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Machine Learning in Digital Forensics

  • June 26, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Introduction

We are overwhelmed with an enormously big stream of information that is increasing minute by minute, drowning us in a sea of data, many of which are potentially untrue. The globe has undergone a digital revolution during the 1990s that has transformed our way of life. We now unquestionably rely on mobile phones, the internet, and a wide range of other digital services and devices. The amount of data and important information created in digital formats, such as emails, digital photographs, and phone books, has increased as a result, though. The requirements of law enforcement authorities have changed as a result of this issue. Machine learning, which has its roots in artificial intelligence, may be seen to be the fundamental component of behavioural forensics. Software for pattern recognition can cope with enormous volumes of data using machine learning to stop any unlawful conduct. These training sessions might involve anything from intrusion attacks to burglary and money laundering. Utilising networked software and tools, machine learning may be used for remote analysis.

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Machine Learning Algorithms

Many diverse applications use machine learning methods and methodologies. Machine learning researchers and developers must have a thorough grasp of the algorithms being utilised, how they function, and how to learn from raw data to function even more precisely.

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Support Vector Machines

An SVM can be observed as an abstract machine learning algorithm that attempts to learn by training on a particular set of data to make a correct prediction and generalization on the remaining data. An excellent way to understand SVM would be by taking a binary classification, for example, or many problems in the real world that include making predictions over two classes. SVMs can be categorized as supervised learning models along with the related algorithms that are utilized for pattern recognition, analyzing data, regression analysis, and classification. Possess a group of training examples, with each belonging to a separate category from two categories, an SVM algorithm starts training on the examples to build a model that finds to which categories the new examples belong. An SVM model can be presented by the example of mapped points in space so that the separate category examples could be separated by a clear and wide gap, and lastly, the decision of where to map the new examples is made based on which side of the gap they belong to, and that is one of the categories.

Decision Tree

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A decision tree (DT) can be used as a statistical model in the categorization process. This method divides data into classes and produces a flowchart, such as a tree structure, as a consequence. A query structure is used by a DT algorithm to separate data in a dataset by proceeding bottom-up from the root to the leaf representing one class. In a classification, the class is represented by the leaf, and the root defines the property that has a significant impact. The steps that make up a DT's categorization process are as follows:

putting all training samples into one root for display.

Depending on the attributes chosen, we are dispersing training examples.

Statistical metrics are used to choose qualities.

Until all training examples are categorised, all of the remaining training examples belong to a single class, there are no characteristics, or all training examples are classified, the recursive partition procedure is carried out.

As the most common method for creating decision trees from data, we may conclude that this DT technique is top-down.

Naïve Bayes Classification

The Naïve Bayes classification can be explained as a probabilistic classifier that is derived from the Bayestheorem application]. That is, it is an equation in statistical quantities defining the connection of conditional probabilities. In high dimension datasets, the Naïve Bayes classification could be very useful as they are a simple and fast classification algorithm as well as being a baseline for the classification problem by being a quick and dirty algorithm based on naïve assumptions about data. Various naïve Bayes classifiers exist such as Gaussian Naïve Bayes. Perhaps the untroubled way to understand a naïve Bayes classifier is through this algorithm. This classifier works on the assumption that in each label, data is drawn from an easy Gaussian distribution. One of the best ways to create a model is to assume that the data described by the Gaussian distribution has no covariance between dimensions. By finding the standard deviation and mean, we could fit this model within each label.

The k-Nearest Neighbours

An explanation of the k-nearest neighbour algorithm as a nonparametric technique for regression and classification is possible. Whether classification or regression is utilised will decide the output of the k-NN in both scenarios, where k is the input pointing to the nearest training sample that occupies the feature space:

An outcome from using K-NN for classification will be a class member. An item is identified as one of the closest k neighbours through neighbours; for example, if k=1, the object is assigned to the most prevalent class where k's value is positive and often has a low average value, or k-NN.

Artificial Neural Networks

A neural network or an artificial neural network is one of the algorithms in machine learning derived from the model or system that serves in the human brain or the human neuron. Made up of millions of neurons, the human brain uses ]electrical and chemical signals to communicate and then processes them. Special structures, known as synapses, attach to these neurons, which allow signals to pass. A neural network, as one of the algorithms in machine learning, replicate the behavior of ‘neurons’ from the biological system, having the capability of pattern recognition, alongside being used in machine learning by having a group of interconnected ‘neurons’ that work on the input to deliver an output value, consisting of three layers.

Machine Learning Forensics

Machine learning has the ability to recognise criminal patterns and anticipate criminal activities, such as where and when crimes are likely to occur. This new discipline, known as machine learning forensics, has been developed as a result of the use of machine learning in the field of digital forensics. A framework must be able to gather and analyse servers that are connected to the internet or wirelessly, as well as many other sorts of data, in order for this form of digital forensics to be carried out. This data is needed for link association, visualisation, segmentation, and grouping of criminal activities. There are several methods.

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Conclusion

Digital forensics is a rapidly developing and significant discipline that frequently requires a significant amount of sophisticated data to be analysed and collected from the crime scene. Examining the digital evidence related to the crime that will be used as evidence in court is a part of the digital forensic investigation. In this process, machine learning might be viewed as the best method for addressing the issues that the digital forensics industry faces. Digital evidence may be revived and analysed using a variety of machine learning methods and approaches. Machine learning will speed up this process by processing a big quantity of data quickly, accurately, and with high-quality results. Investigators are encouraged to use the forensic analysis techniques because they enable them to detect various forms of criminal activity on servers, the internet, or through a wireless connection. These techniques also enable data link association, visualisation, segmentation, and clustering.

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