Home > Machine Learning Trends and Its Use in Automated Security Networks
Machine Learning Trends and Its Use in Automated Security Networks
There’s good news, and there’s bad news. The good news is that the volume of big data is steadily on the rise. The bad news is that attack tools/malware that break into systems hoarding this valuable big data have become increasingly sophisticated – so sophisticated that in most instances, it’s humanly impossible to outpace their capabilities. How then, is it possible to secure our data center networks that are not only getting bigger and faster but also increasingly difficult to protect?
Manual monitoring of security data is vulnerable to human error, and with the attackers’ arsenal getting more powerful by the day, it’s sheer stupidity to believe that we humans have the capability to recognize every new malware and prevent every impending attack threatening to breach our manual security systems. The only solution is network security automation with integrated backup and data recovery solutions. This again is not infallible, but with machine learning (ML), we can enhance the protection of our automated security network management.
What is machine learning and how has it evolved?
Machine learning is defined as “a machine’s ability to learn something new, even when it is not specifically programmed to do so”. It does this by using algorithms and statistical models and relying on patterns and inferences.
A few years ago, machine learning was limited to ‘supervised learning,’ where a machine ‘learned’ what it was ‘taught.’ To put this in a more meaningful way, collected data (input) was mapped to observed behavior (output). Any anomaly in a regular pattern was flagged by the machine as a threat. This was and still is used extensively in intrusion detection systems (IDSs). It’s also used in spam email filtering, where the machine recognizes from inputs and patterns, what data is spam and what is not.
Today, machine learning has matured and become more sophisticated. Machines can learn without being taught, and without relying on patterns that are obvious to humans. The system can now train itself to make an inference from new knowledge (data), as and when it is collected. It sees a pattern where humans can’t. Amazingly, the system automatically improves with experience. This ‘deep learning’ or ‘unsupervised learning’ is used extensively in voice recognition and voice-based experiences like Apple’s Siri and Amazon’s Alexa, which over time get better at recognizing a particular voice. The brain behind self-driven cars is again unsupervised machine learning, without which we couldn’t have these cars.
Some machine learning trends that are making it big in 2019
- The growth of big data and the simultaneous low cost of data storage, as well as the maturity of machine learning, will lead to more companies outsourcing their data-centered activities to cloud service providers for network management solutions and backup and data recovery solutions. While cloud lends agility to a business, machine learning will have a major impact on business outcomes.
- The easy availability of data, both old and new, will make machine learning better by helping create more agile statistical models and better algorithms.
- The rapid adoption of AI across global markets will see the AI market reach $13 trillion by 2030.
How do these trends impact a business’ automated security network?
Detecting malicious activity and preventing attacks
Machine learning will be used to detect malware faster and prevent an attack. From basic cybersecurity systems used to just detect malware, we will see a surge in providers offering sophisticated machine learning systems that will interpret tons of events and actions as safe and/or unsafe, and based on this information will make predictions on what will be safe and/or unsafe in the future.
Analyzing mobile endpoints
Machine learning’s capabilities don’t end with Apple’s Siri and Amazon’s Alexa. Today, machine learning is also present in automated security systems to detect threats against mobile endpoints. Google uses it extensively; and so will a steadily-increasing volume of organizations, as they move from a policy that discourages the use of personal devices for business purposes to a more liberal BYOD (bring your own device) policy that leaves plenty of room for cyber threats.
While not a 100% magical solution for security as yet, machine learning has the potential to analyze the structure of data and find patterns that are otherwise humanly impossible to recognize. Machine learning will not only be used to anticipate threats but also counter data security attacks in automated network management – including network security automation – and backup and data recovery solutions.
Enhance human analysis and decrease their need for interference
Early experiments have already proven that machine learning can find the “needle in the haystack” and pinpoint malware to human analysts. Once a threat is recognized, quick remedial measures can be taken to keep casualties to a bare minimum.
Automating quick-time responses is a solution; however, creating automatic scripts is not only time-consuming but also impossible in the case of unforeseeable threats. With unsupervised machine learning, systems could learn from past incident responses and make new recommendations for security threats. These responses can then be shared with human analysts for approval and further action. There are obstacles here though if multiple unsynchronized security solutions are used. For true automation of a company’s security network, the company must integrate its security systems into a single security fabric; unfortunately, this is not always the case. However, with machine learning having come such a long way, teaching itself to analyze data, it won’t be long before it’s able to overcome problems such as this.
In the recent past, and to a certain extent even today, machine learning is perceived as a threat – an evil entity that will one day take over the world, and rule over mankind, if not kill it. Will that day ever come? We don’t know. But as of now, we are certain of this: machine learning is a boon and not a bane; and while it’s still within our power, we must accept it for what it is and use it to our benefit. It has proved and continues to prove that it can, and must be used in conjunction with network security automation across organizations.