India has been placed 10th in the world for cybercrime activities, with advance fee fraud being the most prevalent type, a new study involving cybercrime experts globally has revealed.
The study, leading to the creation of the 'World Cybercrime Index,' ranks about 100 countries based on their involvement in various cybercrime categories, such as ransomware, credit card theft, and scams.
The research, published in the PLoS ONE journal, lists Russia as the leading country in cybercrime, followed by Ukraine, China, the US, Nigeria, and Romania.
North Korea, the UK, and Brazil are ranked seventh, eighth, and ninth, respectively. Experts participating in the survey were asked to identify countries contributing significantly to major cybercrime types.
These types include technical services like malware, attacks and extortion including ransomware, data and identity theft, scams such as advance fee fraud, and cashing out or money laundering through virtual currency.
The survey, conducted from March to October 2021, received 92 complete responses.
Findings show that the top six countries are prevalent across all cybercrime categories, with some countries specialising in specific types.
"Russia and Ukraine are highly technical cybercrime hubs, whereas Nigerian cybercriminals are engaged in less technical forms of cybercrime," the study states.
Countries are found to specialise in crimes ranging from high-tech to low-tech, with India identified as specialising in scams. Romania and the US, like India, were found to specialise in both high-tech and low-tech crimes, positioning India as a "balanced hub" for mid-tech cybercrimes.
"Each country has a distinct profile, indicating a unique local dimension," the authors noted. Miranda Bruce, a co-author from the University of Oxford, UK, highlighted the importance of the findings: "We now have a deeper understanding of the geography of cybercrime, and how different countries specialise in different types of cybercrime."
The surveyed experts were professionals with at least five years of experience in cybercrime intelligence, investigation, and attribution, known for their excellent reputation among peers.
(PTI)
Clifford had previously pleaded guilty to the murders of BBC sports commentator John Hunt’s wife and two daughters at their home in northwest of London, in July 2024. (Photo: Hertfordshire Police /Handout via REUTERS)
Crossbow murderer found guilty of raping ex-girlfriend
A 26-YEAR-OLD man who murdered three women in a crossbow and stabbing attack has been found guilty of raping one of them, his ex-girlfriend, a British court ruled on Thursday.
Kyle Clifford had previously pleaded guilty to the murders of BBC sports commentator John Hunt’s wife and two daughters at their home in Bushey, northwest of London, in July 2024.
The attack led to a manhunt before Clifford was found injured hours later in a north London cemetery.
A jury at Cambridge Crown Court on Thursday convicted Clifford of raping 25-year-old Louise Hunt before killing her.
His sentencing for all the crimes is scheduled for Tuesday.
Clifford had admitted to murdering Carol Hunt, 61, and her daughters Louise and Hannah, 28. He had also pleaded guilty to charges of false imprisonment and possessing offensive weapons but denied raping Louise.
During the trial, the court heard that after killing Carol Hunt, Clifford waited for an hour before attacking Louise, tying her up, raping her, and then killing her with a crossbow. He later killed Hannah when she returned home from work.
The prosecution described Clifford, a former soldier, as committing a "violent, sexual act of spite" and said he was "enraged" after Louise ended their 18-month relationship. They told the court that he had "carefully planned" the attack.
Less than 24 hours before the killings, Clifford had searched for a podcast by social media influencer Andrew Tate, according to the prosecution. They argued that the murders were driven by the "violent misogyny promoted" by Tate.
Justice Joel Bennathan called Clifford’s crimes "dreadful" and "almost unspeakable".
(With inputs from AFP)