Big data is one of the most precious business assets in this digital age. Leveraged well, it can expand the customer base and ensure big profit margins. But data alone cannot convert numbers into results; organisations must analyse the figures accurately and use them optimally to gain a competitive edge over rivals.
Every valuable possession demands careful protection, lest it leads to great loss, and the same applies to big data. With many now terming IoT as ‘Internet of Threats’ in lieu of the Internet of Things, working within a highly complex network of digital devices is always a challenge for big and diversified organisations. This is because leaks of confidential information may lead to financial problems, loss of reputation and legal turmoil.
Data security, a grave concern
According to CERT (Computer Emergency Response Team), about 32,323 websites were hacked in 2014 alone and, as per the reports of the National Crime Records Bureau, the number of reported cases of cyber crime increased by 350 per cent from 2010 to 2016. Reported cyber crimes went from 966 to a whopping 4,356 between these years. India also remained the second most targeted country in the world through social media in 2014. From financial institutions to defence research agencies, all are on the radar of malicious hackers and spies.
A few months back, one of the spokespeople of NATO said: “In recent events, cyber attacks have been part of hybrid warfare”. This means the tentacles of modern warfare have spread into the cyber world too. Even the supremo of the world’s most powerful country seemed afraid when he said: “The increasing prevalence and severity of malicious cyber-enabled activities originating from, or directed by, persons located, in whole or in substantial part, outside the United States constitute an unusual and extraordinary threat to national security, foreign policy, and the economy of the US. I hereby declare a national emergency to deal with this threat.”
Machine-learning algos
Securing data is a difficult task as cyber criminals are well equipped with advanced technologies and strategies and they can be defeated only by using customised tools and strategies in the counter attack. Yet, as diamond cuts diamond, big data technology can be effective enough to thwart online data thefts. Now, the foremost step is scanning the hunters in a particular network to protect transaction gateways such as ERP and SAP that work on Hadoop.
Businesses as well as individuals frequently share information in the form of text, images, graphics, audios and videos through emails, social media sites, mobile apps and even other IoT-based devices. But, using various deceptive techniques like process hollowing and mal-coding, cyber criminals either change the route of data or convert it into a corrupt file.
Security analysts notice all such anomalies and, using big data tools, they detect threats and block the route of hackers. That’s why many reputed companies, especially tech firms, are using big data technology to monitor behavioural changes among the people who are directly or indirectly linked to the organisation’s networks. This kind of practice is commonly known as machine learning algorithm. Big data analysts search, evaluate and deactivate suspicious codes and routes to ensure the safety of the network.
Boosting business gains
According to a survey conducted by Business Application Research Centre (BARC) in the US, 53 per cent of organisations that are taking the help of big data analytics in their security systems are enjoying high business benefits, 41 per cent are witnessing moderate gains and only 6 per cent have reported marginal benefits in the business.
Such studies indicate that big data analytics is an effective remedy against cyber attacks, hacking and data theft. Hence, to protect businesses and to safeguard their valuable information, big data can be used very effectively along with other bespoke security solutions such as multi-factor authentication, data loss prevention, advanced persistent threat protection (APTP), and IPS/ firewalls.