DATA POISONING OF DEEP-WEB GANGSTERS HITTING THE RELIABILITY OF ARTIFICIAL INTELLIGENCE
Human Brain’s Mirror VS Deep-Web Humans
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Indeed there is no single argument against the contribution of Artificial Intelligence as MIRRORING of the human brain. It is bringing automation with the power and ability of decision-making in the fields of IT, businesses, financial matters, marketing, industrial and healthcare sectors.
But when it comes to comparative analysis with the limitless thinking power of the father figure “THE HUMAN”, it’s getting dodged to some extent by being trapped with DATA POISONING of “deep WEB gangsters, THE HACKERS”.
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In the most recent research-based work out of Marta Janus, Tom Bonner and Eoin Wickens of Hidden Layer (the Algorithm based security solutions providing company), the attackers are manipulating the codes of machine learning projects by two major ways;
They gather relevant and nearest matching information which brings ways for them to exploit.
Tracing the best opportunities and moments to inject their influential lines of data poisoning.
Data Poisoning is simply deteriorating trust in the reliability of machine learning!
What the Current Survey and Study Say
As per the statistics described by McKinsey & Company, they had been in discussion with more than a thousand executives related to AI regarding the scalability and enhancement of this sector. 70% out of the total were considering their initiatives as STRUGGLING while scaling.
AIET released its study on this, highlighting 5 categorized threats for AI and ML development.
– Embedding false data into the existing information.
– Corrupting or poisoning data.
– Attacks on the AI training models.
– False input in online projects.
– Working files in progress are more vulnerable.
Ways the Attackers Are Following
The attitude of attackers towards maligning the code of AI is not that much strange. They use to modify the lines of already existing code with result-oriented data injections.
Cyber gangsters hit the authenticity and reliability of machine learning models. They try to prevent the projects at maximum so that the ideal results could not be achieved.
The hackers also use AI by themselves too. Their auto-enabled bots search glitches in ML projects. After getting access into your environment, these bots look for less-protected files, try to inject pieces of code, any analytical formulas or data sets. Actual users still consider these lines as legitimate.
How to Protect AI and ML Projects
It has become a very big challenge for the ML specialists now to secure their online training or continuous learning models of AI. Hackers are particularly targeting the IDS, Biotech and chatbots devices, financial thieves preventing models, healthcare diagnostic devices and auto-completion intelligence systems.
One can give a tough time to hackers and protect his/her data by executing an ultra-care line called Share-Vault. It is the mechanism by which the documents are secured after putting into an extra protection layer.
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