How Three Layers of Endpoint Security Work Together
Every year, hackers work harder to bypass the latest security technologies. In 2019, companies like Capital One, Voova and the American Medical Collection Agency felt the impact of advanced cyber criminals and learned about the need for complete endpoint security and ransomware detection technologies. When it comes to enterprise endpoints, a multi-layered approach to endpoint security is the only truly effective method of protection. RevBits Endpoint Security’s three phases of analysis make up the most thorough and effective endpoint security on the market today. This process begins by isolating new executables in a safe environment.
Endpoint Security Phase One: Signature Comparisons
With help from easily available tools that allow hackers to defeat certain defense strategies, malware is evolving and changing in real-time. In the malware prevention world, each virus has a signature - a group of unique data or bits of code that allow it to be identified. A virus’ signature can be thought of as its fingerprint. Signature comparison, a common process in ransomware prevention, scans code to test for a set of known viruses. Although a very effective form of ransomware detection, signature comparison should not work alone. RevBits Endpoint Security includes signature scanning only as a first step. This technology conducts a unique three-phase analysis that also includes machine learning verification and behavioral analysis.
Endpoint Security Phase Two: Machine Learning Verification
Formerly, machine learning was geared towards testing and not verification. Testing in machine learning is the evaluation of a system in multiple conditions to observe its behavior for defects. Verification, on the other hand, is the process of producing a compelling argument that the system in question will not misbehave under a vast range of conditions and circumstances. As threats have become more advanced and attackers have become savvier, the need for machine learning verification in Endpoint Security tools has become imperative. With machine learning verification, RevBits Endpoint Security constantly analyzes the characteristics of both verified and corrupted files to learn to adapt to new and previously unknown threats.
Endpoint Security Phase Three: Behavioral Analysis
Companies can contain previously unkown zero-day threats by implementing an endpoint security solution that proactively identifies and blocks threats based on factors such as behavior. As the third phase of RevBits malware prevention technology, behavioral analysis successfully blocks malicious actors by unpacking suspicious item behavior. More specifically, RevBits Endpoint Security observes new executables processes, and, in combination with the signature and machine learning scans, determines which are malicious. Upon exposing the malicious executables, RevBits Endpoint Security blocks any harmful code from promulgating.
Because the threat landscape is constantly changing, cybersecurity technologies must work together and not independently of one another. RevBits Endpoint Security is the only solution available that conducts a complete three-phase analysis consisting of signature comparisons, machine learning verification and behavioral analysis. With this multi-layered approach, RevBits Endpoint Security automatically detects, classifies, blocks and reports exploit attempts of all vulnerabilities and is perfect for every enterprise.
The healthcare environment has become a prime target for cybercrime over the past number of years. Attacks on healthcare grew with the Covid-19 pandemic as cybercriminals targeted hospitals, vaccine research companies, and other frontline healthcare provider organizations. With the treasure trove of highly valuable Personally Identifiable Information (PII) and more specific Protected Health Information (PHI) held by healthcare providers and facilities, these environments' targeting will likely continue to grow and become more sophisticated.
XDR is an alternative to the reactive endpoint solutions that provide only single-layered visibility over specific points. Though these layer-specific solutions are quite effective, they deliver a large volume of alerts that require a considerable amount of time in their investigation, response, and management.
After struggling for almost a year, things are finally getting back to normal. Not in the way they were before, but after being under lockdown for so long, people are adapting to this new normal.