of the connections. Also, each neuron usually has an associated bias, which allows the network to identify and learn more complex patterns.

Activation function – The activation functions of a neuron determine its output based on the weighted sum of its inputs, each with their own characteristics and use cases.

Feedforward and backpropagation – In the feedforward phase, input data is passed through each layer of the network to produce a prediction or output. During training, the network’s predictions are compared to the actual targets, with any error propagated backward through the network. Backpropagation adjusts the weight and biases to minimize the error and improve a neural network’s performance.

How neural networks empower digital forensics

The ability to learn complex patterns from data make neural networks powerful tools for digital forensics practitioners tasked with analyzing, interpreting, and extracting insights from diverse types of digital evidence, thus enhancing efficiency and effectiveness of their investigations.

Neural network capabilities are valuable for identifying relevant evidence, classifying data types, and linking disparate pieces of information in forensic investigations including:

Digital image forensics – The ability to learn and recognize visual patterns, including image manipulation or authenticity, aids effective image classification, tampering detection, and forgery detection.

Document analysis – Analysis of digital documents including text documents, emails, or PDF files, as well as extracting metadata, identifying authorship, detecting plagiarism, or uncovering hidden information.

Pattern recognition – Neural network pattern training empowers it to recognize various types of digital evidence including text, audio, video, and sensor data.

Intrusion detection – By analyzing network traffic patterns, anomalies, and known attack signatures, neural networks can detect abnormal or suspicious activities including intrusions or cyber attacks in real-time or post-incident analysis.

Steganalysis – This analysis reveals covert communications or data concealment by detecting hidden information such as data or messages within digital media files.

Data recovery – The ability to analyze fragmented or corrupted data structures can support data recovery efforts. Neural networks can be trained to reconstruct missing or damaged data patterns and recover information from compromised or damaged storage media.

How neural networks and Artificial Intelligence speed investigations

Neural networks and Artificial Intelligence can empower digital forensics by maximizing productivity and resources. Oxygen Forensic® Detective’s image categorization capabilities are powered by a neural network that scans the visual composition of an image to approximate the consistency of enough data that matches a particular supported category.

Using neural networks to enhance image categorization significantly improves the speed of sorting and analyzing visual data on a subject’s device. Artificial intelligence helps speed the process of identification when working with images, eliminating irrelevant data, and recognizing relevant images.

Interested in learning more about how Oxygen Forensics leverages neural networks and AI? Contact us.

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