In one of my unpublished research papers “The Role of Data Science in Detection of Fraud and Intrusion over a Network”, I explained in detail on how fraud, intrusion and any kind of illegal communications and actions can be detected and thwarted with the use of modern technologies.
The implementation of the modern technologies such as machine learning and data science algorithms like logistic regression and artificial neural networks which have human-like decision-making techniques will play an enormous role in this kind of task.
Today, internet fraud is increasingly becoming very common in the modern world because even in the technologically and economically developed countries like the United States, this pandemic fraud is still partaking on the virtual planet like a bushfire.
According to Rossenberg (2007), in 2006, the United States recorded a loss of 198 million dollars due to internet fraud, while in the same year BBC report says the United Kingdom recorded an annual loss of 150 million pounds. Hence, In 2018, consumers reported losing about 1.48 billion US dollars related to fraud complaints, an increase of 406 million US dollars from 2017, Federal Trade Commision Annual Reports says.
The reason why internet fraud is becoming very common is because of the boom of the internet industry. According to Forbes, over 90% of all the data in the world was created in the past few years which resulted the beginning of Big Data era. Domo reported, there are 2.5 quintillion bytes of data created each day at the current pace.
According to Visual Capitalist 2019 Research, every minute 188 million emails are sent, 4.5 million videos are watched on YouTube, Facebook holds 1 million logins, total of 41.6 million messages are sent on WhatsApp and Facebook Messenger, 3.8 million searches are processed on Google Search Engine, 347,222 scrolls are done on Instagram, 2.1 million snaps are created, 18.1 million text messages are sent, and almost million dollars are spent online. This is just one minute on the internet, more details are in the research paper.
So, with these huge amount of data flowing on the digital utopia every second, it’s really very risky to turn a blind eye to it. Then, taking an action to look through it would be an appropriate idea, that is why many companies have already adopted the use of Intrusion Detection Systems (IDS) alongside Fraud Detection Systems (FDS) more especially in the banking and finance industries. The machine learning algorithms were also used in building forged credit cards detection system in PayPal company.
Furthermore, data science algorithms will help in forensic investigation both digital and medical forensic analysis. It will help to detect hypocritical communications and actions over a network. While Machine Learning gives a computer an ability to learn and even predict the future, it can still take part in predicting the intention of guilties by analyzing their conversation. The main concern will be on descriptive and predictive analytics.
In 2001 just before the 9/11 attack, a cybersecurity/digital forensic investigator expert notified the FBI and special security forces about the intention of the attack, but the idea was neglected. Hence, last year upon the sudden existence of novel Covid-19, a young medical doctor in the city of Wuhan notified medical specialists about the danger of the disease, but his idea was also neglected. Today, thousands are dead due to this pandemic and still counting.
So, in the case of the American cybersecurity expert, a conclusion has made that he could have known even a little about the occurence of the incident through analysis of the guilties’ communications and so on. That means, technology could predict the occurence of an event even before it take place.
So today, data science can play a significant role in digital forensic in unveiling culprits and their deadliest intentions.