Cyber warfare is rapidly increasing not just on companies, but nowadays there is no personal privacy or security either which is soon to turn worse. There is a gaping hole of trained workers in the digital defences that companies use to keep out cyber thieves which are why now companies are relying on smart machines which have a security hardware “system” and is run by skilled staff that analyses threats and blocks the intruders’ way to the system.
Currently, the global security industry is lacking about one million trained workers, suggests research by ISC2 - the industry body for security professionals. The deficit looks set to grow to 1.8 million within five years, it believes. The researchers and companies are therefore looking for machines to help them out.
While many nations have taken steps to attract people into the security industry, those efforts will not be enough to close the gap, says Ian Glover, head of Crest - the UK body that certifies the skills of ethical hackers.
Smart machines are helping businesses defend themselves against hackers. The analytical power of machine learning derives from the development of algorithms that can take in huge amounts of data and pick out anomalies or significant trends. Increased computing power has also made this possible. Machine learning tool, Turing, digs out evidence of web attacks from the massive amounts of queries the company handles every day - queries seeking information about the location of UK websites. The winner of a contest at DARPA's Def Con last year, Mayhem, is now being adapted so that it can spot and fix flaws in code that could be exploited by malicious hackers.
There are few factors that cause the hacking, such as lack of trained workers or the companies are not getting right people and most important part is the change in the machines. Since there had been an increase in automation attack tools, we need to have an updated automation in the tools that defend ourselves against cyber thieves, which is lacking now.
According to Peter Woollacott, founder and chief executive of Sydney-base Huntsman Security “that move towards more automation is already under way” and that change was long overdue. The security had been a “hand-rolled” exercise for too long.
So now cybersecurity analysts can sit back and let the machine-learning systems crunch all the data and pick out evidence of serious attacks that really deserve human attention.
Currently, the global security industry is lacking about one million trained workers, suggests research by ISC2 - the industry body for security professionals. The deficit looks set to grow to 1.8 million within five years, it believes. The researchers and companies are therefore looking for machines to help them out.
While many nations have taken steps to attract people into the security industry, those efforts will not be enough to close the gap, says Ian Glover, head of Crest - the UK body that certifies the skills of ethical hackers.
Smart machines are helping businesses defend themselves against hackers. The analytical power of machine learning derives from the development of algorithms that can take in huge amounts of data and pick out anomalies or significant trends. Increased computing power has also made this possible. Machine learning tool, Turing, digs out evidence of web attacks from the massive amounts of queries the company handles every day - queries seeking information about the location of UK websites. The winner of a contest at DARPA's Def Con last year, Mayhem, is now being adapted so that it can spot and fix flaws in code that could be exploited by malicious hackers.
There are few factors that cause the hacking, such as lack of trained workers or the companies are not getting right people and most important part is the change in the machines. Since there had been an increase in automation attack tools, we need to have an updated automation in the tools that defend ourselves against cyber thieves, which is lacking now.
According to Peter Woollacott, founder and chief executive of Sydney-base Huntsman Security “that move towards more automation is already under way” and that change was long overdue. The security had been a “hand-rolled” exercise for too long.
So now cybersecurity analysts can sit back and let the machine-learning systems crunch all the data and pick out evidence of serious attacks that really deserve human attention.