Regardless of the elapsed time of a security breach, the fact is that one day is too long, and by the time a breach is detected, it’s always too late. Shrinking that number to zero is the ultimate goal. To do that, however, organizations need a more intelligent approach to detect threats earlier and turn the tide against sophisticated cyberattacks. Machine learning techniques leveraging cutting-edge data science and human experts combined with advanced computing power can help dramatically reduce the time it takes for organizations to detect an attack.
Machine Learning Overview
In a broad sense, machine learning refers to processes for “training” machines how to solve specific problems through utilization of large datasets. For example, let’s take the case of on-line shopping. Almost every large online storefront will recommend items a user may want to purchase. These recommendations are based on a few data points; for example, previous shopping history, one’s recent searches, or even based on who one’s friends are. Machine learning utilizes this massive amount of data in order to come up with a simple answer: what item might a user like to purchase?
The past decade has witnessed “big data” growing at an incredible rate, which has, in turn, been one of the major factors driving the growth of machine learning techniques analyzing large data sets. Another major force behind the machine learning direction has been the availability of cheap and plentiful computation.
Machine Learning for Cyber Security: Benefits and Challenges
Over the past few years, a new class of solutions has emerged that employ machine learning for enterprise security. These products provide the capability to analyze networks, learn about them, detect anomalies and protect enterprises from threats. Machine learning tools observe behavior in defining a statistical profile of normal activity for a user, device or Web site which provides the foundation for threat analytics enabling prevention of major impact from attacks that may slip by static anti-threat defenses
The need for machine learning for cyber security is driven by two realities: first, it typically takes months to detect a compromise, and, second, in most cases, companies are informed by a third party that it has been breached. As a result, organizations need capabilities that allow them to get in front of security threats, and have been looking for a way to automate the analysis of their security-related log data in such a way that security attacks are detected on a continuous basis
The key benefits of machine learning applied to network security is its ability to detect trends, patterns and anomalies in large and diverse data sets and the speed at which it can do this much faster by far than most big data analysis tools, as it can typically work in seconds to minutes.
As with any new technology, machine learning presents potential difficulties. It may be quite challenging to distinguish the quality of different machine learning tools. This is why experts recommend proof of concept projects focusing on a few discrete use cases for users, devices and Web sites to start out with. In addition, at its root, machine learning detects anomalies in the data logs used. Therefore, its effectiveness is directly tied to the quality of the data exploited.
Machine Leaning Security Scenarios
Machine learning for cyber security is a rapidly growing market which ABI Research believes will boost spending on big data, intelligence, and analytics to $96 billion by 2021. At a top-level, machine learning can help enterprises rapidly detect and respond to attacks and security incidents. It could also help to automate more tedious tasks formerly carried out by sometimes overextended security teams.
Following is a list of potential machine learning use cases for cyber security:
- As a high priority, machine learning can help security staff with all aspects of their job, including detecting malicious attacks, analyzing the network, endpoint protection and vulnerability assessment.
- Machine learning algorithms are helping businesses to detect malicious activity such as last year’s Wannacry ransomware faster and stop such attacks before they even get started.
- Machine learning can also help enterprises block vulnerabilities such zero-day threats targeting mostly unsecured IoT devices.
- Enterprise view machine learning as a key tool in the arsenal against the mounting threat from growing number of BYOD mobile devices.
Interface Masters Technologies has for over 20 years been providing off-the-shelf innovative network security solutions with customization services to OEMs, Fortune 100 and startup companies. Our headquarters are located in San Jose, California in the heart of Silicon Valley where we are proud to design and manufacture all of our products. Based on MIPS, ARM, PowerPC and x86 processors, Interface Masters appliance models enable OEMs to significantly reduce time-to-market with reliable, pre-tested and pre-integrated networking solutions that can meet the most challenging security requirements.