5 Ways Machine Learning Will Improve Cybersecurity

One of the most important innovations we’ve seen is the rise of machine learning applications in cyber security.

IT managers using machine learning for network security

5 Ways Machine Learning Will Improve Cybersecurity

As we push the boundaries of Industry 4.0, we regularly uncover new advantages of cloud computing security and Artificial Intelligence. And as cyber-attacks on companies become more sophisticated, one of the most important innovations we’ve seen is the rise of machine learning applications in cyber security. 

Here are 5 ways your cloud security network could benefit from security deep learning:

  1. Machine Learning supports human analysis, but does not replace it
    Machine learning is not a silver bullet to solve all cybersecurity problems. But it can help security teams stay up to speed with evolving threats, identify potential weak spots in networks and work more efficiently

    For example, intelligent systems can highlight suspicious activity and create daily, or hourly, reports for security staff to investigate in more detail. Systems can rank individual threats, classify them and even suggest which incidents are likely to be more dangerous and should be investigated first.

  2. Machine Learning for Cyber Security can cope with the exponential growth of network data
    Traditional security systems which simply scan network activity looking for known malware and other threats are becoming less effective. There are two reasons for this:

    Firstly, networks no longer have defined “edges” to defend. Business networks are now connected to several partners and suppliers and to mobile networks. An increasing number of mobile workers access the corporate network from outside the physical perimeter of the building.

    Secondly, the scale of data has completely changed; today there are huge amounts of data flowing in all directions, not just thousands of text-based emails. Spotting suspicious behaviour by individual machines or applications is far more difficult when it is hidden in such a mountain of information. In addition, many organizations are using more and more unstructured data that traditional systems struggle to properly scan.

  3. Machine Learning can be taught to detect existing threats and predict how they might change in the future
    Cybercriminals are changing both their tactics and tools all the time. Ransomware, where data is encrypted until a ransom is paid, is still a problem but no longer the number-one cybersecurity threat. After years of rapid growth, ransomware attacks actually decreased last year. It is now far less of a danger compared to the new biggest threat: coin-mining attacks that take over systems to mine cryptocurrencies like Bitcoin.

    Symantec’s Internet Security Threat Report for 2018 found a staggering 8,500 per cent increase in coin-mining attacks compared to last year. Such attacks are almost undetectable. There is no catastrophic failure as with an old-fashioned ransomware attack, just a slight slowdown in systems performance.

    The speed of change is too much for humans to keep up with on their own. With thousands of new threat variants appearing every day, it is not possible to keep signature scanning systems properly up-to-date.

  4. Systems can also learn to predict where attacks are likely to happen and highlight possible targets and weak spots
    Systems have to be proactive and make predictions instead of preparing to defend against the last attack experienced. That means scanning data outside the organization to determine if criminals are likely to target your type of organization based on its business type or geographical location. It means analyzing hundreds of thousands of documented attacks to spot trends and changes.

    Many new attacks rely on old attack code or established weaknesses in operating systems. Machine learning can help spot these problems and recommend upgrades before they are exploited.

  5. Machine learning can improve response times to a security breach
    Security technology is not perfect, but machine learning can improve the response if a breach does happen.
    Experts talk about the “golden hour” - the first few minutes after the breach that can make all the difference to how well, or badly, the organization suffers.

    An ML system can go into action immediately to discover the scale of the problem and whether other systems have been breached. They can automatically shut down access to key systems, and collect data to measure the effectiveness of the organization’s response so that defences are even stronger in the future.

At Fuji Xerox, we believe that machine learning in iot security is necessary to combating the next generation of security threats. Contact us to learn how machine learning can improve your IT network’s security.

Contact Us

Related Solutions