Predictive Analytics Solutions Bolster Crypto Trading Security in 2019

The market for cryptocurrencies has opened the door for a number of new opportunities and challenges. One of the biggest difficulties that crypto traders, brokers and entrepreneurs face is a rising number of security risks. In 2019, crypto scams where the most common type of online security breaches. CIO reports that CryptoLocker was one of the worst ransomware attacks of 2019. Other crypto scams will be even more prevalent in the future. New advances in predictive analytics are helping solve many of these threats.

Here are some reasons that predictive analytics technology is going to be the best line of defense against hackers and malware for the foreseeable future.

Identifying variations of crypto malware

According to a report by Pindrop, the prevalence of ransomware attacks directed at crypto users and networks has surged by 500% over the past year. There are a number of reasons that hackers have become more brazen. One of them is that traditional malware protection software is not adequately equipped to handle them. This is not due to the complexity of ransomware pertaining to crypto. Rather, it is due to the fact that the algorithms are simply different. They do not always trigger the same red flags as other forms of malware. Small alterations in the source code can make these malicious applications virtually undetectable.

This is where predictive analytics technology can be invaluable for security purposes. According to an expert that we spoke with from Blockport, predictive analytics technology is able to identify the types of modifications that malicious programmers make to their algorithms. This makes it easier for these tools to notice crypto malware variance that would otherwise be missed by malware protection software.

Identifying sources of attacks

Noticing variations in malware is important for crypto security. However, other safeguards are equally necessary.

One precaution that security analysts need to take is identifying the sources of different crypto attacks. Some regions are responsible for far more security breaches than others.

Predictive analytics technology is essential for identifying the regions where most of these attacks originate. This technology looks at historical records of data breaches and attempts to look for clustering relationships. Previous security methodologies simply created blacklists of IP addresses for networks and geographic regions that are associated with cyber-attacks.

Predictive analytics algorithms recognize that hackers are more sophisticated than previous security models were able to account for. They use the same data to evaluate for other factors, including:

• Relationships between different networks. Predictive analytics algorithms recognize that hackers often take advantage of different types of networks. For example, they may have created multiple botnets to orchestrate their attacks. If a crypto cyber-attack is associated with one network, then the security defenses will try to separate access to associated networks as well.
• Predictive analytics solutions recognize that hackers often operate in sophisticated groups with centers all over the world. Once they are able to trace a crypto cyber-attack to one known hacker, they can try to limit access to very localized regions that their accomplices are likely to be based in.
• Predictive analytics models are able to look for patterns in VPN IP addresses and Tor nodes. This makes it easier for them to bar connections from them.

Predictive analytics technology is making a significant impact on the effectiveness of security solutions. One of the biggest benefits is knowing where these attacks originate, so they can stop them.

Looking for red flags in social engineering approaches

Crypto hackers don’t usually rely entirely on brute force attacks. They often rely heavily on complex social engineering strategies. This enables them to gain the trust of their targets, so they can keep them into downloading malware or dropping their defenses.

Social engineering strategies are complex and difficult for people to notice. However, as security expert Leslie Lambert points out, predictive analytics technology is able to recognize patterns in these types of attacks. They will be able to screen emails and one recipients of potential phishing attacks. They can also look for warning signs that webpage is a recipient might have been directed to is consistent with those often used in these types of attacks.