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Use of Machine Learning in Digital Forensics
Use of Machine Learning in Digital Forensics

September 12, 2022

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Introduction

 

Every minute, we are hit by a flood of information that is rapidly increasing, drowning us in a massive amount of information, with every piece of it potentially false. Since the 1990s, the world has been undergoing a digital revolution that has ostentatious our way of life. Mobile phones, the Internet, and a plethora of other services and digital devices have become a fundamental part of our daily lives. It has, however, resulted in an increase in the amount of data and valuable information generated in digital forms, such as emails, digital photos, and phone books. This phenomenon has also resulted in a shift in the requirements of law enforcement agencies. 

 

Machine Learning in Digital Forensics

  • Machine Learning Algorithms

Machine learning algorithms and techniques are used in a wide range of applications. Machine learning developers and forensic investigators must thoroughly understand the algorithms being used, how they work, and how to learn from raw data to work more accurately.

  • Support Vector Machines

An SVM is an abstract machine learning algorithm that tries to learn by training on a specific data set to make a correct prediction and generalization of the remaining data.

  • Decision Tree

A decision tree (DT) can be used as a statistical model in the classification process. This algorithm divides data into classes and generates a flow chart, such as a tree structure, as a result. A DT algorithm divides data in a dataset using a query structure, working from the root to the leaf, each representing one class. This DT strategy is a top-down strategy, which is the most commonly used strategy for generating decision trees from data.

  • Naive Bayes Classification

The Naive Bayes classification can be thought of as a probabilistic classifier derived from the Bayes theorem application. That is, a statistical equation defines the relationship between conditional probabilities. The Naive Bayes classification could be very useful in high dimension datasets because it is a simple and fast classification algorithm and a baseline for the classification problem. It is a quick and dirty algorithm based on naive assumptions about data. There are several naive Bayes classifiers, including Gaussian Naive Bayes.

  • k-Nearest neighbors (KNN)

The k-nearest neighbor algorithm is a nonparametric method used for regression and classification. In both circumstances, k is the input indicating the feature space occupancy by the nearest training sample. Whether classification or regression is used will determine whether the feature space is used.

  • Artificial Neural Networks (ANN)

A neural network, also known as an artificial neural network, is a machine learning algorithm derived from the model or system that serves the human brain or human neurons. The human brain, made up of millions of neurons, uses electrical and chemical signals to communicate and process them. Synapses are special structures that attach to neurons and allow signals to pass. 

  • Machine Learning Forensics

The application of machine learning in digital forensics has given rise to a new field known as machine learning forensics, which can recognise criminal patterns and predict criminal activity, such as where and when crimes are likely to occur. For this type of digital forensics to occur, a framework must be capable of capturing and analyzing servers, whether on the Internet or via a wireless connection, as well as many other types of data for link association, visualization, segmenting, and clustering of criminal activity.

  • Link Analysis

Law enforcement used this technique dynamically to create charts that demonstrated a match between the suspects and the evidence collected before the advent of computers and modern technologies. Machine learning forensics can use link analysis to determine the content and structure of a body of information by converting it into a set of interconnected new associations.

Conclusion

The field of digital forensics is becoming more advanced and important as it requires a large amount of complex data to be analyzed and extracted from the crime scene. The digital forensic investigation is an examination of digital evidence about the crime to be used as legal proof in a court of law. Machine learning is an optimal approach to solving problems in the field of digital forensics in this process. In reviving and analyzing digital evidence, various machine learning algorithms and techniques can be useful. Machine learning will speed up this process by processing large amounts of data in a short period of time while maintaining a high level of accuracy and quality.

 

 

 


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