5 ways machine learning can be used for security today
Credit to Author: Trend Micro| Date: Thu, 16 Mar 2017 17:47:43 +0000
Is your cyber security strategy mature enough to handle a sophisticated threat? Many organizations would likely say no. 2016 had "an all-time record high of 1,093 breaches", according to the Identify Theft Resource Center. Skimming and phishing were among the most prevalent hacking techniques.
Despite the rising numbers of organizations that are impacted by sophisticated threats, many IT leaders aren't planning to change their security strategies this year. Don't let yourself become another statistic waiting for an attack to happen. While machine learning isn't a new technology by any means, it's being applied to five new areas with high hopes that this capability can catch threats before they result in major cyber security incidents.
1. Closing IoT vulnerabilities
The Internet of Things is emerging in full force, and businesses could be in trouble if they aren't prepared to protect those networks. Everything – from cars to coffee makers – is becoming internet-enabled. While these features are beneficial, internet-connected smart machines can create vulnerabilities within your organization. As TechCrunch's Ben Dickson noted, many IoT devices that are currently in use have lax or no security capabilities, making them easy to compromise. Because these devices are often directly connected to your network, hackers can take advantage of inadequate protections and move laterally within to breach your critical information and systems.
With the immense number of IoT devices and the amount of data they produce, you will likely be overwhelmed trying to manually manage and track it all. However, you can leverage machine learning technology to analyze data and network interactions, determining safe device behavior. With this level of insight into general usage patterns, it becomes easier to identify abnormal activity and block harmful actions.
2. Monitoring exchanges
Data sharing is paramount to modern business. Consider the number of files you share with your co-workers, business partners and vendors. These activities would be impossible to monitor without the right tools and personnel. Unfortunately, the lack of available talent has forced technology to pick up the slack, making machine learning an important piece for monitoring exchanges and improving cyber security posture.
Machine learning is by no means perfect. In a separate TechCrunch piece, Dickson remarked that relying on this technology can result in too many notifications and false positives, creating alert fatigue. But once these systems start to learn more and more, and with some help from human experts, those false reports will become a thing of the past. When the machine learning system identifies something as suspicious, you can provide feedback by tagging legitimate threats. The solution then adjusts its monitoring filters, boosting its ability to find and mitigate real breaches before they cause you damage.
"Leverage machine learning to identify and respond to issues quickly."
3. Stopping exploits before they stop you
Zero-day threats put everyone – from organizations to individuals – at risk of losing sensitive data via an unknown exploit. It's virtually impossible to identify these threats before they result in a breach and cause significant damage. Machine learning can help track down these threats and stop them before they severely impact your operations.
Criminal hackers often share ideas and information on forums that exist on TOR networks and websites that aren't indexed by search engines. A team at Arizona State University took advantage of machine learning to monitor traffic within these areas to identify data relating to zero-day exploits, Forbes contributor Kevin Murnane reported. Armed with this type of insight, organizations can close vulnerabilities and stop patch exploits before they result in a data breach.
4. Predicting threats based on historical data
Machine learning systems are adept at combing through large data sets and adjusting themselves based on specific trends. In order to improve your organization's cyber security, you should expose your machine learning solutions to your historical security data to show what attacks look like and their relationships to each other, Datanami contributor Mike Stute stated.
Your cyber security solutions should easily integrate with machine learning to detect network changes over time and revise behavioral profiles. Pairing these systems can help predict threats based on historical data and limit the number of false positives that appear.
5. Providing holistic enterprise protection
A single gap in your cyber security can result in a data breach. And the bigger your organization, the more likely that a gap will appear due to the number of people and devices that must be accounted for. The good news is that machine learning can help identify flaws, especially when paired with other cyber security solutions.
Trend Micro's XGen™ endpoint security combines proven threat detection techniques, advanced protection methods and high-fidelity machine learning. This trifecta ensures that you'll have the right technology at the right time for holistic enterprise defense.
XGen™ is the result of nearly three decades of experience in protecting more than 155 million endpoints. The system leverages techniques to maximize protection against today's and tomorrow's threats while minimizing the potential impact to maintain optimal performance. XGen™ is a great starting point if you're looking to utilize machine learning or want to boost your protection capabilities across your organization. Machine learning can be used to advance business security today in a number of ways.
Contact Trend Micro today to learn more about how XGen™ and machine learning can benefit your cyber security efforts.