NOVAM – Distributed Cybersecurity for IoT

Novam Greetings, Friends! I am always in search of interesting projects for you, the most potential and deeply thought-out.
Wandering through the expanses of the World Wide Web, I found for you a very interesting and already well-proven project. Take a few minutes to read this review, I promise you will love this project and its awesome idea. In addition, this review will be different from those that are made under the copier, I’ll try to invest the maximum amount of information and details so that you can even better understand all the subtleties of the project, the past experience of the team members, its counterparts and Competitors.
Today, our test subjects will be the NOVAM project!
It presents almost a unique idea, with a minimum number of similar projects!
So there is a great chance to get the coveted caviar.
The truth should not hurt. As technology continues to be all-encompassing, threats and anomalies are more prevalent than ever. Malicious attacks can originate from any point at any time from people and/or machines. The chances of someone or something discovering vulnerabilities, stealing personal data and causing damage to organizations and individuals are higher than everDamage to reputation, finances and operations has become its own industry, replete with
Methodical experts.
The International Data Corporation (IDC) predicts that our global data will include 163 Zettabita by 2025. And that’s just the beginning as projections for scale, Novam profit and innovation in most industries are based on an increasingly interconnected and data-driven world.
The best thing most authorities can do is watch news summaries with the rest of us as millions of confidential documents containing personally identifiable information continue to steal.
Damage a vulnerable entry point to a company or third-party provider, malicious attacks and did – often before detection. Internal or global, security trends indicate that detection of threats and attacks is becoming increasingly difficult, especially at the enterprise level scaling networks and organizations.
Increased complexity of connection and communication of global offices to bring, and clouds of virtual networks inherit, and policies-physical data centers and own devices (BYOD) baked-in all increased susceptibility to threats to the organization Face twenty-four-seven.
An outdated approach uses security levels for cyber products, which prevents communication, smooth updating, and limiting the views required to assess the threat.
Internal and external threats are able to adapt and grow along with the digital landscape as new technologies evolve. As companies and networks evolve, so should the tasks and responsibilities of IT teams. Many IT professionals are accused of finding and mitigating attacks and anomalies manually. Unfortunately, with more human interaction comes a high probability of human error.
In addition, as attacks become more intelligent, our network security must match the wit of malicious software. Without automation, it can take thousands of man-hours, professionals, and millions of dollars and is still unable to provide adequate protection.
Novam Controlled machine learning controlled machine learning is an approach that maps input and output data based on an example of an input-output pair. Traditionally, it allows the processed training data to be filed in a system for the determination of the result. In the context of information security, anti-virus can recognize  “known ” malicious threats that have been seen previously, correctly labeled and
Loaded into the database for subsequent cross-referencing.
Disadvantages of controlled machine learning in information security:
 Malicious behavior that changes the form may become unrecognizable by signature and may
Compromise system
 Incorrectly labeled data does not allow the system to accurately classify malicious behavior
Serious compromise of the system and increasing exposure to undetected threats
 Marking training data manually increases time and cost

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