19
Dec, 2018

machine learning

Traditional Antivirus Landscape

Rapid changes in security threats are now a part and parcel of our daily digital lives. Online attackers are constantly looking for new ways to steal information, extort money and execute their malicious motives. Among all the threats lurking out in the cyberspace, the most challenging ones are the zero-day threats – the unknown, the never-seen-before threats. These modern day attacks are sophisticated – they creep in, change your systems and remain undetected.

Failure of Legacy Systems

Ever since, Legacy Antiviruses with signature-based scanning have been the keystone to detect and prevent malicious files. These Traditional Security Software work on signature based approach – They scan and monitor software behaviour and leverage pre-discovered hashes to guess if an incoming file could contain malware. However, in today’s rapidly changing environment, this approach seems like bringing a knife to a gunfight as emerging threats do not fit to the traditional signatures.

Legacy Products do not work in the ever-evolving cyber threat landscape, as they represent some major challenges:

  • They are dependent on external reliance in detecting malware
  • They can only block previously discovered malware
  • They need perfect knowledge of all past and future threats

Advanced Machine Learning

As the technology moves swiftly, hackers ramp up attacks thus raising the stakes. The need is to fight back with an adaptive approach that identifies malware automatically, without having the need of creating signatures manually.

Thanks to REVE Antivirus Machine Learning Technology that works on the Self-Learning model to detect previously unidentified cyber threats along with forecasting new attacks by itself and fights back by deploying appropriate responses in real time.

REVE Antivirus is utilizing advanced Machine Learning Algorithms to address zero-day attacks, differentiate between clean and malicious software applications and stop threats before they have a chance to execute.

Machine Learning Technology – A New Defensive Approach to Cyber Security

We are witnessing a paradigm shift. It’s a wedding knot between machines and people. The growing community of automation has replaced minutes with milliseconds. Automation in Cyber Defense brings about an innovative move which is able to pace up with today’s advanced, subtle and varied cyber attacks.

The security posture of an organization determines the security of its assets. Prevention of attackers bypassing a company’s security functions demands the best possible insight and an over- defense. REVE Antivirus online has gone an extra mile to close the wide security gap left by yesterday’s AV solutions.

  • It gives high detection rate and low false positives
  • It is trained on robust data that represents real world threat scenarios
  • It delivers real time performance without consuming large amount of memory
  • It automatically adapts itself as the training data increases

The value of data is enormous. It can change our view of the world. REVE Antivirus is an advanced malware detection and prevention software which is fueled by Big Data – the kind of data we see in real world that has been collected over the years. This helps REVE Antivirus to make concrete and accurate analysis and anomaly detection.

The Author

Kanika Sharma

Kanika Sharma is a cyber security writer and digital marketer. For the past 5 years, she has been writing for various technology blogs. Being an engineering graduate, her background allows her to connect with cutting edge technologies and relate them to real world scenarios. When she is not writing, she loves wandering around the hills, as exploring nature excites her the most.
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