Ransomware is a rising threat to businesses all over the world. Between 2016 and 2017, the number of ransomware incidents increased by 350%. The FBI now estimates that there are 4,000 ransomware attacks every day.
Digital security experts are desperately looking for new ways to thwart these attacks. Predictive analytics is one of the options at their disposal. New predictive analytics algorithms are able to help identify red flags of a ransomware attack and assess the probability that a business will be targeted in the near future.
A growing number of experts are praising the advances in predictive analytics, as they strive to find more reliable ways to fight ransomware. However, they may be putting too much faith in these algorithms.
Is predictive analytics the best line of defense against these attacks? Or is it lulling companies into a false sense of security and tempting them to drop their guard? There is a case to be made that both of these scenarios are playing out at the same time. Let’s take a look at the benefits and limitations of predictive analytics in the war against ransomware.
Some Variables Are Universally Effective For Identifying Ransomware
There are a number of variables that predictive analytics algorithms can use to screen for ransomware attacks. Some of them are highly reliable indicators, while others are more hit or miss.
Data usage is one of the most reliable variables for identifying signs of ransomware. Ransomware attacks can use a number of different angles and psychological pain points to trick their victims. They may use different programming languages and syntax, as well. However, there is one thing that they all have in common: they need to use a massive amount of data to access their target and implement the attack.
The most effective predictive analytics best responses to ransomware look carefully at the amount of data that is being used on a system. High spikes indicate that a hacker might be installing it on the network or hard drive.
How helpful are these algorithms, though? Doesn’t this mean that the warning would be sent out too late? Not if the predictive analytics algorithms were incorporated into a highly versatile malware protection tool.
This could help identify the malignant application as it was being installed and respond in real time, without the need for human input. Access to the rootkit could be immediately severed and a warning to remove it could be sent out. The cybersecurity team could review the application to make sure that it wasn’t a false positive and have it promptly removed if they identified as ransomware.
Some Indicators Are More Subjective And Prone To Error
Data usage is a reliable indicator of the presence of ransomware. However, there are other explanations for high levels of data usage, so it is not perfect on its own. Other variables are needed to identify ransomware with a high level of certainty.
The problem is that these variables can’t pinpoint the presence of this vile type of malware nearly as conclusively. Some of the other factors include:
- The source that the application originated from. Developers that create ransomware solutions use machine learning to identify the types of places that ransomware is usually uploaded from.
- Pieces of code that overlap with those of known ransomware variants.
- Any content that has not been cloaked from malware detection tools.
These indicators are usually very accurate, especially when used in conjunction with other variables that are almost always present in a ransomware attack. However, they might leave networks vulnerable if completely original and highly sophisticated types of ransomware make their way into the digital ecosystem.
Predictive Analytics is Useful, but Other Precautions Are Also Needed
Ransomware is a growing threat that businesses in every sector must deal with. New predictive analytics solutions are reliable lines of defense against it. However, there are some limitations that must be considered as well. You need to use a number of different tools and strategies to stop ransomware. Predictive analytics is just one tool that should be in your arsenal.
