When dealing with the security of your cloud-based software, what you don’t know can hurt you. Vulnerabilities within your security system that you fail to identify and patch could spawn an opportunity for cybercriminals to hack into your systems and cause major problems. Known as zero-day vulnerabilities, hackers can harness such flaws in your system for years without you even noticing it.
Nearly 30% of malware attacks spawn from zero-day exploits. Since these attacks are somehow new to the security world, legacy antivirus software might find it tough to identify them. What worsens this situation is that as vendors work on improving their security solutions, cybercriminals are working overtime to create even more complex attack strategies to make zero-day attacks work in their favor. The trick is finding a way to counter such bold moves.
Here are four proven techniques for detecting zero-day vulnerabilities:
This technique leverages machine learning to aggregate data about past zero-day attacks to identify the normal behavior expected from your application. It can work with solutions such as AWS cloudtrail log ingestion tools to do so, according to Loggly. The longer the tool stays in your system, the more data it can collect for building a comprehensive threat profile.
Depending on the baseline of normal behavior that you choose for your application, a statistics-based detection solution might produce a number of false positives and negatives. While false negatives should be avoided to beef up your security, false positives should be eliminated to avoid impacting your company’s normal operation. The only other limitation for this solution is that it might struggle to identify highly encrypted malware and zero-day exploits.
This technique leverages antivirus software to detect malware using the specific malware signatures as a reference for scanning the system, as noted on Life Wire. Although the detection software will need to be updated quite quickly and perpetually to work effectively, it is tough to identify the zero-day threats as their signature isn’t well known. To circumvent this barrier, such solutions need to use AI and machine learning to generate signatures for unknown threats in real time to make it easily recognizable.
The solutions can come up with the signatures using three techniques which include content-based, vulnerability-based and semantic-based detection. The speed at which the solution can generate signatures for real-time malware will determine how effective it is in the fight against zero-day attacks.
Unlike signature-based detection solutions, the behavior-based solutions do not look at malware code. Instead, they focus on the normal behavior of the system when interacting with other software and identify any anomaly as a red flag. As a result, the solution will work best as long as a lot of data about the behavior of your cloud-based software is available.
To achieve this, such solutions often leverage machine learning. When used in a single system for a long time, it can turn out to be a potent solution in the fight against zero-day attacks as it will understand the behavior of your software even better.
Hybrid Detection Techniques
A solution that uses hybrid detection techniques is simply a combination of any two or three of the solutions mentioned above while avoiding the flaws that the solutions have. This allows the solutions to produce highly accurate results. For instance, a tool can combine both statistics-based techniques and behavior-based ones to speed up the learning process. On the other hand, signature-based techniques can be combined with statistics-based algorithms to steer away from false positives and negatives.
Zero-day threats are quite difficult to manage, let alone detect. The fact still remains that you need to eliminate them or watch your business crumble. Consider using solutions that leverage the above techniques to keep such threats at bay.